Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review
Abstract
:1. Introduction
2. Basic Concept of Multi-Task Optimization and Multi-Task Evolutionary Computation
2.1. Definition of Multi-Task Optimization
2.2. Confusing Concepts of MTO
2.2.1. Multi-Objective Optimization (MOO)
2.2.2. Sequential Transfer Optimization
2.2.3. Multi-Form Optimization
2.3. Multifactorial Evolutionary Algorithm
Algorithm 1 Basic Structure of the Canonical MFEA | |
1 | Randomly sample N∙K individuals to form initial population P(0); |
2 | for each task Tk do |
3 | for every individual pi in P(0) do |
4 | Evaluate pi for task Tk; |
5 | end for |
6 | end for |
7 | Calculate skill factor r over population P(0); |
8 | Calculate scalar fitness according to skill factor r; |
9 | t = 1; |
10 | while stopping conditions are not satisfied do |
11 | while offspring generated for each task < N do |
12 | Sample two individuals (xi and xj) randomly from P(t); |
13 | if then |
14 | [xa, xb] ← intra-task crossover between xi and xj; |
15 | Assign offspring xa and xb with skill factor (); |
16 | else if rand < rmp then |
17 | [xa, xb] ← inter-task crossover between xi and xj; |
18 | Assign each offspring with skill factor or randomly; |
19 | end if |
20 | [xa] ← mutation of xi; |
21 | Assign offspring xa with skill factor ; |
22 | [xb] ← mutation of xj; |
23 | Assign offspring xb with skill factor ; |
24 | Evaluate [xa, xb] for their assigned task only; |
25 | end while |
26 | Calculate skill factor r over population P(t); |
27 | Calculate scalar fitness according to skill factor r; |
28 | Select survivors to next generation; |
29 | t = t+1; |
30 | end while |
2.4. Literature Review and Analysis
3. Theoretical Analyses of Multi-Task Evolutionary Computation
4. Basic Implementation Approaches of Multi-Task Evolutionary Computation
4.1. Chromosome Encoding and Decoding Scheme
4.2. Intro-Population Reproduction
4.3. Inter-Population Reproduction
4.3.1. When to Transfer
4.3.2. What to Transfer
4.3.3. How to Knowledge Transfer Implicitly
4.3.4. How to Knowledge Transfer Explicitly
4.4. Balance between Intra-Population Reproduction and Inter-Population Reproduction
4.4.1. Fixed Parameter Strategy
4.4.2. Parameter Adaptation Strategy
4.4.3. Resource Reallocating Strategy
4.5. Evaluation and Selection Strategy
5. Related Extension Issues of Multi-Task Evolutionary Computation
5.1. Algorithm Framework
5.2. Similarity Measure between Tasks
5.3. Many-Task Optimization Problem
5.4. Decision Variable Translation Strategy
5.5. Decision Variable Shuffling Strategy
5.6. Adaptive Operator Selection Strategy
5.7. Multi-Task Optimization under Uncertainties
5.8. Hyper-Heuristic Multi-Task Evolutionary Computation
5.9. Auxiliary Task Construction
6. Applications of Multi-Task Evolutionary Computation
6.1. Benchmark Problems
6.1.1. Continuous Optimization Problem
6.1.2. Discrete Optimization Problem
6.2. Real-World Problems
6.2.1. Machine Learning
6.2.2. Manufacturing Industry
6.2.3. Industrial Engineering
6.2.4. Others
7. Future Works
7.1. Explore Mechanism of Knowledge Transfer
7.2. Balance Theoretical Analysis and Practical Application
7.3. Enhance Effectiveness and Efficiency of MTEC Algorithms
7.4. Extend MTEC Algorithmic Advancements
7.5. Develop New Science and Engineering Applications
7.6. Compare Disparate Algorithms under Different Scenarios
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Molina, D.; LaTorre, A.; Herrera, F. An insight into bio-inspired and evolutionary algorithms for global optimization: Review, analysis, and lessons learnt over a decade of competitions. Cogn. Comput. 2018, 10, 517–544. [Google Scholar] [CrossRef]
- Lin, M.-H.; Tsai, J.-F.; Yu, C.-S. A review of deterministic optimization methods in engineering and management. Math. Probl. Eng. 2012, 2012, 756023. [Google Scholar] [CrossRef] [Green Version]
- Kizielewicz, B.; Sałabun, W. A new approach to identifying a multi-criteria decision model based on stochastic optimization techniques. Symmetry 2020, 12, 1551. [Google Scholar] [CrossRef]
- Back, T.; Hammel, U.; Schwefel, H.-P. Evolutionary computation: Comments on the history and current state. IEEE Trans. Evol. Comput. 1997, 1, 3–17. [Google Scholar] [CrossRef] [Green Version]
- Jin, Y.C.; Branke, J. Evolutionary optimization in uncertain environments—A survey. IEEE Trans. Evol. Comput. 2005, 9, 303–317. [Google Scholar] [CrossRef] [Green Version]
- Nguyen, T.T.; Yang, S.X.; Branke, J. Evolutionary dynamic optimization: A survey of the state of the art. Swarm Evol. Comput. 2012, 6, 1–24. [Google Scholar] [CrossRef]
- Tanabe, R.; Ishibuchi, H. A review of evolutionary multimodal multiobjective optimization. IEEE Trans. Evol. Comput. 2020, 24, 193–200. [Google Scholar] [CrossRef]
- Li, J.; Lei, H.; Alavi, A.H.; Wang, G.-G. Elephant herding optimization: Variants, hybrids, and applications. Mathematics 2020, 8, 1415. [Google Scholar] [CrossRef]
- Bennis, F.; Bhattacharjya, R.K. Nature-Inspired Methods for Metaheuristics Optimization: Algorithms and Applications in Science and Engineering; Springer Nature: Basingstoke, UK, 2020. [Google Scholar]
- Mirjalili, S.; Dong, J.S.; Lewis, A. Nature-Inspired Optimizers: Theories, Literature Reviews and Applications; Springer Nature: Basingstoke, UK, 2020. [Google Scholar]
- Ong, Y.-S.; Gupta, A. Evolutionary multitasking: A computer science view of cognitive multitasking. Cogn. Comput. 2016, 8, 125–142. [Google Scholar] [CrossRef]
- Gupta, A.; Ong, Y.-S.; Feng, L. Insights on transfer optimization: Because experience is the best teacher. IEEE Trans. Emerg. Top. Comput. Intell. 2018, 2, 51–64. [Google Scholar] [CrossRef]
- Pan, S.J.; Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345–1359. [Google Scholar] [CrossRef]
- NIPS*95 Post-Conference Workshop. Available online: http://socrates.acadiau.ca/courses/comp/dsilver/NIPS95_LTL/transfer.workshop.1995.html (accessed on 31 March 2021).
- Caruana, R. Multitask learning. In Learning to Learn; Thrun, S., Pratt, L., Eds.; Springer: New York, NY, USA, 1998; pp. 95–133. [Google Scholar]
- Weiss, K.; Khoshgoftaar, T.M.; Wang, D.D. A survey of transfer learning. J. Big Data 2016, 3, 9. [Google Scholar] [CrossRef] [Green Version]
- Thrun, S. Is learning the n-th thing any easier than learning the first. In Advances in Neural Information Processing Systems; Mozer, M.C., Jordan, M.I., Petsche, T., Eds.; The MIT Press: Cambridge, MA, USA, 1996; pp. 640–646. [Google Scholar]
- Gupta, A.; Ong, Y.-S.; Feng, L. Multifactorial evolution: Toward evolutionary multitasking. IEEE Trans. Evol. Comput. 2016, 20, 343–357. [Google Scholar] [CrossRef]
- Lin, J.B.; Liu, H.L.; Tan, K.C.; Gu, F.Q. An effective knowledge transfer approach for multiobjective multitasking optimization. IEEE Trans. Cybern. 2020, in press. Available online: https://ieeexplore.ieee.org/document/9032363/ (accessed on 11 March 2020). [CrossRef]
- Min, A.T.W.; Sagarna, R.; Gupta, A.; Ong, Y.-S.; Goh, C.K. Knowledge transfer through machine learning in aircraft design. IEEE Comput. Intell. Mag. 2017, 12, 48–60. [Google Scholar] [CrossRef]
- Feng, L.; Zhou, L.; Zhong, J.H.; Gupta, A.; Ong, Y.-S.; Tan, K.C.; Qin, A.K. Evolutionary multitasking via explicit autoencoding. IEEE Trans. Cybern. 2019, 49, 3457–3470. [Google Scholar] [CrossRef]
- Liang, Z.P.; Zhang, J.; Feng, L.; Zhu, Z.X. A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking. Expert Syst. Appl. 2019, 138, 1–18. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, X.P. Multiobjective multifactorial operation optimization for continuous annealing production process. Ind. Eng. Chem. Res. 2019, 58, 19166–19178. [Google Scholar] [CrossRef]
- Ong, Y.-S. Towards evolutionary multitasking: A new paradigm in evolutionary computation. In Proceedings of the International Conference on Computational Intelligence, Cyber Security and Computational Models, Coimbatore, India, 17–19 December 2015; pp. 25–26. [Google Scholar]
- Gupta, A.; Da, B.S.; Yuan, Y.; Ong, Y.-S. On the emerging notion of evolutionary multitasking: A computational analog of cognitive multitasking. In Recent Advances in Evolutionary Multi-Objective Optimization; Bechikh, S., Datta, R., Gupta, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 139–157. [Google Scholar]
- Cheng, M.Y. Attribute Selection Method Based on Binary Ant Colony Optimization and Fractal Dimension. Ph.D. Thesis, Hefei University of Technology, Hefei, China, 2017. (In Chinese). [Google Scholar]
- Chen, W.Q. Active Module Identification in Biological Networks. Ph.D. Thesis, University of Birmingham, Birmingham, UK, 2018. [Google Scholar]
- Min, A.T.W. Transfer Optimization in Complex Engineering Design. Ph.D. Thesis, Nanyang Technological University, Singapore, 2019. [Google Scholar]
- Da, B.S. Methods in Multi-Source Data-Driven Transfer Optimization. Ph.D. Thesis, Nanyang Technological University, Singapore, 2019. [Google Scholar]
- Gupta, A.; Ong, Y.-S. Back to the roots: Multi-x evolutionary computation. Cogn. Comput. 2019, 11, 1–17. [Google Scholar] [CrossRef]
- Trivedi, A.; Srinivasan, D.; Sanyal, K.; Ghosh, A. A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Trans. Evol. Comput. 2017, 21, 440–462. [Google Scholar] [CrossRef]
- Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6, 182–197. [Google Scholar] [CrossRef] [Green Version]
- Bader, J.; Zitzler, E. HypE: An algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 2011, 19, 45–76. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Q.F.; Li, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 2007, 11, 712–731. [Google Scholar] [CrossRef]
- Rice, J.; Cloninger, C.R.; Reich, T. Multifactorial inheritance with cultural transmission and assortative mating. I. Description and basic properties of the unitary models. Am. J. Hum. Genet. 1978, 30, 618–643. [Google Scholar]
- Cloninger, C.R.; Rice, J.; Reich, T. Multifactorial inheritance with cultural transmission and assortative mating. II. A general model of combined polygenic and cultural inheritance. Am. J. Hum. Genet. 1979, 31, 176–198. [Google Scholar]
- Gupta, A.; Mańdziuk, J.; Ong, Y.-S. Evolutionary multitasking in bi-level optimization. Complex Intell. Syst. 2015, 1, 83–95. [Google Scholar] [CrossRef] [Green Version]
- Gupta, A.; Ong, Y.-S.; Feng, L.; Tan, K.C. Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans. Cybern. 2017, 47, 1652–1665. [Google Scholar] [CrossRef]
- Ding, J.L.; Yang, C.E.; Jin, Y.C.; Chai, T.Y. Generalized multi-tasking for evolutionary optimization of expensive problems. IEEE Trans. Evol. Comput. 2019, 23, 44–58. [Google Scholar] [CrossRef]
- Bridges, C.L.; Goldberg, D.E. An analysis of reproduction and crossover in a binary-coded genetic algorithm. In Proceedings of the International Conference on Genetic Algorithms and Their Application, Cambridge, MA, USA, 28–31 July 1987; pp. 9–13. [Google Scholar]
- Bali, K.K.; Ong, Y.-S.; Gupta, A.; Tan, P.S. Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation: MFEA-II. IEEE Trans. Evol. Comput. 2020, 24, 69–83. [Google Scholar] [CrossRef]
- Tang, Z.D.; Gong, M.G.; Wu, Y.; Liu, W.F.; Xie, Y. Regularized evolutionary multitask optimization: Learning to intertask transfer in aligned subspace. IEEE Trans. Evol. Comput. 2020, 25, 262–276. [Google Scholar] [CrossRef]
- Da, B.S.; Gupta, A.; Ong, Y.-S. Curbing negative influences online for seamless transfer evolutionary optimization. IEEE Trans. Cybern. 2019, 49, 4365–4378. [Google Scholar] [CrossRef]
- Yi, J.; Bai, J.R.; He, H.B.; Zhou, W.; Yao, L.Z. A multifactorial evolutionary algorithm for multitasking under interval uncertainties. IEEE Trans. Evol. Comput. 2020, 24, 908–922. [Google Scholar] [CrossRef]
- Osaba, E.; Martinez, A.D.; Lobo, J.L.; Lana, I.; Ser, J.D. On the transferability of knowledge among vehicle routing problems by using cellular evolutionary multitasking. In Proceedings of the IEEE International Conference on Intelligent Transportation Systems, Rhodes, Greece, 20–23 September 2020; pp. 1–8. [Google Scholar]
- Lian, Y.C.; Huang, Z.X.; Zhou, Y.R.; Chen, Z.F. Improve theoretical upper bound of Jumpk function by evolutionary multitasking. In Proceedings of the High Performance Computing and Cluster Technologies Conference, Guangzhou, China, 22–24 June 2019; pp. 44–50. [Google Scholar]
- Huang, Z.X.; Chen, Z.F.; Zhou, Y.R. Analysis on the efficiency of multifactorial evolutionary algorithms. In Proceedings of the International Conference on Parallel Problem Solving from Nature, Glasgow, UK, 19–24 July 2020; pp. 634–647. [Google Scholar]
- Gupta, A.; Ong, Y.-S. Genetic transfer or population diversification? Deciphering the secret ingredients of evolutionary multitask optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Athens, Greece, 6–9 December 2016; pp. 1–7. [Google Scholar]
- Da, B.S.; Gupta, A.; Ong, Y.-S.; Feng, L. The boon of gene-culture interaction for effective evolutionary multitasking. In Proceedings of the Australasian Conference on Artificial Life and Computational Intelligence, Canberra, Australia, 2–5 February 2016; pp. 54–65. [Google Scholar]
- Peng, D.M.; Cai, Y.Q.; Fu, S.K.; Luo, W. Experimental analysis of selective imitation for multifactorial differential evolution. In Proceedings of the International Conference on Bio-Inspired Computing: Theories and Applications, Zhengzhou, China, 22–25 November 2019; pp. 15–26. [Google Scholar]
- Wang, N.; Xu, Q.Z.; Fei, R.; Yang, J.G.; Wang, L. Rigorous analysis of multi-factorial evolutionary algorithm as multi-population evolution model. Int. J. Comput. Intell. Syst. 2019, 12, 1121–1133. [Google Scholar] [CrossRef] [Green Version]
- Bean, J.C. Genetic algorithms and random keys for sequencing and optimization. Orsa J. Comput. 1994, 6, 154–160. [Google Scholar] [CrossRef]
- Yuan, Y.; Ong, Y.-S.; Gupta, A.; Tan, P.S.; Xu, H. Evolutionary multitasking in permutation-based combinatorial optimization problems: Realization with TSP, QAP, LOP, and JSP. In Proceedings of the IEEE Region 10 Conference, Singapore, 22–25 November 2016; pp. 3157–3164. [Google Scholar]
- Mirabi, M. A novel hybrid genetic algorithm for the multidepot periodic vehicle routing problem. Artif. Intell. Eng. Des. Anal. Manuf. Aiedam 2014, 29, 45–54. [Google Scholar] [CrossRef]
- Prins, C. Two memetic algorithms for heterogeneous fleet vehicle routing problems. Eng. Appl. Artif. Intell. 2009, 22, 916–928. [Google Scholar] [CrossRef]
- Zhou, L.; Feng, L.; Zhong, J.H.; Ong, Y.-S.; Zhu, Z.X.; Sha, E. Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Athens, Greece, 6–9 December 2016; pp. 1–8. [Google Scholar]
- Feng, L.; Zhou, L.; Gupta, A.; Zhong, J.H.; Zhu, Z.X.; Tan, K.C.; Qin, K. Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking. IEEE Trans. Cybern. 2019, in press. Available online: https://ieeexplore.ieee.org/document/8938734 (accessed on 23 December 2019). [CrossRef]
- Chandra, R.; Gupta, A.; Ong, Y.-S.; Goh, C.K. Evolutionary multi-task learning for modular training of feedforward neural networks. In Proceedings of the International Conference on Neural Information Processing, Kyoto, Japan, 16–21 October 2016; pp. 37–46. [Google Scholar]
- Wen, Y.-W.; Ting, C.-K. Learning ensemble of decision trees through multifactorial genetic programming. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 24–29 July 2016; pp. 5293–5300. [Google Scholar]
- Zhong, J.H.; Ong, Y.-S.; Cai, W.T. Self-learning gene expression programming. IEEE Trans. Evol. Comput. 2016, 20, 65–80. [Google Scholar] [CrossRef]
- Zhong, J.H.; Feng, L.; Cai, W.T.; Ong, Y.-S. Multifactorial genetic programming for symbolic regression problems. IEEE Trans. Syst. ManCybern. Syst. 2020, 50, 4492–4505. [Google Scholar] [CrossRef]
- Binh, H.T.T.; Thanh, P.D.; Trung, T.B.; Thao, L.P. Effective multifactorial evolutionary algorithm for solving the cluster shortest path tree problem. In Proceedings of the IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8. [Google Scholar]
- Trung, T.B.; Thanh, L.T.; Hieu, L.T.; Thanh, P.D.; Binh, H.T.T. Multifactorial evolutionary algorithm for clustered minimum routing cost problem. In Proceedings of the International Symposium on Information and Communication Technology, Hanoi, Vietnam, 4–6 December 2019; pp. 170–177. [Google Scholar]
- Thanh, P.D.; Dung, D.A.; Tien, T.N.; Binh, H.T.T. An effective representation scheme in multifactorial evolutionary algorithm for solving cluster shortest-path tree problem. In Proceedings of the IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8. [Google Scholar]
- Thanh, P.D.; Binh, H.T.T.; Trung, T.B.; Long, N.B. Multifactorial evolutionary algorithm for solving clustered tree problems: Competition among Cayley codes. Memetic Comput. 2020, 12, 185–217. [Google Scholar]
- Raidl, G.R.; Julstrom, B.A. Edge sets: An effective evolutionary coding of spanning trees. IEEE Trans. Evol. Comput. 2003, 7, 225–239. [Google Scholar] [CrossRef]
- Tam, N.T.; Tuan, T.Q.; Binh, H.T.T.; Swami, A. Multifactorial evolutionary optimization for maximizing data aggregation tree lifetime in wireless sensor networks. In Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, Online Only, CA, USA, 27 April–9 May 2020; pp. 114130Z:1–114130Z:14. [Google Scholar]
- Thanh, P.D.; Binh, H.T.T.; Trung, T.B. An efficient strategy for usingmultifactorial optimization to solve the clustered shortest path tree problem. Appl. Intelliigence 2020, 50, 1233–1258. [Google Scholar] [CrossRef]
- Binh, H.T.T.; Thanh, P.D.; Thang, T.B. New approach to solving the clustered shortest-path tree problem based on reducing the search space of evolutionary algorithm. Knowl. -Based Syst. 2019, 180, 12–25. [Google Scholar] [CrossRef] [Green Version]
- Binh, H.T.T.; Thanh, P.D. Two levels approach based on multifactorial optimization to solve the clustered shortest path tree problem. Evol. Intell. 2020, in press. Available online: https://link.springer.com/article/10.1007/s12065-020-00501-w (accessed on 14 October 2020).
- Binh, H.T.T.; Thang, T.B.; Long, N.B.; Hoang, N.V.; Thanh, P.D. Multifactorial evolutionary algorithm for inter-domain path computation under domain uniqueness constraint. In Proceedings of the IEEE Congress on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Zhang, K.; Hao, W.N.; Yu, X.H.; Jin, D.W.; Zhang, Z.H. A multitasking genetic algorithm for mamdani fuzzy system with fully overlapping triangle membership functions. Int. J. Fuzzy Syst. 2020, 22, 2449–2465. [Google Scholar] [CrossRef]
- Chen, W.Q.; Zhu, Z.X.; He, S. MUMI: Multitask module identification for biological networks. IEEE Trans. Evol. Comput. 2020, 24, 765–776. [Google Scholar] [CrossRef]
- Wang, C.; Ma, H.; Chen, G.; Hartmann, S. Evolutionary multitasking for semantic web service composition. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 2490–2497. [Google Scholar]
- Wang, C.; Ma, H.; Chen, A.; Hartmann, S. Comprehensive quality-aware automated semantic web service composition. In Proceedings of the Australasian Joint Conference on Artificial Intelligence, Melbourne, Australia, 19–20 August 2017; pp. 195–207. [Google Scholar]
- Wang, T.-C.; Liaw, R.-T. Multifactorial genetic fuzzy data mining for building membership functions. In Proceedings of the IEEE Congress on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Ting, C.-K.; Wang, T.-C.; Liaw, R.-T.; Hong, T.-P. Genetic algorithm with a structure-based representation for genetic-fuzzy data mining. Soft Comput. 2017, 21, 2871–2882. [Google Scholar] [CrossRef]
- Hao, X.X.; Qu, R.; Liu, J. A unified framework of graph-based evolutionary multitasking hyper-heuristic. IEEE Trans. Evol. Comput. 2021, 25, 35–47. [Google Scholar] [CrossRef]
- Bali, K.K.; Gupta, A.; Ong, Y.-S.; Tan, P.S. Cognizant multitasking in multiobjective multifactorial evolution: MO-MFEA-II. IEEE Trans. Cybern. 2020, 51, 1784–1796. [Google Scholar] [CrossRef]
- Da, B.S.; Gupta, A.; Ong, Y.-S.; Feng, L. Evolutionary multitasking across single and multi-objective formulations for improved problem solving. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 24–29 July 2016; pp. 1695–1701. [Google Scholar]
- Tuan, N.Q.; Hoang, T.D.; Binh, H.T.T. A guided differential evolutionary multi-tasking with powell search method for solving multi-objective continuous optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8. [Google Scholar]
- Li, G.H.; Zhang, Q.F.; Gao, W.F. Multipopulation evolution framework for multifactorial optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15–19 July 2018; pp. 215–216. [Google Scholar]
- Li, G.H.; Lin, Q.Z.; Gao, W.F. Multifactorial optimization via explicit multipopulation evolutionary framework. Inf. Sci. 2020, 512, 1555–1570. [Google Scholar] [CrossRef]
- Chen, Y.L.; Zhong, J.H.; Tan, M.K. A fast memetic multi-objective differential evolution for multi-tasking optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8. [Google Scholar]
- Feng, L.; Zhou, W.; Zhou, L.; Jiang, S.W.; Zhong, J.H.; Da, B.S.; Zhu, Z.X.; Wang, Y. An empirical study of multifactorial PSO and multifactorial DE. In Proceedings of the IEEE Congress on Evolutionary Computation, San Sebastian, Spain, 5–8 June 2017; pp. 921–928. [Google Scholar]
- Liu, D.N.; Huang, S.J.; Zhong, J.H. Surrogate-assisted multi-tasking memetic algorithm. In Proceedings of the IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8. [Google Scholar]
- Cai, Y.Q.; Peng, D.M.; Fu, S.K.; Tian, H. Multitasking differential evolution with difference vector sharing mechanism. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Xiamen, China, 6–9 December 2019; pp. 3309–3346. [Google Scholar]
- Cheng, M.Y.; Gupta, A.; Ong, Y.-S.; Ni, Z.W. Coevolutionary multitasking for concurrent global optimization: With case studies in complex engineering design. Eng. Appl. Artif. Intell. 2017, 64, 13–24. [Google Scholar] [CrossRef]
- Zhang, B.Y.; Qin, A.K.; Sellis, T. Evolutionary feature subspaces generation for ensemble classification. In Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15–19 July 2018; pp. 577–584. [Google Scholar]
- Song, H.; Qin, A.K.; Tsai, P.-W.; Liang, J.J. Multitasking multi-swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 1937–1944. [Google Scholar]
- Xiao, H.; Yokoya, G.; Hatanaka, T. Multifactorial PSO-FA hybrid algorithm for multiple car design benchmark. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Bari, Italy, 6–9 October 2019; pp. 1926–1931. [Google Scholar]
- Cheng, M.Y.; Qian, Q.; Ni, Z.W.; Zhu, X.H. Co-evolutionary particle swarm optimization for multitasking. Pattern Recognit. Artif. Intell. 2018, 31, 322–334. (In Chinese) [Google Scholar]
- Cheng, M.Y.; Qian, Q.; Ni, Z.W.; Zhu, X.H. Information exchange particle swarm optimization for multitasking. Pattern Recognit. Artif. Intell. 2019, 32, 385–397. (In Chinese) [Google Scholar]
- Tang, Z.D.; Gong, M.G. Adaptive multifactorial particle swarm optimisation. Caai Trans. Intell. Technol. 2019, 4, 37–46. [Google Scholar] [CrossRef]
- Yokoya, G.; Xiao, H.; Hatanaka, T. Multifactorial optimization using artificial bee colony and its application to car structure design optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 3404–3409. [Google Scholar]
- Xu, Z.W.; Zhang, K.; Xu, X.; He, J.J. A fireworks algorithm based on transfer spark for evolutionary multitasking. Front. Neurorobotics 2020, 13, 109. [Google Scholar] [CrossRef]
- Cheng, M.Y.; Qian, Q.; Ni, Z.W.; Zhu, X.H. Self-organized migrating algorithm for multi-task optimization with information filtering. J. Comput. Appl. 2020, in press. Available online: http://www.joca.cn/CN/10.11772/j.issn.1001-9081.2020091390 (accessed on 26 November 2020). (In Chinese).
- Zheng, X.L.; Lei, Y.; Gong, M.G.; Tang, Z.D. Multifactorial brain storm optimization algorithm. In Proceedings of the International Conference on Bio-inspired Computing: Theories and Applications, Xi’an, China, 28–30 October 2016; pp. 47–53. [Google Scholar]
- Lyu, C.; Shi, Y.H.; Sun, L.J. A novel multi-task optimization algorithm based on the brainstorming process. IEEE Access 2020, 8, 217134–217149. [Google Scholar] [CrossRef]
- Osaba, E.; Ser, J.D.; Yang, X.S.; Iglesias, A.; Galvez, A. COEBA: A coevolutionary bat algorithm for discrete evolutionary multitasking. In Proceedings of the International Conference on Computational Science, Amsterdam, The Netherlands, 3–5 June 2020; pp. 244–256. [Google Scholar]
- Chen, Q.J.; Ma, X.L.; Zhu, Z.X.; Sun, Y.W. Evolutionary multi-tasking single-objective optimization based on cooperative co-evolutionary memetic algorithm. In Proceedings of the International Conference on Computational Intelligence and Security, Hong Kong, China, 15–18 December 2017; pp. 197–201. [Google Scholar]
- Liang, J.; Qiao, K.J.; Yuan, M.H.; Yu, K.J.; Qu, B.Y.; Ge, S.L.; Li, Y.X.; Chen, G.L. Evolutionary multi-task optimization for parameters extraction of photovoltaic models. Energy Convers. Manag. 2020, 207, 112509. [Google Scholar] [CrossRef]
- Hashimoto, R.; Ishibuchi, H.; Masuyama, N.; Nojima, Y. Analysis of evolutionary multi-tasking as an island model. In Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15–19 July 2018; pp. 1894–1897. [Google Scholar]
- Wen, Y.-W.; Ting, C.-K. Parting ways and reallocating resources in evolutionary multitasking. In Proceedings of the IEEE Congress on Evolutionary Computation, San Sebastian, Spain, 5–8 June 2017; pp. 2404–2411. [Google Scholar]
- Zheng, X.L.; Lei, Y.; Qin, A.K.; Zhou, D.Y.; Shi, J.; Gong, M.G. Differential evolutionary multi-task optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 1914–1921. [Google Scholar]
- Lin, J.B.; Liu, H.L.; Xue, B.; Zhang, M.J.; Gu, F.Q. Multi-objective multi-tasking optimization based on incremental learning. IEEE Trans. Evol. Comput. 2020, 24, 824–838. [Google Scholar] [CrossRef]
- Zhou, Y.J.; Wang, T.H.; Peng, X.G. MFEA-IG: A multi-task algorithm for mobile agents path planning. In Proceedings of the IEEE Congress on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–7. [Google Scholar]
- Hu, H.; Zhou, Y.J.; Wang, T.H.; Peng, X.G. A multi-task algorithm for autonomous underwater vehicles 3D path planning. In Proceedings of the International Conference on Unmanned Systems, Harbin, China, 27–28 November 2020; pp. 972–977. [Google Scholar]
- Min, A.T.W.; Ong, Y.-S.; Gupta, A.; Goh, C.K. Multiproblem surrogates: Transfer evolutionary multiobjective optimization of computationally expensive problems. IEEE Trans. Evol. Comput. 2019, 23, 15–28. [Google Scholar] [CrossRef]
- Yin, J.; Zhu, A.M.; Zhu, Z.X.; Yu, Y.N.; Ma, X.L. Multifactorial evolutionary algorithm enhanced with cross-task search direction. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 2244–2251. [Google Scholar]
- Feng, Y.L.; Feng, L.; Hou, Y.Q.; Tan, K.C. Large-scale optimization via evolutionary multitasking assisted random embedding. In Proceedings of the IEEE Congress on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Feng, L.; Huang, Y.X.; Zhou, L.; Zhong, J.H.; Gupta, A.; Tang, K.; Tan, K.C. Explicit evolutionary multitasking for combinatorial optimization: A case study on capacitated vehicle routing problem. IEEE Trans. Cybern. 2020, in press. Available online: https://ieeexplore.ieee.org/document/9023952/ (accessed on 4 March 2020). [CrossRef] [PubMed]
- Bali, K.K.; Gupta, A.; Feng, L.; Ong, Y.-S.; Tan, P.S. Linearized domain adaptation in evolutionary multitasking. In Proceedings of the IEEE Congress on Evolutionary Computation, San Sebastian, Spain, 5–8 June 2017; pp. 1295–1302. [Google Scholar]
- Shang, Q.X.; Zhou, L.; Feng, L. Multi-task optimization algorithm based on denoising auto-encoder. J. Dalian Univ. Technol. 2019, 59, 417–426. (In Chinese) [Google Scholar]
- Liang, Z.P.; Dong, H.; Liu, C.; Liang, W.Q.; Zhu, Z.X. Evolutionary multitasking for multiobjective optimization with subspace alignment and adaptive differential evolution. IEEE Trans. Cybern. 2020, in press. Available online: https://ieeexplore.ieee.org/document/9123962/ (accessed on 24 June 2020). [CrossRef]
- Xue, X.M.; Zhang, K.; Tan, K.C.; Feng, L.; Wang, J.; Chen, G.D.; Zhao, X.G.; Zhang, L.M.; Yao, J. Affine transformation-enhanced multifactorial optimization for heterogeneous problems. IEEE Trans. Cybern. 2020, in press. Available online: https://ieeexplore.ieee.org/document/9295394/ (accessed on 15 December 2020). [CrossRef] [PubMed]
- Chen, Z.F.; Zhou, Y.R.; He, X.Y.; Zhang, J. Learning task relationships in evolutionary multitasking for multiobjective continuous optimization. IEEE Trans. Cybern. 2020, in press. Available online: https://ieeexplore.ieee.org/document/9262898/ (accessed on 18 November 2020). [CrossRef]
- Xu, Q.Z.; Zhang, J.H.; Fei, R.; Li, W. Parameter analysis on multifactorial evolutionary algorithm. J. Eng. 2020, 2020, 620–625. [Google Scholar] [CrossRef]
- Yang, C.E.; Ding, J.L.; Jin, Y.C.; Wang, C.Z.; Chai, T.Y. Multitasking multiobjective evolutionary operational indices optimization of beneficiation processes. IEEE Trans. Autom. Sci. Eng. 2019, 16, 1046–1057. [Google Scholar] [CrossRef]
- Yang, C.E.; Ding, J.L.; Tan, K.C.; Jin, Y.C. Two-stage assortative mating for multi-objective multifactorial evolutionary optimization. In Proceedings of the IEEE 56th Annual Conference on Decision and Control, Melbourne, Australia, 12–15 December 2017; pp. 76–81. [Google Scholar]
- Wu, T.; Bu, S.Q.; Wei, X.; Wang, G.B.; Zhou, B. Multitasking multi-objective operation optimization of integrated energy system considering biogas-solar-wind renewables. Energy Convers. Manag. 2021, 229, 113736. [Google Scholar] [CrossRef]
- Liaw, R.-T.; Ting, C.-K. Evolutionary many-tasking based on biocoenosis through symbiosis: A framework and benchmark problems. In Proceedings of the IEEE Congress on Evolutionary Computation, San Sebastian, Spain, 5–8 June 2017; pp. 2266–2273. [Google Scholar]
- Liaw, R.-T.; Ting, C.-K. Evolution of biocoenosis through symbiosis with fitness approximation formany-tasking optimization. Memetic Comput. 2020, 12, 399–417. [Google Scholar] [CrossRef]
- Binh, H.T.T.; Tuan, N.Q.; Long, D.C.T. A multi-objective multi-factorial evolutionary algorithm with reference-point-based approach. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 2824–2831. [Google Scholar]
- Zheng, X.L.; Qin, A.K.; Gong, M.G.; Zhou, D.Y. Self-regulated evolutionary multi-task optimization. IEEE Trans. Evol. Comput. 2020, 24, 16–28. [Google Scholar] [CrossRef]
- Osaba, E.; Martinez, A.D.; Galvez, A.; Iglesias, A.; Ser, J.D. dMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems. In Proceedings of the Genetic and Evolutionary Computation Conference, Cancún, Mexico, 8–12 July 2020; pp. 1690–1696. [Google Scholar]
- Gong, M.G.; Tang, Z.D.; Li, H.; Zhang, J. Evolutionary multitasking with dynamic resource allocating strategy. IEEE Trans. Evol. Comput. 2019, 23, 858–869. [Google Scholar] [CrossRef]
- Yao, S.S.; Dong, Z.M.; Wang, X.P.; Ren, L. A Multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy. Inf. Sci. 2020, 511, 18–35. [Google Scholar] [CrossRef]
- Chen, Q.J.; Ma, X.L.; Sun, Y.W.; Zhu, Z.X. Adaptive memetic algorithm based evolutionary multi-tasking single-objective optimization. In Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning, Shenzhen, China, 10–13 November 2017; pp. 462–472. [Google Scholar]
- Tang, J.; Chen, Y.K.; Deng, Z.X.; Xiang, Y.P.; Joy, C.P. A group-based approach to improve multifactorial evolutionary algorithm. In Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13–19 July 2018; pp. 3870–3876. [Google Scholar]
- Tang, Z.D.; Gong, M.G.; Jiang, F.L.; Li, H.; Wu, Y. Multipopulation optimization for multitask optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 1906–1913. [Google Scholar]
- Jin, C.; Tsai, P.-W.; Qin, A.K. A study on knowledge reuse strategies in multitasking differential evolution. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 1564–1571. [Google Scholar]
- Ma, X.L.; Chen, Q.J.; Yu, Y.N.; Sun, Y.W.; Ma, L.J.; Zhu, Z.X. A two-level transfer learning algorithm for evolutionary multitasking. Front. Neurosci. 2020, 13, 1408. [Google Scholar] [CrossRef] [Green Version]
- Xie, T.; Gong, M.G.; Tang, Z.D.; Lei, Y.; Liu, J.; Wang, Z. Enhancing evolutionary multifactorial optimization based on particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 24–29 July 2016; pp. 1658–1665. [Google Scholar]
- Da, B.S.; Ong, Y.-S.; Feng, L.; Qin, A.K.; Gupta, A.; Zhu, Z.X.; Ting, C.-K.; Tang, K.; Yao, X. Evolutionary Multitasking for Single-Objective Continuous Optimization: Benchmark Problems, Performance Metric and Baseline Results; Technical Report; Nanyang Technological University: Singapore, 2016. [Google Scholar]
- Zhou, L.; Feng, L.; Zhong, J.H.; Zhu, Z.X.; Da, B.S.; Wu, Z. A study of similarity measure between tasks for multifactorial evolutionary algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15–19 July 2018; pp. 229–230. [Google Scholar]
- Gupta, A.; Ong, Y.-S.; Da, B.S.; Feng, L.; Handoko, S.D. Landscape synergy in evolutionary multitasking. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 24–29 July 2016; pp. 3076–3083. [Google Scholar]
- Nguyen, T.B.; Browne, W.N.; Zhang, M.J. Relatedness measures to aid the transfer of building blocks among multiple tasks. In Proceedings of the Genetic and Evolutionary Computation Conference, Cancún, Mexico, 8–12 July 2020; pp. 377–385. [Google Scholar]
- Sagarna, R.; Ong, Y.-S. Concurrently searching branches in software tests generation through multitask evolution. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Athens, Greece, 6–9 December 2016; pp. 1–8. [Google Scholar]
- Scott, E.O.; De Jong, K.A. Automating knowledge transfer with multi-task optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 2252–2259. [Google Scholar]
- Chen, Y.L.; Zhong, J.H.; Feng, L.; Zhang, J. An adaptive archive-based evolutionary framework for many-task optimization. IEEE Trans. Emerg. Top. Comput. Intell. 2020, 4, 369–384. [Google Scholar] [CrossRef]
- Shang, Q.; Zhang, L.; Feng, L.; Hou, Y.; Zhong, J.; Gupta, A.; Tan, K.C.; Liu, H.L. A preliminary study of adaptive task selection in explicit evolutionary many-tasking. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 2153–2159. [Google Scholar]
- Xu, Q.Z.; Tian, B.L.; Wang, L.; Sun, Q.; Zou, F. An effective variable transfer strategy in multitasking optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, Cancún, Mexico, 8–12 July 2020; pp. 59–60. [Google Scholar]
- Xu, Q.Z.; Wang, L.; Yang, J.G.; Wang, N.; Fei, R.; Sun, Q. An effective variable transformation strategy in multitasking evolutionary algorithms. Complexity 2020, 2020, 8815117. [Google Scholar] [CrossRef]
- Zhang, D.Q.; Jiang, M.Y. Hetero-dimensional multitask neuroevolution for chaotic time series prediction. IEEE Access 2020, 8, 123135–123150. [Google Scholar] [CrossRef]
- Wang, L.; Sun, Q.; Xu, Q.Z.; Tian, B.L.; Li, W. On the order of variables for multitasking optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, Cancún, Mexico, 8–12 July 2020; pp. 57–58. [Google Scholar]
- Wang, L.; Sun, Q.; Xu, Q.Z.; Li, W.; Jiang, Q.Y. Analysis of multitasking evolutionary algorithms under the order of solution variables. Complexity 2020, 2020, 4609489. [Google Scholar] [CrossRef]
- Zhou, L.; Feng, L.; Tan, K.C.; Zhong, J.H.; Zhu, Z.X.; Liu, K.; Chen, C. Toward adaptive knowledge transfer in multifactorial evolutionary computation. IEEE Trans. Cybern. 2020, in press. Available online: https://ieeexplore.ieee.org/document/9027113/ (accessed on 6 March 2020). in press. [CrossRef]
- Zhou, L.; Feng, L.; Liu, K.; Chen, C.; Deng, S.J.; Xiang, T.; Jiang, S.W. Towards effective mutation for knowledge transfer in multifactorial differential evolution. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 1541–1547. [Google Scholar]
- Gong, D.W.; Xu, B.; Zhang, Y.; Guo, Y.N.; Yang, S.X. A similarity-based cooperative co-evolutionary algorithm for dynamic interval multiobjective optimization problems. IEEE Trans. Evol. Comput. 2020, 24, 142–156. [Google Scholar] [CrossRef] [Green Version]
- Gong, D.W.; Sun, J.; Miao, Z. A set-based genetic algorithm for interval many-objective optimization problems. IEEE Trans. Evol. Comput. 2018, 22, 47–60. [Google Scholar] [CrossRef]
- Więckowski, J.; Kizielewicz, B.; Kołodziejczyk, J. The search of the optimal preference values of the characteristic objects by using particle swarm optimization in the uncertain environment. In Proceedings of the 12th KES International Conference on Intelligent Decision Technologies, Split, Croatia, 17–19 June 2020; pp. 353–363. [Google Scholar]
- Burke, E.; Kendall, G.; Newall, J.; Hart, E.; Ross, P.; Schulenburg, S. Hyper-heuristics: An emerging direction in modern search technology. In Handbook of Metaheuristics; Glover, F.W., Kochenberger, G.A., Eds.; Springer: Berlin/Heidelberg, Germany, 2003; pp. 457–474. [Google Scholar]
- Pillay, N.; Qu, R. Hyper-Heuristics: Theory and Applications; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
- Więckowski, J.; Kizielewicz, B.; Kołodziejczyk, J. Application of hill climbing algorithm in determining the characteristic objects preferences based on the reference set of alternatives. In Proceedings of the 12th KES International Conference on Intelligent Decision Technologies, Split, Croatia, 17–19 June 2020; pp. 341–351. [Google Scholar]
- Więckowski, J.; Kizielewicz, B.; Kołodziejczyk, J. Finding an approximate global optimum of characteristic objects preferences by using simulated annealing. In Proceedings of the 12th KES International Conference on Intelligent Decision Technologies, Split, Croatia, 17–19 June 2020; Springer: Singapore, 2020; pp. 365–375. [Google Scholar]
- Zhou, Z.F.; Ma, X.L.; Liang, Z.P.; Zhu, Z.X. Multi-objective multi-factorial memetic algorithm based on bone route and large neighborhood local search for VRPTW. In Proceedings of the IEEE Congress on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Huang, L.Y.; Feng, L.; Wang, H.D.; Hou, Y.Q.; Liu, K.; Chen, C. A preliminary study of improving evolutionary multi-objective optimization via knowledge transfer from single-objective problems. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Toronto, ON, Canada, 11–14 October 2020; pp. 1552–1559. [Google Scholar]
- Zheng, Y.J.; Zhu, Z.X.; Qi, Y.T.; Wang, L.; Ma, X.L. Multi-objective multifactorial evolutionary algorithm enhanced with the weighting helper-task. In Proceedings of the International Conference on Industrial Artificial Intelligence, Shenyang, China, 23–25 October 2020; pp. 1–6. [Google Scholar]
- Yu, Y.N.; Zhu, A.M.; Zhu, Z.X.; Lin, Q.Z.; Yin, J.; Ma, X.L. Multifactorial differential evolution with opposition-based learning for multi-tasking optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10–13 June 2019; pp. 1898–1905. [Google Scholar]
- Yao, S.S.; Dong, Z.M.; Wang, X.P. A multiobjective multifactorial evolutionary algorithm based on decomposition. Control Decis. 2021, 36, 637–644. (In Chinese) [Google Scholar]
- Mo, J.J.; Fan, Z.; Li, W.J.; Fang, Y.; You, Y.G.; Cai, X.Y. (2017) Multi-factorial evolutionary algorithm based on M2M decomposition. In Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning, Shenzhen, China, 10–13 November 2017; pp. 134–144. [Google Scholar]
- Liao, P.; Sun, C.L.; Zhang, G.C.; Jin, Y.C. Multi-surrogate multi-tasking optimization of expensive problems. Knowl.-Based Syst. 2020, 205, 106262. [Google Scholar] [CrossRef]
- Binh, H.T.T.; Thanh, P.D.; Trung, T.B.; Thanh, L.C.; Phong, L.M.H.; Swami, A.; Lam, B.T. A multifactorial optimization paradigm for linkage tree genetic algorithm. Inf. Sci. 2020, 540, 325–344. [Google Scholar]
- Park, J.; Mei, Y.; Nguyen, S.; Chen, G.; Zhang, M.J. Evolutionary multitask optimisation for dynamic job shop scheduling using niched genetic programming. In Proceedings of the Australasian Joint Conference on Artificial Intelligence, Wellington, New Zealand, 11–14 December 2018; pp. 739–751. [Google Scholar]
- Rauniyar, A.; Nath, R.; Muhuri, P.K. Multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution routing problem. Comput. Ind. Eng. 2019, 130, 757–771. [Google Scholar] [CrossRef]
- Karunakaran, D.; Mei, Y.; Zhang, M.J. Multitasking genetic programming for stochastic team orienteering problem with time windows. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Xiamen, China, 6–9 December 2019; pp. 1598–1605. [Google Scholar]
- Zhuang, Z.Y.; Wei, C.; Li, B.; Xu, P.; Guo, Y.F.; Ren, J.C. Performance prediction model based on multi-task learning and co-evolutionary strategy for ground source heat pump system. IEEE Access 2019, 7, 117925–117933. [Google Scholar] [CrossRef]
- Shen, F.; Liu, J.; Wu, K. Evolutionary multitasking fuzzy cognitive map learning. Knowl. -Based Syst. 2019, 192, 105294. [Google Scholar] [CrossRef]
- Zhang, B.Y.; Qin, A.K.; Pan, H.; Sellis, T. A novel DNN training framework via data sampling and multi-task optimization. In Proceedings of the International Joint Conference on Neural Networks, Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Martinez, A.D.; Osaba, E.; Ser, J.D.; Herrera, F. Simultaneously evolving deep reinforcement learning models using multifactorial optimization. In Proceedings of the IEEE Conference on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Wei, T.Y.; Zhong, J.H. A preliminary study of knowledge transfer in multi-classification using gene expression programming. Front. Neurosci. 2020, 13, 1396. [Google Scholar] [CrossRef]
- Chen, K.; Xue, B.; Zhang, M.J.; Zhou, F.Y. An evolutionary multitasking-based feature selection method for high-dimensional classification. IEEE Trans. Cybern. 2020. Available online: https://ieeexplore.ieee.org/document/9311803/ (accessed on 31 December 2020).
- Tang, Z.D.; Gong, M.G.; Zhang, M.Y. Evolutionary multi-task learning for modular extremal learning machine. In Proceedings of the IEEE Congress on Evolutionary Computation, San Sebastian, Spain, 5–8 June 2017; pp. 474–479. [Google Scholar]
- Li, H.; Ong, Y.-S.; Gong, M.G.; Wang, Z.K. Evolutionary multitasking sparse reconstruction: Framework and case study. IEEE Trans. Evol. Comput. 2019, 23, 733–747. [Google Scholar] [CrossRef]
- Zhao, Y.Z.; Li, H.; Wu, Y.; Wang, S.F.; Gong, M.G. Endmember selection of hyperspectral images based on evolutionary multitask. In Proceedings of the IEEE Congress on Evolutionary Computation, Glasgow, UK, 19–24 July 2020; pp. 1–7. [Google Scholar]
- Sampath, L.P.M.I.; Gupta, A.; Ong, Y.-S.; Gooi, H.B. Evolutionary multitasking to support optimal power flow under rapid load variations. South. Power Syst. Technol. 2017, 11, 103–114. [Google Scholar]
- Liu, J.W.; Li, P.L.; Wang, G.B.; Zha, Y.X.; Peng, J.C.; Xu, G. A multitasking electric power dispatch approach with multi-objective multifactorial optimization algorithm. IEEE Access 2020, 8, 155902–155910. [Google Scholar] [CrossRef]
- Bao, L.; Qi, Y.T.; Shen, M.Q.; Bu, X.X.; Yu, J.S.; Li, Q.; Chen, P. An evolutionary multitasking algorithm for cloud computing service composition. In Proceedings of the World Congress on Services, Seattle, WA, USA, 25–30 June 2018; pp. 130–144. [Google Scholar]
- Singh, D.; Sisodia, D.S.; Singh, P. Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease. J. Biomed. Inform. 2020, 102, 103376. [Google Scholar] [CrossRef]
- Sinha, A.; Malo, P.; Deb, K. Unconstrained scalable test problems for single-objective bilevel optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Brisbane, Australia, 10–15 June 2012; pp. 1–8. [Google Scholar]
- Ruan, G.; Minku, L.L.; Menzel, S.; Sendhoff, B.; Yao, X. When and how to transfer knowledge in dynamic multi-objective optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Xiamen, China, 6–9 December 2019; pp. 2034–2041. [Google Scholar]
- Kohira, T.; Akira, O.; Kemmotsu, H.; Tatsukawa, T. Proposal of benchmark problem based on real-world car structure design optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15–19 July 2018; pp. 183–184. [Google Scholar]
- Xu, Q.Z.; Yang, H.; Wang, N.; Wu, G.H.; Jiang, Q.Y. Recent advances in multifactorial evolutionary algorithm. Comput. Eng. Appl. 2018, 54, 15–20. (In Chinese) [Google Scholar]
- Hao, G.-S.; Wang, G.-G.; Zhang, Z.-J.; Zou, D.-X. Optimization of the high order problems in evolutionary algorithms: An application of transfer learning. Int. J. Wirel. Mob. Comput. 2018, 14, 56–63. [Google Scholar] [CrossRef]
- Wang, G.-G.; Tan, Y. Improving metaheuristic algorithms with information feedback models. IEEE Trans. Cybern. 2019, 49, 542–555. [Google Scholar] [CrossRef]
- Yuan, Y.; Ong, Y.-S.; Feng, L.; Qin, A.K.; Gupta, A.; Da, B.S.; Zhang, Q.F.; Tan, K.C.; Jin, Y.C.; Ishibuchi, H. Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results; Technical Report; Nanyang Technological University: Singapore, 2016. [Google Scholar]
- Wolpert, D.H.; Macready, W.G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1997, 1, 67–82. [Google Scholar] [CrossRef] [Green Version]
- Sands, T. Comparison and interpretation methods for predictive control of mechanics. Algorithms 2019, 12, 232. [Google Scholar] [CrossRef] [Green Version]
- Sałabun, W.; Wątróbski, J.; Shekhovtsov, A. Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry 2020, 12, 1549. [Google Scholar] [CrossRef]
- Jiang, S.W.; Xu, C.; Gupta, A.; Feng, L.; Ong, Y.-S.; Zhang, A.N.; Tan, P.S. Complex and intelligent systems in manufacturing. IEEE Potentials 2016, 35, 23–28. [Google Scholar] [CrossRef]
- Sands, T. Development of deterministic artificial intelligence for unmanned underwater vehicles (UUV). J. Mar. Sci. Eng. 2020, 8, 578. [Google Scholar] [CrossRef]
Year | 2016 | 2017 | 2018 | 2019 | 2020 | Subtotal |
---|---|---|---|---|---|---|
Journal | 4 | 4 | 3 | 20(3) | 38(10) | 69 |
Conference | 12 | 9 | 12 | 19 | 19 | 71 |
Total | 16 | 13 | 15 | 39(3) | 57(10) | 140 |
Rank | Name | Affiliations | Address | Total Number (Journal + Conference) | |
---|---|---|---|---|---|
1 | Yew-Soon Ong | Nanyang Technological University | Singapore | [email protected] | 27 (17 + 10) |
2 | Abhishek Gupta | Singapore Institute of Manufacturing Technology (SIMTech) | Singapore | [email protected] | 25 (17 + 8) |
3 | Liang Feng | Chongqing University | Chongqing, China | [email protected] | 24 (13 + 11) |
4 | Zexuan Zhu | Shenzhen University | Shenzhen, China | [email protected] | 15 (6 + 9) |
5 | Jinghui Zhong | South China University of Technology | Guangzhou, China | [email protected] | 13 (7 + 6) |
6 | Maoguo Gong | Xidian University | Xi’an, China | [email protected] | 11 (5 + 6) |
7 | Huynh Thi Thanh Binh | Hanoi University of Science and Technology | Hanoi, Vietnam | [email protected] | 11 (4 + 7) |
8 | Kay Chen Tan | City University of Hong Kong | Hong Kong, China | [email protected] | 10 (7 + 3) |
Category | Domain | Problem | Algorithms |
---|---|---|---|
Benchmark problem | Continuous optimization problem | Single-objective optimization problem (SOOP) | MFEA [11], MFEA [18], None [21], MFEA-GHS [22], G-MFEA [39], MFEA-II [41], ASCMFDE [42], PGEA [49], MFDE with AIM [50], MPEF-SHADE [82], MFMP [83], MFDE [85], MFPSO [85], SaM-MA [86], MT-CPSO [86], MDE-DVSM [87], MTMSO [90], CPSOM [92], AMFPSO [94], MTO-FWA [96], MFBSO [98], BSMTO [99], BSMTO-II [99], EMTSO-CCMA [101], MFEARR [104], DEMTO [105], MFEA-DV [110], EMT-RE [111], LDA-MFEA [113], None [114], AT-MFEA [116], EBS-CMAES [122], EBSFA-CMAES [123], SREMTO [125], MTO-DRA [127], AMA [129], GMFEA [130], mMTDE [131], MTDE [132], TLTLA [133], MFEA [137], MaTDE [141], None [142], MFEA-VT [143,144], HD-MFEA [145], MFEA-FuR [146,147], MFEA-AKT [148], MFDE [149], MFEA/DE-OBL [160] |
Multiobjective optimization problem (MOOP) | EMT/ET [19], None [21], MFEA-GHS [22], AdaMOMFDE [23], MO-MFEA [38], AMTEA [43], IMFEA [44], MO-MFEA-II [79], GDE-MO-MFEA [81], MM-DE [84], MTO-FWA [96], EMTIL [106], TEMO-MPS [109], MOMFEA-SADE [115], EMT-LTR [117], TMO-MFEA [120], RPB-MO-MFEA [124], MFEA/D-DRA [128], MaTDE [141], MFEA-AKT [148], NSGAII+M [158], MO-MFEA/HELP TASK [159], MFEA/D [161], MFEA/D-M2M-SVM [162] | ||
Bi-level optimization problem | M-BLEA [37] | ||
Expensive optimization problem | MCEEA [39], MS-MTO [163] | ||
Discrete optimization problem | Deceptive trap function (DTF) | MF-LTGA [164] | |
Clustered traveling salesman problem (CluTSP) | MF-LTGA [165] | ||
Vehicle routing problem (VRP) | MFEA [18], MFCGA [45], P-MFEA [56], EMA [57], EEMTA [112], dMFEA-II [126], MTO-DRA [127], MOMFMA [157] | ||
Quadratic assignment problem (QAP) | MFEA [11], MFEA [18], MFEA-Perm-LBS [53], MTO-DRA [127] | ||
Knapsack problem (KP) | MFEA [18], AMTEA [43] | ||
Sudoku puzzles | MFEA [48], GMFEA [130] | ||
Travel salesman problem (TSP) | MFEA-Perm-LBS [53], S&M-MFEA [80], COEBA [100], dMFEA-II [126] | ||
Linear ordering problem (LOP) | MFEA-Perm-LBS [53] | ||
Job-shop scheduling problem (JSP) | MFEA [11], MFEA-Perm-LBS [53], NGP [165] | ||
9 LOGIC suite | None [140] | ||
N-bit parity problem | EMTL [58] | ||
Minimum routing cost clustered tree problem (CluMRCT) | MFEA [63] | ||
Pollution-routing problem (PRP) | None [166] | ||
Package delivery problem (PDP) | EEMTA [112] | ||
Team orienteering problem with time windows (TOPTW) | Island-EMT [167] | ||
Examination timetabling problem | EMHH [78] | ||
Graph coloring problem | EMHH [78] | ||
Minimum inter-cluster routing cost clustered tree problem (InterCluMRCT) | CC-MFEA [65] | ||
Clustered shortest path tree problem (CluSTP) | None [62], None [64], CC-MFEA [65], N-MFEA [68], N-MFEA [70] | ||
Real-world problem | Machine learning | Time series prediction problem | MFGP [61] |
Performance prediction problem | None [168] | ||
Gene regulatory network (GRN) reconstruction | MMMA-FCM [169] | ||
Community detection | MUMI [73] | ||
Chaotic time series prediction problem | HD-MFEA neuroevolution [145] | ||
Training deep neural networks (DNN) problem | AMTO [170], None [171] | ||
Fuzzy cognitive map (FCM) learning | MMMA-FCM [169] | ||
Symbolic regression problem (SRP) | MFGP [61] | ||
Multi-classification problem | mXOF [138], EMC-GEP [172] | ||
Binary classification problem | MFGP [59] | ||
Automatic hyperparameter tuning of machine learning models | TEMO-MPS [109] | ||
Fuzzy system optimization problem | MTGFS [72] | ||
Association mining problem | MFEA [76] | ||
Classification problem | DMSPSO [89], PSO-EMT [173], MMT-ELM [174] | ||
Manufucturing industry | Composites manufacturing technique | M-BLEA [37], MO-MFEA [38], MT-CPSO [88], CPSOM [92], TEMO-MPS [109] | |
Pressure vessel design problem (PVDP) | MT-CPSO [88] | ||
Parameter extraction of photovoltaic model | SGDE [102] | ||
Minimum energy cost aggregation tree (MECAT) problem | ESMFA [67] | ||
Hyperspectral unmixing | MTSR [175], MTES [176] | ||
Spread spectrum radar polyphase code design (SSRPCD) problem | MFMP [83] | ||
Industrial engineering | Operational indices optimization of beneficiation (OIOB) | ATMO-MFEA [119] | |
Continuous annealing production process (CAPL) | AdaMOMFDE [23], MFEA/D-DRA [128] | ||
Inter-domain path computation under domain uniqueness constraint (IDPC-DU) | MFEA [71] | ||
Optimal power flow (OPF) problem | MFEA [177] | ||
Electric power dispatch problem | MO-MFO [178] | ||
Well location optimization problem | AT-MFEA [116] | ||
Operation optimization of integrated energy system | MO-MFEA-II [121] | ||
Car structure design optimization problem | Multifactorial PSO-FA hybrid algorithm [91], TS+FM [95] | ||
Robotic | Mobile robot path planning | IMFEA [44], MFEA-IG [107,108] | |
Unmanned aerial vehicle (UAV) path planning problem | MFEA [11], MO-MFEA-II [79] | ||
Software engineering | Search-based software test data generation (SBSTDG) | MT-EC [139] | |
Cloud computing service composition (CCSC) problem | PMFEA [74], CCSC-EMA [179] | ||
Medicine | HIV-1 protease cleavage site prediction | None [180] | |
Cybernetics | Double-pole balancing problem | MFEA-II [41], ASCMFDE [42], AMTEA [43] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xu, Q.; Wang, N.; Wang, L.; Li, W.; Sun, Q. Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review. Mathematics 2021, 9, 864. https://doi.org/10.3390/math9080864
Xu Q, Wang N, Wang L, Li W, Sun Q. Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review. Mathematics. 2021; 9(8):864. https://doi.org/10.3390/math9080864
Chicago/Turabian StyleXu, Qingzheng, Na Wang, Lei Wang, Wei Li, and Qian Sun. 2021. "Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review" Mathematics 9, no. 8: 864. https://doi.org/10.3390/math9080864