A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization
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Faculty of Computers and Artificial Intelligence, Beni-Suef University, 62111 Beni-Suef, Egypt
2
Faculty of Computers and Information, Assiut University, 71516 Assiut, Egypt
3
College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(12), 261; https://doi.org/10.3390/a12120261
Received: 14 October 2019
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Revised: 15 November 2019
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Accepted: 29 November 2019
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Published: 4 December 2019
Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.
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Keywords:
Multi-Objective Optimization; Multi-Objective Problems; Pareto Optimization; Swarm Intelligence; Tabu Search; Whale Optimization Algorithm
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MDPI and ACS Style
AbdelAziz, A.M.; Soliman, T.H.A.; Ghany, K.K.A.; Sewisy, A.A.E.-M. A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization. Algorithms 2019, 12, 261. https://doi.org/10.3390/a12120261
AMA Style
AbdelAziz AM, Soliman THA, Ghany KKA, Sewisy AAE-M. A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization. Algorithms. 2019; 12(12):261. https://doi.org/10.3390/a12120261
Chicago/Turabian StyleAbdelAziz, Amr M., Taysir H.A. Soliman, Kareem K.A. Ghany, and Adel A.E.-M. Sewisy. 2019. "A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization" Algorithms 12, no. 12: 261. https://doi.org/10.3390/a12120261
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