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Article

Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization

by 1 and 1,2,3,4,*
1
Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
2
Institute of Algorithm and Big Data Analysis, Northeast Normal University, Changchun 130117, China
3
School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
4
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(9), 1403; https://doi.org/10.3390/math8091403
Received: 27 July 2020 / Revised: 15 August 2020 / Accepted: 17 August 2020 / Published: 21 August 2020
(This article belongs to the Special Issue Evolutionary Computation 2020)
The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems. View Full-Text
Keywords: swarm intelligence; simulated annealing; krill herd; particle swarm optimization; quantum swarm intelligence; simulated annealing; krill herd; particle swarm optimization; quantum
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MDPI and ACS Style

Wei, C.-L.; Wang, G.-G. Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization. Mathematics 2020, 8, 1403. https://doi.org/10.3390/math8091403

AMA Style

Wei C-L, Wang G-G. Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization. Mathematics. 2020; 8(9):1403. https://doi.org/10.3390/math8091403

Chicago/Turabian Style

Wei, Cheng-Long, and Gai-Ge Wang. 2020. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization" Mathematics 8, no. 9: 1403. https://doi.org/10.3390/math8091403

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