Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization

Cheng-Long Wei and Gai-Ge Wang
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Cheng-Long Wei: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Gai-Ge Wang: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China

Mathematics, 2020, vol. 8, issue 9, 1-23

Abstract: 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.

Keywords: swarm intelligence; simulated annealing; krill herd; particle swarm optimization; quantum (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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