Chaotic Simulated Annealing Quantum-Behaved Particle Swarm Optimization Research

Ai-jun Liu (), Hua Li and Ming Dong
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Ai-jun Liu: Xidian University
Hua Li: Xidian University
Ming Dong: Xidian University

A chapter in Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, 2013, pp 1179-1186 from Springer

Abstract: Abstract In order to solve the premature convergence problem of Quantum-behaved Particle Swarm Optimization (QPSO), a Chaotic Simulated Annealing Quantum-behaved Particle Swarm Optimization (SAQPSO) is presented. Particles in population are first initialized using Logistics chaotic mapping, which in return, improve the global convergence performance of algorithm. Simulated annealing algorithm is introduced, with a certain probability of accepting bad solutions, enriches the population diversity, and improves the ability of global optimization. Adaptive temperature decay coefficient is introduced, so the simulated annealing algorithm can automatically adjust the search based on the current environment conditions, so as to improve the search efficiency of the algorithm. Results on Benchmark functions show that the proposed algorithm shows better search and convergence performance than standard QPSO and other algorithms.

Keywords: Chaos optimization; Quantum-behaved Particle Swarm Optimization (QPSO); Simulated annealing algorithm (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-40072-8_117

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DOI: 10.1007/978-3-642-40072-8_117

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