Chaotic dynamic weight particle swarm optimization for numerical function optimization

Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, PSO has problems with pr...

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Bibliographic Details
Published in:Knowledge-based systems 2018-01, Vol.139, p.23-40
Main Authors: Chen, Ke, Zhou, Fengyu, Liu, Aling
Format: Article
Language:English
Subjects:
Algorithms
Birds
Chaotic map
Dynamic weight
Fitness
Global optimization
Optimization
Optimization algorithms
Particle swarm optimization
Search process
Searches
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Summary:Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, PSO has problems with premature convergence and easy trapping into local optimum solutions. In order to overcome these deficiencies, a chaotic dynamic weight particle swarm optimization (CDW-PSO) is proposed. In the CDW-PSO algorithm, a chaotic map and dynamic weight are introduced to modify the search process. The dynamic weight is defined as a function of the fitness. The search accuracy and performance of the CDW-PSO algorithm are verified on seventeen well-known classical benchmark functions. The experimental results show that, for almost all functions, the CDW-PSO technique has superior performance compared with other nature-inspired optimizations and well-known PSO variants. Namely, the proposed algorithm of CDW-PSO has better search performance.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.10.011