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A competitive clustering particle swarm optimizer for dynamic optimization problems

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Abstract

Optimization in dynamic optimization problems (DOPs) requires the optimization algorithms not only to locate, but also to continuously track the moving optima. Particle swarm optimization (PSO) is a population-based optimization algorithm, originally developed for static problems. Recently, several researchers have proposed variants of PSO for optimization in DOPs. This paper presents a novel multi-swarm PSO algorithm, namely competitive clustering PSO (CCPSO), designed specially for DOPs. Employing a multi-stage clustering procedure, CCPSO splits the particles of the main swarm over a number of sub-swarms based on the particles positions and on their objective function values. The algorithm automatically adjusts the number of sub-swarms and the corresponding region of each sub-swarm. In addition to the sub-swarms, there is also a group of free particles that explore the environment to locate new emerging optima or exploit the current optima which are not followed by any sub-swarm. The adaptive search strategy adopted by the sub-swarms improves both the exploitation and tracking characteristics of CCPSO. A set of experiments is conducted to study the behavior of the proposed algorithm in different DOPs and to provide guidelines for setting the algorithm’s parameters in different problems. The results of CCPSO on a variety of moving peaks benchmark (MPB) functions are compared with those of several state-of-the-art PSO algorithms, indicating the efficiency of the proposed model.

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Notes

  1. In this paper, we consider, without loss of generality, maximization problems.

  2. Here, each objective function evaluation is regarded as a time step.

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Acknowledgements

The authors would like to thank the anonymous referees and the editor-in-chief of the journal for their valuable and constructive comments which significantly improved the quality of the paper.

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Correspondence to Mohammad Mehdi Ebadzadeh.

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Nickabadi, A., Ebadzadeh, M.M. & Safabakhsh, R. A competitive clustering particle swarm optimizer for dynamic optimization problems. Swarm Intell 6, 177–206 (2012). https://doi.org/10.1007/s11721-012-0069-0

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