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Recent Advances in Particle Swarm Optimization for Large Scale Problems

Danping Yan, Yongzhong Lu

Abstract


Accompanied by the advent of current big data ages, the scales of real world optimization problems with many decisive design variables are becoming much larger. Up to date, how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation. So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics. As a branch of the swarm intelligence based algorithms, particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years. This review paper mainly presents its recent achievements and trends, and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.

Keywords


swarm intelligence; particle swarm optimization; large scale optimization problem; cooperative coevolution; ensemble evolution; static grouping method; dynamic grouping method.

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References


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DOI: https://doi.org/10.32629/jai.v1i1.15

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Copyright (c) 2018 Danping Yan, Yongzhong Lu

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