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Distributed learning particle swarm optimizer for global optimization of multimodal problems

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Abstract

Particle swarm optimizer (PSO) is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimumwhen solving complex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer (DLPSO) to solve multimodal nonseparable problems. The strategy for DLPSO is to extract good vector information from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimental studies on a set of test functions show that DLPSO exhibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.

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Acknowledgements

The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (Grant Nos. 51575544, 51275353), Macao Science and Technology Development Fund (108/2012/A3 and 110/2013/A3) and Research Committee of University of Macau (MYRG2015-00194-FST, MYRG203(Y1-L4)-FST11-LYM).

Author information

Authors and Affiliations

  1. Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macao, China

    Geng Zhang

  2. Industrial and Systems Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Yangmin Li

  3. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yuhui Shi

Authors
  1. Geng Zhang
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  2. Yangmin Li
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  3. Yuhui Shi
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Corresponding author

Correspondence to Yangmin Li.

Additional information

Geng Zhang received the BS and MS degrees in electrical automation from Lanzhou University of Technology, China in 2004 and 2007, respectively, and the MS degree in electrical and electronic engineering from the University of Hong Kong, China in 2009. He is currently working toward the PhD degree at the University of Macau, China. His current research interests include particle swarm optimization, memetic algorithm and neural network.

Yangmin Li received the BS and MS degrees from Jilin University, China in 1985 and 1988, respectively, and the PhD degree from Tianjin University, China in 1994. He was a posdoctoral research associate at Purdue University, USA in 1997. He is currently a professor and director of the Mechatronics Laboratory at Dept. Electromech. Eng. at the University of Macau, China. His research interests include nanorobotics, micromanipulator, mobile and parallel robot, computational intelligence and control. He has authored over 360 papers in above areas. Prof. Li is an IEEE Senior member, ASME and CSME member. He is an editor of the Chinese Journal of Mechanical Engineering, an associate editor of the IEEE Transactions on Automation Science Engineering, Mechatronics, International Journal of Control, Automation and Systems, and IEEE Access.

Yuhui Shi is a professor in the Department of Electrical and Electronic Engineering at the Xi’an Jiaotong-Liverpool University (XJTLU), China since 2008. Dr. Shi was with the Electronic Data Systems Corporation (EDS), Indiana, USA from 1998 to 2007. He is an IEEE Fellow, the Editor-in-Chief of the International Journal of Swarm Intelligence Research, and an associate editor of the IEEE Transactions on Evolutionary Computation. Dr. Shi co-authored a book on Swarm Intelligence together with Dr. James Kennedy and Dr. Russell C. Eberhart, and another book on Computational Intelligence: Concept to Implementation together with Dr. Russell C. Eberhart.

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Zhang, G., Li, Y. & Shi, Y. Distributed learning particle swarm optimizer for global optimization of multimodal problems. Front. Comput. Sci. 12, 122–134 (2018). https://doi.org/10.1007/s11704-016-5373-1

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  • DOI: https://doi.org/10.1007/s11704-016-5373-1

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