Soft Computing

, Volume 21, Issue 17, pp 5081–5090 | Cite as

Opposition-based particle swarm optimization with adaptive mutation strategy

  • Wenyong Dong
  • Lanlan KangEmail author
  • Wensheng Zhang
Methodologies and Application


To solve the problem of premature convergence in traditional particle swarm optimization (PSO), an opposition-based particle swarm optimization with adaptive mutation strategy (AMOPSO) is proposed in this paper. In all the variants of PSO, the generalized opposition-based PSO (GOPSO), which introduces the generalized opposition-based learning (GOBL), is a prominent one. However, GOPSO may increase probability of being trapped into local optimum. Thus we introduce two complementary strategies to improve the performance of GOPSO: (1) a kind of adaptive mutation selection strategy (AMS) is used to strengthen its exploratory ability, and (2) an adaptive nonlinear inertia weight (ANIW) is introduced to enhance its exploitative ability. The rational principles are as follows: (1) AMS aims to perform local search around the global optimal particle in current population by adaptive disturbed mutation, so it can be beneficial to improve its exploratory ability and accelerate its convergence speed; (2) because it makes the PSO become rigid to keep fixed constant for the inertia weight, ANIW is used to adaptively tune the inertia weight to balance the contradiction between exploration and exploitation during its iteration process. Compared with several opposition-based PSOs on 14 benchmark functions, the experimental results show that the performance of the proposed AMOPSO algorithm is better or competitive to compared algorithms referred in this paper.


Particle swarm optimization Adaptive mutation Generalized opposition-based learning Adaptive nonlinear inertia weight 



This study was funded by the National Natural Science Foundation of China (No. 61170305, No. 61573157, No. 61562025), Natural Science Foundation of Guangdong Province of China (Grant No. 2014A030313454) and the Foundation of science, technology bureau of Liuzhou city of Guangxi Province of China (Grant No. 2014J020401).

Compliance with ethical standards

Conflict of interest

Wenyong Dong declares that he has no conflict of interest. Lanlan Kang declares that she has no conflict of interest. Wensheng Zhang declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. Dehuri S, Roy R, Cho S (2011) An adaptive binary PSO to learn bayesian classifie for prognostic modeling of metabolic syndrome. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation, pp 495–501Google Scholar
  2. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan, pp 39-43Google Scholar
  3. Eiben A, Smith J (2015) From evolutionary computation to the evolution of things. Nature 521(7553):476–482CrossRefGoogle Scholar
  4. Gong C, Wang Z (2012) Proficient optimization calculation in MATLAB. Electronics Industry Press, BeijingGoogle Scholar
  5. Hu M, Wu T, Weir J (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evol Comput 17(5):705–720Google Scholar
  6. Ismail A, Engelbrecht A (2012) Self-adaptive particle swarm optimization. In: Simulated evolution and learning, Springer, pp 228–237Google Scholar
  7. Karafotias G, Hoogendoorn M, Eiben A (2014) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187Google Scholar
  8. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the first IEEE international conference on neural networks, Perth, Australia, vol. 4, 1942C1948 (1995)Google Scholar
  9. Ozcan E, Mohan C (1999) Particle swarm optimization: surfing and waves. In: Proc. congress on evolutionary computation (CEC1999), Washington D.C., pp 1939–1944Google Scholar
  10. Pehlivanoglu Y (2013) A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks. IEEE Trans Evol Comput 17(3):436–452CrossRefGoogle Scholar
  11. Shi Y, Eberhart R (1998a) A modified particle swarm optimizer. In: Evolutionary Computation Proceedings of 1998. The 1998 IEEE international conference on IEEE world congress on computational intelligence, IEEE, pp 69–73. doi: 10.1109/ICEC.1998.699146
  12. Shi Y, Eberhart R (1998b) Parameter selection in particle swarm optimization. In: Evolutionary programming VII: Proc. of the seventh annual conference on evolutionary programming, New York, pp 591–600Google Scholar
  13. Tizhoosh H (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proc. IEEE international conference of intelligent for modeling, control and automation. Inst of Elec. and Elec. Eng. Computer Society, PiscatAWay, pp 695–701Google Scholar
  14. Van den Bergh F, Engelbrecht A (2001) Effect of swarm size on cooperative particle swarm optimizers. In: Genetic and evolutionary computation conference, San Francisco, USA, pp 892–899Google Scholar
  15. Wang H, Wu Z, Rahnamayan S et al (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181(20):4699–4714MathSciNetCrossRefGoogle Scholar
  16. Wang H, Wang W, Wu Z (2013) Particle swarm optimization with adaptive mutation for multimodal optimization. Appl Math Comput 221:296–305MathSciNetzbMATHGoogle Scholar
  17. Wang H, Li H, Liu Y, et al (2007) Opposition-based particle swarm algorithm with Cauchy mutation. In: Proceedings of IEEE congress on evolutionary computation, Tokyo, pp 4750–4756Google Scholar
  18. Wang H, Wu Z, Liu Y, et al (2009) Space transformation search: a new evolutionary technique. In: Proceedings of the first ACM/SIGEVO Summit on genetic and evolution computation, pp 537–544Google Scholar
  19. Zhan Z, Zhang J, Li Y, Chung H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1362–1381Google Scholar
  20. Zhang W, Liu Y, Clerc M (2003) An adaptive PSO algorithm for reactive power optimization. In: Proceedings of 6th international conference on advances in power system control, operation and management, Nov., 302C307Google Scholar
  21. Zhou X, Wu Z, Wang H et al (2013) Elite opposition-based particle swarm optimization. Acta Electro Sin 41(8):1647–1652Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Computer SchoolWuhan UniversityWuhanPeople’s Republic of China
  2. 2.School of Apply ScienceJiangxi University of Science and TechnologyGanzhouPeople’s Republic of China
  3. 3.State Key Laboratory of Intelligent Control and Management of Complex Systems at Institute of Automation Chinese Academy of SciencesBeijingPeople’s Republic of China

Personalised recommendations