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Self-adapting hybrid strategy particle swarm optimization algorithm

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Abstract

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

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Acknowledgments

The authors would like to thank Natural Science Foundation of Liaoning Province, China under Contract No. 2014025006; Education Department General Project of Liaoning Province, China under Contract No. L2014209; Fundamental Research Funds for the Central Universities under Contract No. 3132015028 for financially supporting this research.

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Authors and Affiliations

  1. Marine Engineering College, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Chuan Wang, Yancheng Liu, Yang Chen & Yi Wei

Authors
  1. Chuan Wang
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  2. Yancheng Liu
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  3. Yang Chen
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  4. Yi Wei
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Corresponding author

Correspondence to Yancheng Liu.

Additional information

Communicated by V. Loia.

Appendices

Appendix A

See Table 10.

Table 10 Transmission line data for IEEE 30-bus power system (100 MVA base)
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Appendix B

See Table 11.

Table 11 Initialization of active power, reactive power and voltage for each bus in IEEE 30-bus power system (p.u.) (100 MVA)
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Appendix C

See Table 12.

Table 12 Limits of variables of IEEE 30-bus power system (p.u.) (100 MVA)
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Cite this article

Wang, C., Liu, Y., Chen, Y. et al. Self-adapting hybrid strategy particle swarm optimization algorithm. Soft Comput 20, 4933–4963 (2016). https://doi.org/10.1007/s00500-015-1784-4

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