Soft Computing

, Volume 23, Issue 14, pp 5571–5582 | Cite as

Empirical study of particle swarm optimization inspired by Lotka–Volterra model in Ecology

  • Xianxiang WuEmail author
  • Muye Sun
  • Xiao Chen
  • Juan Wang
  • Baolong Guo
Methodologies and Application


Particle swarm optimization (PSO) has been proved to be an effective technique in solving complex global optimization problems. Many modified versions of the original PSO algorithm emerged during the last 15 years. Many of those existing methods employ all particles in a single population which adopts the similar monotonic strategy. The loss of diversity resulted in the premature convergence problem. In this paper, we proposed a suite of multi-swarm Lotka–Volterra model inspired particle swarm optimization algorithms (MSLVPSO) to address the premature convergence problem. The intraspecific and interspecific cooperation and competition strategy of the proposed model dramatically increased diversity of particles. As a result, it makes the particles more likely to break away from the local optimum. In addition, we derived the method to set parameters and developed several cooperative–competitive schemes. We evaluated the proposed MSLVPSO algorithms using a variety of benchmark functions. We also compared our proposed method with typical single-swarm PSO algorithms. Our experimental results show that the proposed MSLVPSO optimizers outperform other state-of-the-art algorithms.


Particle swarm optimization (PSO) Premature convergence problem Diversity loss Lotka–Volterra model Cooperative–competitive coevolution 



This research was supported in part by National Natural Science Foundation of China under Grants 61671356, 61571346, 61601352, 61704127 and 61105066, in part by Scientific Research Program Funded by Shaanxi Provincial Education Department under Grant 17JK0989, in part by Fundamental Research Funds for the Central Universities under Grant JB141305. In addition, we are grateful to the anonymous reviewers and editors for their valuable suggestions and comments on the initial version of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this article.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Intelligent Control and Image Engineering, School of Aerospace Science and TechnologyXidian UniversityXi’anPeople’s Republic of China
  2. 2.School of Technology and EngineeringXi’an Fanyi UniversityXi’anPeople’s Republic of China

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