A Newly Cooperative PSO – Multiple Particle Swarm Optimizers with Diversive Curiosity, MPSOα/DC

  • Hong ZhangEmail author
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 70)


In this paper we propose a newly multiple particle swarm optimizers with diversive curiosity (MPSOα/DC) for enhancing the search performance. It has three outstanding features: (1) Implementing plural particle swarms in parallel to explore; (2) Finding the most suitable solution in a small limited space by a localized random search for correcting the solution found by each particle swarm; (3) Introducing diversive curiosity into the multi-swarm to alleviate stagnation. To demonstrate the proposal’s effectiveness, computer experiments on a suite of benchmark problems are carried out. We investigate its intrinsic characteristics, and compare the search performance with other methods. The obtained results show that the search performance of the MPSOα/DC is superior to that by the PSO/DC, EPSO, OPSO, and RGA/E for the given benchmark problems.


Cooperative particle swarm optimization Hybrid computation Localized random search Exploitation and exploration Diversive and specific curiosity Swarm intelligence 



This research was partially supported by Grant-in-Aid Scientific Research(C) (22500132) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.


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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Brain Science and Engineering, Graduate School of Life Science & Systems EngineeringKyushu Institute of TechnologyWakamatsu, KitakyushuJapan

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