Research on improved QPSO algorithm based on cooperative evolution with two populations

Authors:
Longhan Cao;Shentao Wang;Xiaoli Liu;Rui Dai;Mingliang Wu
Affiliations:
Key Laboratory of Control Engineering, Chongqing Institute of Communication, Chongqing and Key Laboratory of Manufacture and Test Techniques for Automobile Parts, Chongqing University of Technolog ...;Key Laboratory of Control Engineering, Chongqing Institute of Communication, Chongqing;Key Laboratory of Control Engineering, Chongqing Institute of Communication, Chongqing;Key Laboratory of Control Engineering, Chongqing Institute of Communication, Chongqing;Key Laboratory of Control Engineering, Chongqing Institute of Communication, Chongqing
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Year:
2010

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Abstract

This paper presents a Cooperative Evolutionary Quantum-behaved Particle Swarm Optimization (CEQPSO) algorithm with two populations to tackle the shortcomings of the original QPSO algorithm on premature convergence and easily trapping into local extremum. In the proposed CEQPSO algorithm, the QPSO algorithm is used to update individual and global extremum in each population; the operations of absorbing and cooperation are used to exchange and share information between the two populations. The absorbing strategy makes the worse population attracted by the other population with a certain probability, and the cooperation strategy makes the two populations mutually exchange their respective best information. Moreover, when the two populations are trapped into the same optimum value, Cauchy mutation operator is adopted in one population. Four benchmark functions are used to test the performance of the CEQPSO algorithm at a fixed iteration, and the simulation results showed that the proposed algorithm in this paper had a better optimization performance and faster convergence rate than PSO and QPSO algorithms.