Particle swarm optimization with opposition-based disturbance
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Particle Swarm Optimization (PSO) has shown its fast search speed in many optimization and search problems. However, PSO easily fall into local optima on some multimodal and complicated problems. In this paper, an enhanced opposition-based PSO, called EOPSO, is proposed by combing an enhanced opposition-based learning and the standard PSO. The enhanced opposition provides solutions more closely to the global optimum than the traditional opposite solutions. Experimental studies on 4 unimodal functions and 4 multimodal functions show that the EOPSO does not only surpass the standard PSO and opposition-based PSO on all test functions, but also shows faster convergence rate.