Experimental Parameter Investigations on Particle Swarm Optimization Acceleration Coefficients
Jian Zhang · International Journal of Advancements in Computing Technology · 2012
Particle swarm optimization (PSO) is one of the most successful optimization techniques of swarm intelligence and has been fast developed in recent years. However, the performance of PSO is significantly depended on the acceleration coefficients c1 and c2 which control the exploration and convergence abilities. Parameters c1 and c2 are the “self-cognitive” coefficient and “social-influence” coefficient respectively and are both set to 2.0 in traditional studies. Even though some studies have been conducted and argued that the c1 and c2 are unnecessary to be 2.0 for good performance, few literatures that based on the experimental study of the two parameters can be found. This paper gives a comprehensive investigation on the acceleration coefficients c1 and c2 through a set of 13 unimodal and multimodal benchmark functions, in order to study how to set these two parameters for different functions in order to obtain better performance. The experimental results indicate a conclusion that the sum of c1 and c2 should be clamped in the interval of [3.5, 4.5]. This conclusion would be the guidelines and rule for adapting c1 and c2 during the running phases of PSO.