The Pursuit of Evolutionary Particle Swarm Optimization

Particle swarm optimization inspired with the social behavior in flocks of birds and schools of fish is an adaptive, stochastic and population-based optimization technique which was created by Kennedy and Eberhart in 1995 (9; 12). As one of the representatives of swarm intelligence (20), it has the distinctive characteristics: information exchange, intrinsic memory, and directional search in contrast to genetic algorithms (GAs) (14) and genetic programming (GP) (16). Due to ease of understanding and implementation, good expression and expandability, higher searching ability and solution accuracy, the technique has been successfully applied to different fields of science, technology, engineering, and applications for dealing with various large-scale, high-grade nonlinear, and multimodal optimization problems (22; 23).