A Modified Particle Swarm Optimization and Simulation
Li Yong, Liao Ruiquan, Zhang Ding-xue · 2009
To overcome premature searching by standard particle swarm optimization (PSO) algorithm, a new modified PSO with information of the closest particle is proposed. In the algorithm, the particle is updated not only by the best previous position and the best position among all the particles in the swarm, but also by the best previous position of the closest particle. To balance the trade-off between exploration and exploitation and convergence to the global optimum solution, a linearly varying acceleration coefficient over the generations was introduced. The simulation results show that the algorithm has better probability of finding global optimum and mean best value than others algorithm, especially for multimodal function.