Particle Swarm Optimization with Group Decision Making

The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses own experience and otherpsilas to make decision, it is easy to trap into premature convergence, but group decision making with all the individuals to make decisions uses various experiences and viewpoints to get better plan for avoiding conformity. A new formal particle swarm optimization is advanced basing on group decision(GDPSO), it takes each particle as an individual decision-maker and uses the basic information of particle such as the position of individual history and fitness value to decide a new position, then using the position replaces the global best position (P_gj). So the space of searching is expanded and the population diversity is increased through the new improved algorithm, it can improve the convergence speed and the capacity of global searching, the premature convergence is avoided to some degree.