Particle Swarm Optimization with Individual Decision

As a swam intelligent technique, particle swam optimization (PSO) simulates the animal collective behaviors. Since each individual manipulates different experience due to the different living environment, each particle may produce a personal moving direction when making an individual decision at each iteration. However, this decision mechanism is not considered by the standard version of PSO. Therefore, in this paper, a new variant of PSO is introduced by incorporating with individual decision mechanism. In this new version, each particle is moved to the experience position decided by its nor the personal historical best position. Simulation results show that its performance is superior to other two variants.