A particle representation based on considering adaptive particle swarm optimization's parameters in search space

PSO is a way of optimizing a function without knowing the function by analyzing social behavior of particles in a swarm. In PSO every particle knows it's neighbor's best and will be move toward it; if the neighbor's best is better than its own best and this effects the velocity vector of a particle. Evaluation and comparison results shows that our algorithm achieves considerably higher average. In this paper, we combine c
₁
, c
₂
, w parameters with the encoding space of a particle. By doing this personal best of each particle and finally the global best of whole swarm will improve remarkably.