Improved Particle Swarm Optimization Based on Chaotic Cellular Automata

in this paper, a new improved Particle Swarm Optimization (PSO) combined with Chaotic Cellular Automata (CCA) has been proposed. PSO is sensitive to initial conditionsand values like other stochastic search algorithms. In the proposed method, features of chaotic Pseudo Random NumberGenerator (PRNG) are used to move particles in the problem space. This factor leads to the appropriate random behavior of particles in the space that is capable of high exploitationability and also changes in the coefficient inertia w with big steps moving from converging prematurely and fallingin local minimum. In the proposed method, by combining small steps by CCA, that has high exploitation ability and with a large step changes in the coefficient inertia w the highexploration ability leading to balance in the random behavior of the algorithms. Proposed method display good performance for searching the problem space compared with other algorithms.

کلیدواژه ها:

PSO algorithm ، evolutionary algorithms ، chaotic cellular automata ، Pseudo Random Number Generator