An optimizer using swarm of chaotic dynamical particles

Abstract

A novel optimization method called, “optimizer using swarm of chaotic dynamical particles,” (OSCDP) is proposed. The proposed method searches for an optimal solution on the basis of particles governed by chaotic dynamics. The chaotic dynamics make the particles behave in a complex way, by stretching and holding mechanisms, even though the dynamics do not contain any stochastic terms. The particles share information of their best position that is a candidate of the optimal solution, as with particle swarm optimization (PSO). The complex behavior and the sharing information mechanism enable the proposed method to search for the optimal solution. This method consists of only three system parameters, making it suitable for implementing the algorithm. We compared the proposed method with other deterministic PSOs and the standard PSO, and found that the proposed method exhibited a better searching performance.