A Particle Swarm Optimization Algorithm based on Orthogonal Design

The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without "communicating" with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the "generating-and-updating" model in the standard PSO, the elitism preservation strategy is applied to determine the possible movements of the candidate particles in the subsequent iterations. Experimental results demonstrate that our algorithm has a better performance compared to existing methods, including five PSO algorithms and three evolutionary algorithms.