A multiple direction search algorithm for continuous optimization

Abstract

The particle swarm optimization algorithm has been successfully applied to various optimization problems. One of its key features is the combination of particle velocity and search direction towards the optimal position in the history and swarm. Recognizing the limitations of the particle swarm optimization algorithm, this paper proposes a new evolutionary algorithm called the multiple direction search algorithm. The algorithm integrates five different search directions, including a multi-point direction constructed using principal component analysis. The integrated direction is generated by the weighted sum of the search directions. Theoretical analysis shows that under mild conditions, the rate of convergence along the weighted direction is no worse than the rate of convergence along the best of single search directions by a positive constant, or even faster in certain cases. The performance of the proposed algorithm was evaluated on three benchmark test suites by computer simulation. Experimental results demonstrate that the proposed method outperforms seven state-of-the-art particle swarm optimization algorithms.