Hybridizing Particle Swarm Optimization with Signal-to-Noise Ratio for numerical optimization

This paper hybridized the Particle Swarm Optimization (PSO) with Signal-to-Noise Ratio (SNR) to solve the numerical optimization problems. PSO has the ability of both global and local searches, where improper parameter settings could cause the algorithm to converge at the local optimum. SNR, on the other hand, has the ability to evaluate “existence possibility of optimal value”. Integration of PSO and SNR thus becomes more robust, statistically sound and efficient than PSO. In this paper, fifteen standard test functions (benchmark problems) with a large number of local optimal solutions and high dimension (30 or 100 dimension) are used for examples and solved by the proposed algorithm. The results show that the proposed algorithm by this study can effectively obtain the global optimal solutions or close-to-optimal solutions.