A Novel Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for Binary CSPs

Abstract

Heuristic optimization is an efficient approach and robust. A novel hybrid algorithm DE-PSO is proposed in this paper, which combines differential evolution(DE) with the particle swarm optimization(PSO) algorithm. In order to balance of an individual's exploration and exploitation capability for different evolving phase, F and CR equal to two different self-adjusted nonlinear functions. DE adjusts the mutation rate F and the crossover rate CR adaptively, taking account of the different distribution of population. Updating particle not only by DE operators but also by mechanisms of PSO. DE-PSO maintains the diversity of population and improves the global convergence ability. It also improves the efficiency and success rate and avoids the premature convergence. Simulation and comparisons based on test-sets of CSPs demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.