Stochastic Dominant Cognitive Experience Guided Particle Swarm Optimization.

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.