Swarm intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions
Information Sciences: an International Journal
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
In this paper, we present a novel particle swarm optimization (PSO) variant named scatter learning PSO algorithm (SLPSOA) for solving multimodal problems. SLPSOA takes full account of the distribution information of exemplars while following the basic framework of PSO. It constructs an exemplar pool (EP) which is composed of a certain number of relatively high-quality solutions scattering in the solution space, and allows each particle to select a solution from EP as the exemplar using the roulette wheel rule, with the aim of leading the particles to promising solution regions. In addition, SLPSOA employs Solis and Wets? algorithm as a local searcher to enhance its fine search ability in the newfound solution regions. SLPSOA was tested on 16 benchmark functions, and compared with five existing typical PSO algorithms. Computational results demonstrate that it can manage to prevent premature convergence and produce competitive solutions.