Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Journal of Global Optimization
Particle Swarn Optimization with Fast Local Search for the Blind Traveling Salesman Problem
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
A novel population initialization method for accelerating evolutionary algorithms
Computers & Mathematics with Applications
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
Opposition versus randomness in soft computing techniques
Applied Soft Computing
Self-adaptive velocity particle swarm optimization for solving constrained optimization problems
Journal of Global Optimization
Opposition based initialization in particle swarm optimization (O-PSO)
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hardware opposition-based PSO applied to mobile robot controllers
Engineering Applications of Artificial Intelligence
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization phase, an opposition learning strategy is performed to improve the search efficiency. Then a variant of the adaptive velocity based on the differential operator enhances the optimization ability of the particles. Finally, a re-initialization strategy based on two diversity measures for the swarm is act in order to avoid premature convergence and stagnation. The strategy uses the super-opposition paradigm to re-initialize particles in the swarm. The algorithm has been evaluated on a set of 100 global optimization test problems. Comparisons with other global optimization methods show the robustness and effectiveness of the proposed algorithm.