Computational intelligence PC tools
Computational intelligence PC tools
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
The particle swarm: social adaptation in information-processing systems
New ideas in optimization
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
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
Mathematics and Computers in Simulation
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
Expert Systems with Applications: An International Journal
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
Advances in Engineering Software
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
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This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.