Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Particle swarm optimization for multimodal functions: a clustering approach
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Fast Multi-Swarm Optimization for Dynamic Optimization Problems
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Cellular PSO: A PSO for Dynamic Environments
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Multi-swarm Particle Swarm Optimizer with Cauchy Mutation for Dynamic Optimization Problems
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
IEEE Transactions on Evolutionary Computation
Hierarchical Particle Swarm Optimization with Ortho-Cyclic Circles
Expert Systems with Applications: An International Journal
Distributed Query Plan Generation using Particle Swarm Optimization
International Journal of Swarm Intelligence Research
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Many real world optimization problems are dynamic in which global optimum and local optimum change over time. Particle swarm optimization has performed well to find and track optimum in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes FCM to adapt exclusion radios and utilize a local search on best swarm to accelerate progress of algorithm and adjust inertia weight adaptively. To improve the search performance, when the search areas of two swarms are overlapped, the worse swarms will be removed. Moreover, in order to track quickly the changes in the environment, all particles in the swarm convert to quantum particles when a change in the environment is detected. Experimental results on different dynamic environments modeled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, for all evaluated environments.