A Cluster-Based Differential Evolution Algorithm With External Archive for Optimization in Dynamic Environments
| Autor: | Swagatam Das, Udit Halder, Dipankar Maity |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Mathematical optimization
Meta-optimization Optimization problem Fitness landscape Computer science Evolutionary algorithm Evolutionary computation Animals Cluster Analysis Humans Computer Simulation Electrical and Electronic Engineering Global optimization Ecosystem Models Statistical Models Genetic IEEE Congress on Evolutionary Computation Imperialist competitive algorithm Biological Evolution Computer Science Applications Human-Computer Interaction Dynamic programming Genetics Population Control and Systems Engineering Differential evolution Mutation Evolution strategy Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 43:881-897 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tsmcb.2012.2217491 |
| Popis: | This paper presents a Cluster-based Dynamic Differential Evolution with external Ar chive (CDDE_Ar) for global optimization in dynamic fitness landscape. The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions. The clusters are evolved separately using a standard differential evolution algorithm. The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations. Accordingly, the total population is redistributed into a new number of clusters. In this way, a certain sharing of information occurs periodically during the optimization process. The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation. Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers. |
| Databáze: | OpenAIRE |
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