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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Hybrid Gravitational Search and Clonal Selection Algorithm for Global Optimization
verfasst von : Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang
Erschienen in: Advances in Swarm Intelligence
Verlag: Springer Berlin Heidelberg
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In recent years, there has been a growing interest in algorithms inspired by the behaviors of natural phenomena. However, the performance of any single pure algorithm is limited by the size and complexity of the problem. To further improve the search effectiveness and solution robustness, hybridization of different algorithms is a promising research direction. In this paper, we propose a hybrid iteration algorithm by combing the gravitational search algorithm with the clonal selection. The gravitational search performs exploration in the search space, while the clonal selection is implemented to carry out exploitation within the neighborhood of the solutio found by gravitational search. The emerged hybrid algorithm, called GSCSA, thus reasonably combines the characteristics of both base algorithms. Experimental results based on several benchmark functions demonstrate the superiority of the proposed algorithm in terms of solution quality and convergence speed.