Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptive Polyclonal Programming Algorithm with Applications
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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Interest in multimodal function optimization problems is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this paper, a new niching strategy, deterministic replacement policy, is put forward in the proposed algorithm named Niching Clonal Selection Algorithm (NCSA). In order to improve the algorithm's performance, it advances a new selection method- meme selection, based on the knowledge of meme. The numerical experiment on four typical multimodal function optimization problems attests to the proposed algorithm's validity. Finally, the study compares NCSA with some niching evolution algorithms. From the experimental results, we can see that the proposed algorithm is superior to them on all of the tested functions.