%0 Journal Article %1 Yazdani20141 %A Yazdani, Sajjad %A Nezamabadi pour, Hossein %A Kamyab, Shima %D 2014 %J Swarm and Evolutionary Computation %K gravitational multimodal optimization search %N 0 %P 1 - 14 %R http://dx.doi.org/10.1016/j.swevo.2013.08.001 %T A gravitational search algorithm for multimodal optimization %U http://www.sciencedirect.com/science/article/pii/S2210650213000503 %V 14 %X Abstract Gravitational search algorithm (GSA) has been recently presented as a new heuristic search algorithm with good results in real-valued and binary encoded optimization problems which is categorized in swarm intelligence optimization techniques. The aim of this article is to show that \GSA\ is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, namely Niche \GSA\ (NGSA) is introduced for multimodal optimization. ŊSA\ extends the idea of partitioning the main population (swarm) of masses into smaller sub-swarms and also preserving them by introducing three strategies: a K -nearest neighbors (K-NN) strategy, an elitism strategy and modification of active gravitational mass formulation. To evaluate the performance of the proposed algorithm several experiments are performed. The results are compared with those of state-of-the-art niching algorithms. The experimental results confirm the efficiency and effectiveness of the ŊSA\ in finding multiple optima on the set of unconstrained and constrained standard benchmark functions.

@article{Yazdani20141, abstract = {Abstract Gravitational search algorithm (GSA) has been recently presented as a new heuristic search algorithm with good results in real-valued and binary encoded optimization problems which is categorized in swarm intelligence optimization techniques. The aim of this article is to show that \{GSA\} is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, namely Niche \{GSA\} (NGSA) is introduced for multimodal optimization. \{NGSA\} extends the idea of partitioning the main population (swarm) of masses into smaller sub-swarms and also preserving them by introducing three strategies: a K -nearest neighbors (K-NN) strategy, an elitism strategy and modification of active gravitational mass formulation. To evaluate the performance of the proposed algorithm several experiments are performed. The results are compared with those of state-of-the-art niching algorithms. The experimental results confirm the efficiency and effectiveness of the \{NGSA\} in finding multiple optima on the set of unconstrained and constrained standard benchmark functions. }, added-at = {2014-09-28T00:38:20.000+0200}, author = {Yazdani, Sajjad and Nezamabadi pour, Hossein and Kamyab, Shima}, biburl = {https://www.bibsonomy.org/bibtex/247581e4a64148df814487baf9c0d733a/marcioweck}, description = {A gravitational search algorithm for multimodal optimization}, doi = {http://dx.doi.org/10.1016/j.swevo.2013.08.001}, interhash = {5209671ec20e6cf67d4a663ee7fc1fd9}, intrahash = {47581e4a64148df814487baf9c0d733a}, issn = {2210-6502}, journal = {Swarm and Evolutionary Computation }, keywords = {gravitational multimodal optimization search}, number = 0, pages = {1 - 14}, timestamp = {2014-09-28T00:38:20.000+0200}, title = {A gravitational search algorithm for multimodal optimization }, url = {http://www.sciencedirect.com/science/article/pii/S2210650213000503}, volume = 14, year = 2014 }