Abstract
Genetic algorithm (GA) is adaptive heuristic search evolutionary algorithm. GA has had a great measure of success in the optimization process. Spider monkey optimization (SMO) is the relatively new swarm intelligence algorithm. SMO inspired by food foraging behavior of spider monkeys. We introduce a new idea that integrates swarm intelligence and evolutionary technique into the optimization process. In this article, we propose two hybridization methodologies for SMO and GA, namely SMOGA (SMO followed by GA) and GASMO (GA followed by SMO) for the numerical optimization problems. These algorithms effectiveness have been tested here on both its “ancestors", SMO and GA for various benchmark problems.
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Agrawal, A., Farswan, P., Agrawal, V., Tiwari, D.C., Bansal, J.C. (2017). On the Hybridization of Spider Monkey Optimization and Genetic Algorithms. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_17
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DOI: https://doi.org/10.1007/978-981-10-3322-3_17
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