Abstract
Spider monkey optimization (SMO) algorithm is a recently developed optimizer that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. During the solution search process in SMO, perturbation rate plays very important role. The convergence rate of SMO is highly affected by it. Usually, perturbation rate is defined by a simple function that is linearly in nature. But some application has nonlinear nature, thus a nonlinear function may improve the outcomes of SMO. For that reason, a non linear function, namely sigmoidal function used to decide perturbation in SMO and proposed strategy named as sigmoidal SMO. The investigational outcomes show the superiority of the anticipated technique over other meta-heuristics.
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Sharma, B., Sharma, V.K., Kumar, S. (2020). Sigmoidal Spider Monkey Optimization Algorithm. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_10
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DOI: https://doi.org/10.1007/978-981-15-0751-9_10
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