Abstract
Spider monkey optimization algorithm (SMOA) is one of the powerful techniques in the arena of swarm intelligence (SI)-based strategies. This article proposes a modified variant of SMOA that is based on an exponential adaptive strategy for step size. During the search of the optimal solution, this exponential strategy is used to adjust the step size so that it can speed up the convergence ability of the swarm. The proposed algorithm is termed as exponential adaptive spider monkey optimization (EASMO) algorithm. This evinced algorithm is tested over 14 standard optimization problems to examine its authenticity. Further, the obtained results are compared with the artificial bee colony (ABC), differential evolution (DE), Gbest-guided artificial bee colony (GABC), particle swarm optimization (PSO), SMOA, and three of its momentous variants, namely levy flight SMOA (LFSMOA), modified limacon SMOA (MLSMOA), and power law-based local search in SMOA (PLSMOA). The analysis of the results proved the competence of EASMO in the field of SI-based strategies.
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Sharma, A., Sharma, N., Sharma, H., Chand Bansal, J. (2020). Exponential Adaptive Strategy in Spider Monkey Optimization Algorithm. In: Nagar, A., Deep, K., Bansal, J., Das, K. (eds) Soft Computing for Problem Solving 2019 . Advances in Intelligent Systems and Computing, vol 1139. Springer, Singapore. https://doi.org/10.1007/978-981-15-3287-0_1
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DOI: https://doi.org/10.1007/978-981-15-3287-0_1
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