Abstract
Spider Monkey Optimization (SMO) Algorithm being a stretcher to the domain of meta-heuristics is performing well but has a flaw of converging early. For eradicating this flaw and improving exploration capability, a new modification is intended which is named as SMO based on metropolis principle (SMOM). The Metropolis principle is taken from simulated annealing in expectation to improve exploration capability of SMO. In this intended modification, non-prominent solutions also get a chance to upgrade themselves and reach global optima. This amendment enhances the global search capability of global leader phase which helps in sustaining exploration and exploitation of algorithm while maintaining the convergence speed. The intended algorithm is analyzed with SMO, one of its recent variant namely, self-adaptive spider monkey optimization (SaSMO) and another rooted algorithm i.e. particle swarm optimization (PSO) over 12 benchmark functions and recorded outcomes depicts that SMOM is a noted variant among them.
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Hazrati, G., Sharma, H., Sharma, N., Agarwal, V., Tiwari, D.C. (2017). Spider Monkey Optimization Algorithm Based on Metropolis Principle. 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_10
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DOI: https://doi.org/10.1007/978-981-10-3322-3_10
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