Abstract
Gravitational search algorithm (GSA) is modeled on the theory of Newton’s law of gravity and motion. It is a well-known swarm intelligence (SI)-based heuristic optimization technique. GSA also experienced the problem of slow convergence like other SI-dependent algorithms. Hence, to improve the convergence and exploitation capability, a variant of GSA is introduced. The proposed algorithm is specified as fast convergent gravitational search algorithm (FCGSA). In FCGSA, a sigmoidal function is used that controls the number of agents that employ the force to another agent in the search region, to enhance the convergence speed. Further, in the location modification process of FCGSA, the step size is exponentially computed, leads to improve exploitation capability. The intended algorithm is compared with basic gravitational search algorithm (GSA), spider monkey optimization (SMO), biogeography optimization algorithm (BBO), and one of its recent variant fitness-based gravitational search algorithm (FBGSA) over 15 benchmark functions and the obtained outcomes prove the performance of the introduced algorithm.
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Rawal, P., Sharma, H., Sharma, N. (2020). Fast Convergent Gravitational Search Algorithm. In: Sharma, H., Pundir, A., Yadav, N., Sharma, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0426-6_1
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DOI: https://doi.org/10.1007/978-981-15-0426-6_1
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