Sobia Pervaiz1, Waqas Haider Bangyal2, Adnan Ashraf3, Kashif Nisar4,*, Muhammad Reazul Haque5, Ag. Asri Bin Ag. Ibrahim4, BS Chowdhry6, Waqas Rasheed7, Joel J. P. C. Rodrigues8,9, Richard Etengu5, Danda B. Rawat10
Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1427-1444, 2022, DOI:10.32604/iasc.2022.017304
Abstract In existing meta-heuristic algorithms, population initialization forms a huge part towards problem optimization. These calculations can impact variety and combination to locate a productive ideal arrangement. Especially, for perceiving the significance of variety and intermingling, different specialists have attempted to improve the presentation of meta-heuristic algorithms. Particle Swarm Optimization (PSO) algorithm is a populace-based, shrewd stochastic inquiry strategy that is motivated by the inherent honey bee swarm food search mechanism. Population initialization is an indispensable factor in the PSO algorithm. To improve the variety and combination factors, rather than applying the irregular circulation for the introduction of the populace, semi-arbitrary… More >
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