Adnan Ashraf1, Abdulwahab Ali Almazroi2, Waqas Haider Bangyal3,*, Mohammed A. Alqarni4
Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 191-206, 2022, DOI:10.32604/iasc.2022.015810
Abstract Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the… More >
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