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• W2914857512 abstract "Particle Swarm Optimization (PSO) is a powerful nature-inspired metaheuristic optimization method. Compared to other methods, PSO can determine the optimal solution in fewer evaluations and generally performs more efficiently and effectively. However, researches show that the PSO method suffers from premature convergence and a dependence on the initial control settings. Due to these shortcomings, the application of PSO may lead to failure in obtaining the global optimal solution. In this work, modifications were performed on the original PSO algorithm to adapt the control parameters to the circumstances of the particles at a specific moment. The proposed method is known as the Unique Adaptive Particle Swarm Optimization (UAPSO). In the developed approach, constraints were handled by forcing the particles to learn from their feasible solutions only. Therefore, the constraint handling technique worked in accord with the adapting scheme to ensure that the particles were adapting to the environment by directing itself to the feasible regions. The performance of UAPSO was verified by a comparative study involving eight benchmark constrained optimization problems and a real-world design problem. The numerical results showed the superiority of UAPSO compared to the selected state-of-the-art metaheuristic methods and PSO variants, its ability in avoiding premature convergence and its consistency and efficiency. • Developed a novel mechanism to self-adapt the Particle Swarm Optimization method. • Proposed approach adapts the control parameters of particles and handles constraints. • Solved a set of optimization problems to study performance and convergence. • Provided a design study to demonstrate the effectiveness and efficiency of the developed approach. • Displayed the superiority of the developed mechanism by comparing with some metaheuristic methods." @default.
• W2914857512 created "2019-02-21" @default.
• W2914857512 creator A5055162857 @default.
• W2914857512 creator A5082741189 @default.
• W2914857512 date "2019-10-01" @default.
• W2914857512 modified "2023-10-06" @default.
• W2914857512 title "Self-adapting control parameters in particle swarm optimization" @default.
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• W2914857512 doi "https://doi.org/10.1016/j.asoc.2019.105653" @default.
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