Please use this identifier to cite or link to this item: https://lib.hpu.edu.vn/handle/123456789/34467
Title: A New Optimization Based on Parallelizing Hybrid PSOGSA Algorithm
Authors: Yu, Jie
Wang, Hong Jiang
Pan, Jeng Shyang
Chang, Kuo Chi
Ngô, Trường Giang
Nguyễn, Trọng Thể
Keywords: Parallel PSOGSA algorithm
Mutation strategy
Particle swarm optimization
Gravitational search algorithm
Issue Date: 2021
Publisher: Springer
Abstract: This study suggests a new metaheuristic algorithm for global optimization, based on parallel hybridizing the swarm optimization(PSO) andGravitational search algorithm (GSA). Subgroups of the population are formed by dividing the swarm’s community. Communication between the subsets can be developed by adding strategies for the mutation. Twenty-three benchmark functions are used to test its performance to verify the feasibility of the proposed algorithm. Compared with the PSO, GSA, and parallel PSO (PPSO), the findings of the proposed algorithm reveal that the proposed PPSOGSA achieves higher precision than other competitor algorithms.
URI: https://lib.hpu.edu.vn/handle/123456789/34467
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