Journal of System Simulation
Abstract
Abstract: In order to improve the convergence of multi-objective optimization algorithm and the diversity of optimization solution set, and alleviate the flown down of population in target space, a multi-objective optimization algorithm based on multi-attribute elite individual game mechanism is proposed. This paper uses Pareto dominance relationship and multi-index to comprehensively screen elite individuals. The elite individual game mechanism with K-means clustering is integrated with cross and mutation strategy, which effectively improves the convergence and diversity of the algorithm. A detailed convergence analysis of the algorithm is performed to prove the convergence of the algorithm. Eight representative comparison algorithms are compared on the standard test function to solve the actual pump scheduling problem. The convergence and diversity of the algorithm in this paper are better than or equal to other comparison algorithms, verifying the effectiveness of the algorithm and reducing the probability of population flown down in the target space to a certain extent.
Recommended Citation
Wang, Xu; Ji, Weidong; Zhou, Guohui; and Yang, Jiahui
(2023)
"Multi-objective Optimization Algorithm Based on Multi-index Elite Individual Game Mechanism,"
Journal of System Simulation: Vol. 35:
Iss.
3, Article 5.
DOI: 10.16182/j.issn1004731x.joss.21-1148
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss3/5
First Page
494
Revised Date
2022-02-08
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-1148
Last Page
514
CLC
TP301.6
Recommended Citation
Xu Wang, Weidong Ji, Guohui Zhou, Jiahui Yang. Multi-objective Optimization Algorithm Based on Multi-index Elite Individual Game Mechanism[J]. Journal of System Simulation, 2023, 35(3): 494-514.
DOI
10.16182/j.issn1004731x.joss.21-1148
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