Journal of System Simulation ›› 2023, Vol. 35 ›› Issue (3): 494-514.doi: 10.16182/j.issn1004731x.joss.21-1148

• Papers • Previous Articles     Next Articles

Multi-objective Optimization Algorithm Based on Multi-index Elite Individual Game Mechanism

Xu Wang(), Weidong Ji(), Guohui Zhou, Jiahui Yang   

  1. College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
  • Received:2021-11-09 Revised:2022-02-08 Online:2023-03-30 Published:2023-03-22
  • Contact: Weidong Ji E-mail:wx971025@163.com;kingjwd@126.com

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.

Key words: multi-objective optimization, convergence analysis, game mechanism, K-means clustering, elite individual screening

CLC Number: