A dynamic multi-objective evolutionary algorithm using center and multi-direction prediction strategies

Hongtao Gao; Hecheng Li; Yu Shen; Hongtao Gao; Hecheng Li; Yu Shen

doi:10.3934/mbe.2024156

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 3540-3562. doi: 10.3934/mbe.2024156

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A dynamic multi-objective evolutionary algorithm using center and multi-direction prediction strategies

1.
School of Computer Science and Technology, Qinghai Normal University, Xining 810008, China
2.
School of Mathematics and Statistics, Qinghai Normal University, Xining 810008, China

Academic Editor: Yiu-ming Cheung

Received: 15 October 2023 Revised: 05 December 2023 Accepted: 18 January 2024 Published: 05 February 2024

Dynamic multi-objective optimization problems have been popular because of its extensive application. The difficulty of solving the problem focuses on the moving PS as well as PF dynamically. A large number of efficient strategies have been put forward to deal with such problems by speeding up convergence and keeping diversity. Prediction strategy is a common method which is widely used in dynamic optimization environment. However, how to increase the efficiency of prediction is always a key but difficult issue. In this paper, a new prediction model is designed by using the rank sums of individuals, and the position difference of individuals in the previous two adjacent environments is defined to identify the present change type. The proposed prediction strategy depends on environment change types. In order to show the effectiveness of the proposed algorithm, the comparison is carried out with five state-of-the–art approaches on 20 benchmark instances of dynamic multi-objective problems. The experimental results indicate the proposed algorithm can get good convergence and distribution in dynamic environments.
- dynamic multi-objective optimization,
- evolutionary algorithm,
- multi-direction,
- prediction,
- Pareto optimal solutions
Citation: Hongtao Gao, Hecheng Li, Yu Shen. A dynamic multi-objective evolutionary algorithm using center and multi-direction prediction strategies[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 3540-3562. doi: 10.3934/mbe.2024156

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Abstract

Dynamic multi-objective optimization problems have been popular because of its extensive application. The difficulty of solving the problem focuses on the moving PS as well as PF dynamically. A large number of efficient strategies have been put forward to deal with such problems by speeding up convergence and keeping diversity. Prediction strategy is a common method which is widely used in dynamic optimization environment. However, how to increase the efficiency of prediction is always a key but difficult issue. In this paper, a new prediction model is designed by using the rank sums of individuals, and the position difference of individuals in the previous two adjacent environments is defined to identify the present change type. The proposed prediction strategy depends on environment change types. In order to show the effectiveness of the proposed algorithm, the comparison is carried out with five state-of-the–art approaches on 20 benchmark instances of dynamic multi-objective problems. The experimental results indicate the proposed algorithm can get good convergence and distribution in dynamic environments.

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