Gao Hongtao, Li Hecheng, Shen Yu
School of Computer Science and Technology, Qinghai Normal University, Xining 810008, China.
School of Mathematics and Statistics, Qinghai Normal University, Xining 810008, China.
Math Biosci Eng. 2024 Feb 5;21(3):3540-3562. doi: 10.3934/mbe.2024156.
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.
动态多目标优化问题因其广泛的应用而受到关注。解决该问题的难点在于动态地移动 Pareto 解集（PS）和 Pareto 前沿（PF）。为了解决这类问题，人们提出了大量有效的策略，旨在加速收敛并保持多样性。预测策略是一种常用的方法，在动态优化环境中得到了广泛应用。然而，如何提高预测效率始终是一个关键且具有挑战性的问题。本文提出了一种基于个体秩和的预测模型，通过定义个体在相邻两个环境中的位置差异来识别当前的变化类型。所提出的预测策略取决于环境变化类型。为了验证所提算法的有效性，将其与五种先进方法在 20 个动态多目标问题的基准实例上进行了比较。实验结果表明，所提算法在动态环境中能够获得良好的收敛性和分布性。
