Solving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach
Solving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach
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DOI：
10.1109/tcyb.2016.2602561
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发表时间：
2017-12
影响因子：
11.8
通讯作者：
Sen Bong Gee;K. Tan;C. Alippi
中科院分区：
文献类型：
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作者：
Sen Bong Gee;K. Tan;C. Alippi
动态环境下的进化多目标优化是一个具有挑战性的任务，因为它要求优化算法收敛到时变的Pareto最优前沿。本文提出了一种动态多目标优化算法，利用逆模型集引导搜索到有希望的决策区域。为了减少用于变化检测目的的适应度评估的数量，提出了一种两阶段的变化检测测试，该测试使用逆模型集来检查目标函数景观中的潜在变化。静态和动态多目标基准优化问题被认为是评估所提出的算法的性能。实验结果表明，优化性能的改善是可以实现的，当采用所提出的逆模型集。
Evolutionary multiobjective optimization in dynamic environments is a challenging task, as it requires the optimization algorithm converging to a time-variant Pareto optimal front. This paper proposes a dynamic multiobjective optimization algorithm which utilizes an inverse model set to guide the search toward promising decision regions. In order to reduce the number of fitness evalutions for change detection purpose, a two-stage change detection test is proposed which uses the inverse model set to check potential changes in the objective function landscape. Both static and dynamic multiobjective benchmark optimization problems have been considered to evaluate the performance of the proposed algorithm. Experimental results show that the improvement in optimization performance is achievable when the proposed inverse model set is adopted.