引用本文:武燕,石露露,周艳.动态多目标优化:测试函数和算法比较[J].控制与决策,2020,35(10):2372-2380
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动态多目标优化:测试函数和算法比较
武燕1,石露露1, 周艳2
(1. 西安电子科技大学数学与统计学院,西安710126;2. 陆军边海防学院基础部,西安710108)
摘要:
生活中存在大量的动态多目标优化问题,应用进化算法求解动态多目标优化问题受到越来越多的关注,而动态多目标测试函数对算法的评估起着重要的作用.在已有动态多目标测试函数的基础上,设计一组新的动态多目标测试函数.Pareto最优解集和Pareto前沿面的不同变化形式影响着动态多目标测试函数的难易程度,通过引入Pareto最优解集形状的变化,结合已有的Pareto最优解集移动模式,设计一组测试函数集.基于提出的测试函数集,对3个算法进行测试,仿真实验结果表明,所设计的函数给3个算法带来了挑战,并展现出算法的优劣.
关键词:  动态多目标优化  进化算法  测试函数  性能比较
DOI:10.13195/j.kzyjc.2019.0236
分类号:TP18
基金项目:国家自然科学基金项目(61772391).
Dynamic multi-objective optimization: Test function and algorithm comparisons
WU Yan1,SHI Lu-lu1,ZHOU Yan2
(1. School of Mathematics and Statistics,Xidian University,Xián710126,China;2. Department of the Foundation,Army Academy of Border and Coastal Defence,Xián710108,China)
Abstract:
In real world, there are many dynamic multi-objective optimization problems. In the field of evolutionary algorithms, there is a rapid increase in research work dealing with dynamic multi-objective optimization problems. However, dynamic multi-objective test functions play an important role in measuring algorithms performance. This paper proposes a set of new dynamic multi-objective test functions to evaluate the performance of algorithms based on the existing dynamic multi-objective test functions. The difficulty of dynamic multi-objective test functions is affected by shapes of Pareto-optimal solutions and Pareto-optimal front. According to this idea, a set of test functions is designed by introducing the change shape of Pareto-optimal solutions and combining with the existing shift mode of Pareto-optimal solutions. Three algorithms are tested on the proposed test functions. The simulation results show that the designed functions bring challenges to the algorithms and reveal the advantages and disadvantages of the algorithms.
Key words:  dynamic multi-objective optimization  evolutionary algorithm  test function  performance comparison

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