A directed search strategy for evolutionary dynamic multiobjective optimization
A directed search strategy for evolutionary dynamic multiobjective optimization
复制标题
DOI：
10.1007/s00500-014-1477-4
复制
发表时间：
2014-10
期刊：
影响因子：
4.1
通讯作者：
Yan Wu;Yaochu Jin;Xiaoxiong Liu
中科院分区：
文献类型：
--
作者：
Yan Wu;Yaochu Jin;Xiaoxiong Liu
现实世界中的许多多目标优化问题都是动态的，需要一种能够随着时间的推移连续跟踪移动的帕累托前沿的优化算法。在本文中，我们提出了一种由两种机制组成的定向搜索策略(DSS)来提高多目标进化算法在变化环境中的性能。当检测到变化时，第一机制基于预测的移动方向以及与帕累托集合的移动方向垂直的方向重新初始化种群。第二种机制旨在根据连续两代之间非支配解的移动方向，在Pareto集合的预测区域内生成解，以加速收敛。这两种机制相结合，能够很好地平衡进化算法求解动态多目标优化问题的探索和开发。在具有不同变化动态的各种测试实例上，我们将DSS与现有的两种预测策略进行了比较。实验结果表明，决策支持系统是进化算法处理动态多目标优化问题的有力工具。
Many real-world multiobjective optimization problems are dynamic, requiring an optimization algorithm that is able to continuously track the moving Pareto front over time. In this paper, we propose a directed search strategy (DSS) consisting of two mechanisms for improving the performance of multiobjective evolutionary algorithms in changing environments. The first mechanism reinitializes the population based on the predicted moving direction as well as the directions that are orthogonal to the moving direction of the Pareto set, when a change is detected. The second mechanism aims to accelerate the convergence by generating solutions in predicted regions of the Pareto set according to the moving direction of the non-dominated solutions between two consecutive generations. The two mechanisms, when combined together, are able to achieve a good balance between exploration and exploitation for evolutionary algorithms to solve dynamic multiobjective optimization problems. We compare DSS with two existing prediction strategies on a variety of test instances having different changing dynamics. Empirical results show that DSS is powerful for evolutionary algorithms to deal with dynamic multiobjective optimization problems.