Dynamic multiobjective optimization problems: Test cases, approximations, and applications
Dynamic multiobjective optimization problems: Test cases, approximations, and applications
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DOI：
10.1109/tevc.2004.831456
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发表时间：
2004-10-01
影响因子：
14.3
通讯作者：
Amato, P
中科院分区：
文献类型：
--
作者：
Farina, M;Deb, K;Amato, P
在充分证明了进化多目标优化（EMO）算法在寻找静态多目标优化问题的多个Pareto最优解方面的有用性之后，现在越来越需要以类似的方式解决动态多目标优化问题。在本文中。我们通过开发一些测试问题和建议基线算法来集中解决这个问题。由于在一个动态多目标优化问题中，所得到的帕累托最优集预计会随着时间的推移（或迭代的优化过程）而变化，一套五个测试问题提供不同的模式，这种变化和不同的困难，在跟踪动态帕累托最优前沿的多目标优化算法。此外，一个简单的例子所产生的动态控制回路的动态多目标优化问题。一个扩展到以前提出的基于方向的搜索方法，提出了解决这些问题，并测试建议的测试问题。本文介绍的测试问题，鼓励研究人员有兴趣在多目标优化和动态优化问题，开发更有效的算法在不久的将来。
After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper. we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm. Since in a dynamic multiobjective optimization problem, the resulting Pareto-optimal set is expected to change with time (or, iteration of the optimization process), a suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented. Moreover, a simple example of a dynamic multiobjective optimization problem arising from a dynamic control loop is presented. An extension to a previously proposed direction-based search method is proposed for solving such problems and tested on the proposed test problems. The test problems introduced in this paper should encourage researchers interested in multiobjective optimization and dynamic optimization problems to develop more efficient algorithms in the near future.