Evolutionary Multi-objective Optimization in Uncertain Environments

DOI：

10.1007/978-3-540-95976-2

发表时间：

2009-03

期刊：

影响因子：

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中科院分区：

其他

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作者：

C. Goh;K. Tan

许多现实世界的问题涉及到同时优化几个相互竞争的目标和约束，如果没有强大的优化算法的帮助，这些问题很难（如果不是不可能的话）解决。使多目标优化如此具有挑战性的是，在存在冲突规范的情况下，没有一个解决方案对所有目标都是最优的，优化算法必须能够找到许多代表权衡的替代解决方案。然而，多目标只是现实世界应用程序的一个方面。大多数优化问题还具有各种形式的不确定性，这些不确定性源于数据的不完整性和不确定性、环境条件的不确定性以及无法精确实现的解决方案。进化算法是一类随机搜索方法，在解决复杂的多目标问题时非常有效，而传统的优化工具无法很好地发挥作用。进化算法的优势可以归因于它同时采样多个候选解的能力，这是大多数经典多目标优化技术所缺乏的任务。在过去的十年中，这些算法的发展已经做了大量的工作，并且越来越多地应用于生物信息学、逻辑电路设计、控制工程和资源分配等领域。有趣的是，进化多目标优化领域的许多研究人员都假设优化问题是确定性的，而不确定性很少被考虑。虽然多目标进化算法的灵感来自于不确定性是一种普遍现象的自然界，但我们不能想当然地认为这些算法在没有进一步研究的情况下对不确定性具有固有的鲁棒性。这项工作的主要动机是对存在不确定性的多目标优化的多目标进化算法的设计和应用提供一个全面的处理。第1章提供了理解这项工作所需的必要背景信息，涵盖了多目标优化的关键概念和定义

Many real-world problems involve the simultaneous optimization of several competing objectives and constraints that are difficult, if not impossible, to solve without the aid of powerful optimization algorithms. What makes multiobjective optimization so challenging is that, in the presence of conflicting specifications, no one solution is optimal to all objectives and optimization algorithms must be capable of finding a number of alternative solutions representing the tradeoffs. However, multi-objectivity is just one facet of real-world applications. Most optimization problems are also characterized by various forms of uncertainties stemming from factors such as data incompleteness and uncertainties, environmental conditions uncertainties, and solutions that cannot be implemented exactly.Evolutionary algorithms are a class of stochastic search methods that have been found to be very efficient and effective in solving sophisticated multiobjective problems where conventional optimization tools fail to work well. Evolutionary algorithms’ advantage can be attributed to it’s capability of sampling multiple candidate solutions simultaneously, a task that most classical multi-objective optimization techniques are found to be wanting. Much work has been done to the development of these algorithms in the past decade and it is finding increasingly application to the fields of bioinformatics, logical circuit design, control engineering and resource allocation. Interestingly, many researchers in the field of evolutionary multi-objective optimization assume that the optimization problems are deterministic, and uncertainties are rarely examined. While multi-objective evolutionary algorithms draw its inspiration from nature where uncertainty is a common phenomenon, it cannot be taken for granted that these algorithms will hence be inherently robust to uncertainties without any further investigation. The primary motivation of this work is to provide a comprehensive treatment on the design and application of multi-objective evolutionary algorithms for multi-objective optimization in the presence of uncertainties. Chapter 1 provides the necessary background information required to appreciate this work, covering key concepts and definitions of multi-objective optimization

Evolutionary Multi-objective Optimization in Uncertain Environments - Issues and Algorithms

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