Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part I: A unified formulation
Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part I: A unified formulation
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DOI：
10.1109/3468.650319
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发表时间：
1998-01-01
影响因子：
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通讯作者：
Fleming, PJ
中科院分区：
文献类型：
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作者：
Fonseca, CM;Fleming, PJ
在优化中，多个目标和约束不能独立于底层优化器来处理，诸如成本表面的连续性和可微性之类的要求向决策过程添加了另一个冲突元素，虽然“更好”的解决方案应该被评为高于“更差”的解决方案，但由此产生的成本景观也必须符合这样的要求，进化算法（EA），已经在许多领域中发现了应用，而这些领域不适于通过其它方法进行优化，具有多目标优化器中所期望的许多特性，最显著的是多个候选解的协调处理。然而，EA本质上是无约束的搜索技术，其需要分配质量或适应度的标量度量，对于此类候选解，在回顾了当前多目标和约束优化的进化方法后，本文提出将适应度分配解释为多标准决策过程或至少与多标准决策过程相关，随后根据目标和优先级制定了合适的决策框架。关系运算符，其特征在于，最后，考虑了任意数量的候选人的排序，对于一个简单的问题，用图形说明了偏好变化对EA所看到的成本表面的影响，最后，本文给出了基于所提出的决策策略的多目标遗传算法的公式，生态位形成技术被用来促进优选的候选人之间的多样性，并逐步清晰的偏好被证明是可能的，只要遗传算法可以从成本景观的突然变化中恢复。
In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer, Requirements such as continuity and differentiability of the cost surface add yet another conflicting element to the decision process, While "better" solutions should be rated higher than "worse" ones, the resulting cost landscape must also comply with such requirements, Evolutionary algorithms (EA's), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions, However, EA's are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or fitness, to such candidate solutions, After reviewing current evolutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process, A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, Finally, the ranking of an arbitrary number of candidates is considered, The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem, The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy, Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.