Topic
Multi-objective optimization
About: Multi-objective optimization is a research topic. Over the lifetime, 28329 publications have been published within this topic receiving 743850 citations. The topic is also known as: multi-objective programming & vector optimization.
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TL;DR: An overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives that differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.
2,943 citations
Proceedings Article•
01 Jun 1993
TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.
Abstract: The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-off surface.
2,788 citations
27 Jun 1994
TL;DR: The Niched Pareto GA is introduced as an algorithm for finding the Pare to optimal set and its ability to find and maintain a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems is demonstrated.
Abstract: Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoff surface. We introduce the Niched Pareto GA as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems. >
2,566 citations
Book•
18 May 2005
TL;DR: This paper presents a meta-modelling framework for estimating the modeled solutions for various types of optimization problems in the multicriteria setting.
Abstract: Multicriteria Optimization and Decision Analysis, 2017 ... Multicriteria Optimization | Request PDF Multicriteria Optimization Solving Multicriteria Optimization Problems with WebOptim ... Multicriteria Optimization | Matthias Ehrgott | Springer Multicriteria Optimization | Matthias Ehrgott | Springer Multicriteria VMAT optimization PubMed Central (PMC) Multicriteria Optimization | Guide books Multiple-criteria decision analysis Wikipedia Multicriteria optimization: Sitespecific class solutions ... Multicriteria Optimization | SpringerLink Multicriteria Optimization Harvard University Multicriteria Optimization Matthias Ehrgott Google Books Multicriteria optimization in humanitarian aid ScienceDirect Multicriteria Optimization and
2,422 citations
27 Sep 1998
TL;DR: In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.
Abstract: Since 1985 various evolutionary approaches to multiobjective optimization have been developed, capable of searching for multiple solutions concurrently in a single run. But the few comparative studies of different methods available to date are mostly qualitative and restricted to two approaches. In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.
2,401 citations