Journal Article10.1109/TEVC.2004.831456
Dynamic multiobjective optimization problems: test cases, approximations, and applications
TL;DR: 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.
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Abstract: 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.
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References
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Kalyanmoy Deb,Deb Kalyanmoy +1 more
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TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
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Nonlinear Multiobjective Optimization
Kaisa Miettinen
- 26 Sep 2011
TL;DR: This paper is concerned with the development of methods for dealing with the role of symbols in the interpretation of semantics.
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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
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Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications
Eckart Zitzler
- 27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
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Scalable Test Problems for Evolutionary Multiobjective Optimization
Kalyanmoy Deb,Lothar Thiele,Marco Laumanns,Eckart Zitzler +3 more
- 01 Jan 2005
TL;DR: In this paper, the authors have suggested three different approaches for systematically designing test problems for multi-objective evolutionary algorithms (MOEAs) with more than two objectives, which can be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing dierent MOEA, and better understanding of the working principles of MOEA.