Evaluating Multiobjective Evolutionary Algorithms Using MCDM Methods
Xiaobing Yu,Yiqun Lu,Xianrui Yu +2 more
TL;DR: An experimental study is designed and thirteen benchmark functions are selected to validate the proposed framework, and results have indicated that the framework is effective in evaluating MOEAs.
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Abstract: The evaluation of multiobjective evolutionary algorithms (MOEAs) involves many metrics, it can be considered as a multiple-criteria decision making (MCDM) problem. A framework is proposed to estimate MOEAs, in which six MOEAs, five performance metrics, and two MCDM methods are used. An experimental study is designed and thirteen benchmark functions are selected to validate the proposed framework. The experimental results have indicated that the framework is effective in evaluating MOEAs.
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References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Muiltiobjective optimization using nondominated sorting in genetic algorithms
N. Srinivas,Kalyanmoy Deb +1 more
TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
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SPEA2: Improving the strength pareto evolutionary algorithm
Eckart Zitzler,Marco Laumanns,Lothar Thiele +2 more
- 01 Jan 2001
TL;DR: An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.
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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.
•Proceedings Article
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
J. David Schaffer
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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.
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