Dominance-based variable analysis for large-scale multi-objective problems
Natural Computing, vol. 22, pp. 243–257
Abstract
Abstract Optimization problems with multiple objectives and many input variables inherit challenges from both large-scale optimization and multi-objective optimization. To solve the problems, decomposition and transformation methods are frequently used. In this study, an improved control variable analysis is proposed based on dominance and diversity in Pareto optimization. Further, the decomposition method is used in a cooperative coevolution framework with orthogonal sampling mutation. The algorithm’s performances are compared against the weighted optimization framework. The results show that the proposed decomposition method has much better accuracy compared to the traditional method. The results also show that the cooperative coevolution framework with a good grouping is very competitive. Additionally, the number of search directions in orthogonal sampling can be easily configured. A small number of search directions will reduce the search space greatly while also restricting the area that can be explored and vice versa.
Authors 4
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TH Köln - University of Applied Sciences · Leiden University
Affiliation as printed
Institute for Data Science, Engineering, and Analytics, TH Köln – University of Applied Sciences, Gummersbach, Germany
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
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TH Köln - University of Applied Sciences
Affiliation as printed
Institute for Data Science, Engineering, and Analytics, TH Köln – University of Applied Sciences, Gummersbach, Germany
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
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Leiden University · University of Jyväskylä
Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
Multiobjective Optimization Group, Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
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