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NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations

Authors Enrico Rigoni, Silvia Poles



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DagSemProc.04461.15.pdf
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Enrico Rigoni
Silvia Poles

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Enrico Rigoni and Silvia Poles. NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2005)
https://doi.org/10.4230/DagSemProc.04461.15

Abstract

The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradient-based NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBI´s convergence. In order to overcome this problem, a hybridization technique coupled with a partitioning method is proposed.
Keywords
  • Genetic Algorithms
  • Normal-Boundary Intersection
  • Designs optimizations

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