Nsga-III evolutionary multi-objective optimization
2016-11-08
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Development of multi-objective optimization algorithm using evolutionary structural optimization method and proved its advantages in various practical problems are mainly two and three goals, there is now a growing demand for development of evolutionary multiobjective optimization (EMO) algorithm used to process many goals (with 4 or more goals) optimization problem. In this article, we recognize the efforts of recent years, and discussed a number of possible potential EMO direction algorithm for solving multiobjective optimization problems. Since then, we propose reference points based on a number of target NSGA-II (called NSGA-III), stressed that members of the population, provided by non-dominant and close to a group of reference points.
Development of multi-objective optimization algorithm using evolutionary structural optimization method and proved its advantages in various practical problems are mainly two and three goals, there is now a growing demand for development of evolutionary multiobjective optimization (EMO) algorithm used to process many goals (with 4 or more goals) optimization problem. In this article, we recognize the efforts of recent years, and discussed a number of possible potential EMO direction algorithm for solving multiobjective optimization problems. Since then, we propose reference points based on a number of target NSGA-II (called NSGA-III), stressed that members of the population, provided by non-dominant and close to a group of reference points.
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