NSGA-II and its contemporary EMO algorithms were found to be vulnerable in solving many-objective optimization problems having four or more objectives. It is not surprising that EMO researchers have been concentrating in developing efficient algorithms for many-objective optimization problems. Recently, authors suggested an extension of NSGA-II (NSGA-III) which is based on the supply of a set of reference points and demonstrated its working in three to 15-objective optimization problems. In this paper, NSGA-III’s reference point allocation task is made adaptive so that a better distribution of points can be found. The approach is compared with NSGA-III and a previous adaptive approach on a number of constrained and unconstrained many-objective optimization problems. NSGA-III and its adaptive extension proposed here open up new directions for research and development in the area of solving many-objective optimization problems.
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- An Improved Adaptive Approach for Elitist Nondominated Sorting Genetic Algorithm for Many-Objective Optimization
- Springer Berlin Heidelberg
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