On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Information Sciences: an International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
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Many multi-objective evolutionary algorithms (MOEA) require non-dominated sorting of the population. The process of non-dominated sorting is one of the main time consuming parts of MOEA. The performance of MOEA can be improved by designing efficient non-dominated sorting algorithm. The paper proposes Novel Non-dominated Sorting algorithm (NNS). NNS algorithm uses special arrangement of solutions which in turn helps to reduce total number of comparisons among solutions. Experimental analysis and comparison study show that NNS algorithm improves the process of non-dominated sorting for large population size with increasing number of objectives.