PROJECT TITLE :
An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
Evolutionary algorithms are shown to be powerful for solving multiobjective optimization issues, in that nondominated sorting could be a widely adopted technique in selection. This technique, but, can be computationally expensive, especially when the number of individuals in the population becomes giant. This can be mainly because in most existing nondominated sorting algorithms, a resolution wants to be compared with all alternative solutions before it will be assigned to a front. During this paper we tend to propose a completely unique, computationally efficient approach to nondominated sorting, termed efficient nondominated type (ENS). In ENS, a solution to be assigned to a front needs to be compared only with those who have already been assigned to a front, thereby avoiding many unnecessary dominance comparisons. Based mostly on this new approach, 2 nondominated sorting algorithms are prompt. Both theoretical analysis and empirical results show that the ENS-based mostly sorting algorithms are computationally more economical than the state-of-the-art nondominated sorting methods.
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