An improved secondary ranking for many objective optimization problems

Authors:
Hemant Kumar Singh;Amitay Isaacs;Tapabrata Ray;Warren Smith
Affiliations:
University of New South Wales at Australian Defence Force Academy, Canberra ACT 2600, Australia;University of New South Wales at Australian Defence Force Academy, Canberra ACT 2600, Australia;University of New South Wales at Australian Defence Force Academy, Canberra ACT 2600, Australia;University of New South Wales at Australian Defence Force Academy, Canberra ACT 2600, Australia
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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Abstract

Many objective optimization refers to optimization problems for which the number of objectives is significantly greater than conventionally studied 2 or 3. For such problems, large number of solutions become non-dominated, which reduces the convergence pressure of the Evolutionary Algorithms~(EAs) towards the Pareto Optimal Front. Recently, alternate secondary ranking schemes for have been suggested for NSGA-II in lieu of crowding distance to expedite its convergence for many objective problems. In this paper, we improvise upon an existing scheme~(epsilon dominance). The proposed approach is found to perform better than the other substitute distance assignment methods for the problems studied in this paper. A new diversity metric has also been proposed, which can be used in order to compare the performance of the various EAs.