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An Improved Secondary Ranking for Many Objective Optimization Problems by Hemant Kumar Singh; Amitay Isaacs; Tapabrata Ray; Warren Smith is a scholarly article available to read on EtoBox.

What is An Improved Secondary Ranking for Many Objective Optimization Problems about?

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.

Author

Hemant Kumar Singh; Amitay Isaacs; Tapabrata Ray; Warren Smith

Publisher

ACM

Published

2009

Language

EN