Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Information Science
RAS ID
3900
Abstract
The fastest known algorithm for calculating the hypervolume of a set of solutions to a multi-objective optimization problem is the HSO algorithm (hypervolume by slicing objectives). However, the performance of HSO for a given front varies a lot depending on the order in which it processes the objectives in that front. We present and evaluate two alternative heuristics that each attempt to identify a good order for processing the objectives of a given front. We show that both heuristics make a substantial difference to the performance of HSO for randomly-generated and benchmark data in 5-9 objectives, and that they both enable HSO to reliably avoid the worst-case performance for those fronts. The enhanced HSO enable the use of hypervolume with larger populations in more objectives.
DOI
10.1109/CEC.2005.1554971
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: While, L., Bradstreet, L., Barone, L., & Hingston, P. F. (2005). Heuristics for Optimising the Calculation of Hypervolume for Multi-objective Optimisation Problems. Proceedings of IEEE Congress on Evolutionary Computation. (pp. 2225-2232). Edinburgh, Scotland. IEEE. Available here
© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.