Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

School of Computer and Information Science

RAS ID

3900

Comments

This conference paper was originally published as: 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. Original article available here

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

 
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Link to publisher version (DOI)

10.1109/CEC.2005.1554971