Abstract-The fastest known algorithm for calculating the hypervolume of a set of solutions to a multiobjective optimisation 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 will enable the use of hypervolume with larger populations in more objectives.
This algorithm is sensitive to the order in which the dimensions are considered REF .
Heuristics for optimizing the calculation of hypervolume for multi-objective optimization problems
2005 IEEE Congress on Evolutionary Computation
2005 IEEE Congress on Evolutionary Computation
Computer Science