Heuristics for optimizing the calculation of hypervolume for multi-objective optimization problems
Heuristics for optimizing the calculation of hypervolume for multi-objective optimization problems
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DOI：
10.1109/cec.2005.1554971
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发表时间：
2005-12
期刊：
影响因子：
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通讯作者：
Lyndon While;L. Bradstreet;L. Barone;P. Hingston
中科院分区：
文献类型：
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作者：
Lyndon While;L. Bradstreet;L. Barone;P. Hingston
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.
