Martin Pilát
ABSTRACT
This paper describes a surrogate based multi-objective evolutionary algorithm with hyper-volume contribution-based local search. The algorithm switches between an NSGA-II phase and a local search phase. In the local search phase, a model for each of the objectives is trained and CMA-ES is used to optimize the hyper-volume contribution of each individual with respect to its two neighbors on the non-dominated front. The performance of the algorithm is evaluated using the well known ZDT and WFG benchmark suites.
Supplemental Material
Available for Download
This package contains the source codes of HO-MOMA and ASM-MOMA, in the form in which they were used to obtain the results in the paper submitted to GECCO 2014 [1].
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Index Terms
- Hypervolume-based local search in multi-objective evolutionary optimization
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