A decomposition-based approach for knee solution approximation in multi-objective optimization
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Publication Details
Author list: Sudeng S., Wattanapongsakorn N.
Publisher: Hindawi
Publication year: 2016
Start page: 3710
End page: 3717
Number of pages: 8
ISBN: 9781509006229
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
Multi-objective Evolutionary Algorithms (MOEAs) always approximate a set of optimal solutions. This set is required to be well spread and uniformly covering wide area of the Pareto-optimal front. In practical context, the Decision Maker (DM) usually chooses solutions in the middle of objective space, where the surface bulges out the most. Such solutions are called 'knee solutions'. They are the most attractive solutions to the DM when he/she does not have an explicit preference. The aim of this paper is to develop a knee-based MOEA which is a method to find Pareto-optimal solutions with biasing parameter to focus on knee regions. The proposed approach is called k-MOEA/D-DE algorithm. It uses a distance-based concept to guide the solution process towards knee regions. The extent of the obtained solutions can be controlled by density controller parameter. The approach is verified by two and three-objective knee-based test problems. In addition, the approach has been applied to many-objective knee-based test problems including four, five and six-objective problems. The results in two and three-objective problems have shown that our approach is competitive to well-known knee-based MOEAs in convergence to optimality. Furthermore, knee solutions are visible and clearly seen in four, five and six-objective problems. ฉ 2016 IEEE.
Keywords
Knee-based MOEAs, Knee region, Knee solutions
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