Two new reference vector adaptation strategies for many-objective evolutionary algorithms

Maintaining population diversity is critical for multi-objective evolutionary algorithms (MOEAs) to solve many-objective optimization problems (MaOPs). Reference vector guided MOEAs have exhibited superiority in handling this issue, where a set of well distributed reference points on a unit hyperpla...

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Vydané v:Information sciences Ročník 483; s. 332 - 349
Hlavní autori: Liang, Zhengping, Hou, Weijun, Huang, Xiang, Zhu, Zexuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.05.2019
Predmet:
Evolutionary algorithms
Many-objective optimization
Multi-objective optimization
Pareto optimal fronts
Reference vector adaptation strategy
Reference vector adaptation strategy
Multi-objective optimization
Evolutionary algorithms
Many-objective optimization
Pareto optimal fronts
ISSN:0020-0255, 1872-6291
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Popis
Shrnutí:Maintaining population diversity is critical for multi-objective evolutionary algorithms (MOEAs) to solve many-objective optimization problems (MaOPs). Reference vector guided MOEAs have exhibited superiority in handling this issue, where a set of well distributed reference points on a unit hyperplane are generated to construct the reference vectors. Nevertheless, the pre-defined reference vectors could not well handle MaOPs with irregular (e.g., convex, concave, degenerate, and discontinuous) Pareto fronts (PFs). In this paper, we propose two new reference vector adaptation strategies, namely Scaling of Reference Vectors (SRV) and Transformation of Solutions Location (TSL), to handle irregular PFs. Particularly, to solve an MaOP with a convex/concave PF, SRV introduces a specific center vector and adjusts the other reference vectors around it by using a scaling function. TSL transforms the location of well-diversified solutions into a set of new reference vectors to handle degenerate/discontinuous PFs. The two strategies are incorporated into three representative MOEAs based on reference vectors and tested on benchmark MaOPs. The comparison studies with other state-of-the-art algorithms demonstrate the efficiency of the new strategies.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2019.01.049