A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization

Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 20; číslo 1; s. 16 - 37
Hlavní autoři: Yuan, Yuan, Xu, Hua, Wang, Bo, Yao, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Convergence
diversity
Dominance
dominance relation
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Indexes
Many-objective optimization
Mathematical analysis
Mathematical models
non-dominated sorting
Optimization
Production scheduling
Sociology
Sorting
Statistics
many-objective optimization
diversity
nondominated sorting
dominance relation
Convergence
ISSN:1089-778X, 1941-0026
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Shrnutí:Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the nondominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is evaluated on a number of well-known benchmark problems having 3-15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test functions considered in this paper, and it is compared favorably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2420112