Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization

Real-world problems are inherently constrained optimization problems often with multiple conflicting objectives. To solve such constrained multi-objective problems effectively, in this paper, we put forward a new approach which integrates self-adaptive differential evolution algorithm with α-constra...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 16; H. 8; S. 1353 - 1372
Hauptverfasser: Qian, Feng, Xu, Bin, Qi, Rongbin, Tianfield, Huaglory
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer-Verlag 01.08.2012
Springer Nature B.V
Schlagworte:
Adaptive algorithms
Algorithms
Archives & records
Artificial Intelligence
Computational Intelligence
Constraints
Control
Design
Design engineering
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Multiple objective analysis
Mutation
Optimization
Original Paper
Pareto optimum
Principles
Robotics
Search engines
Search strategies
Sorting algorithms
Differential evolution
Multi-objective optimization
α-constrained-domination
Constrained optimization
Self-adaptive strategy
ISSN:1432-7643, 1433-7479
Online-Zugang:Volltext
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Zusammenfassung:Real-world problems are inherently constrained optimization problems often with multiple conflicting objectives. To solve such constrained multi-objective problems effectively, in this paper, we put forward a new approach which integrates self-adaptive differential evolution algorithm with α-constrained-domination principle, named SADE-αCD. In SADE-αCD, the trial vector generation strategies and the DE parameters are gradually self-adjusted adaptively based on the knowledge learnt from the previous searches in generating improved solutions. Furthermore, by incorporating domination principle into α-constrained method, α-constrained-domination principle is proposed to handle constraints in multi-objective problems. The advantageous performance of SADE-αCD is validated by comparisons with non-dominated sorting genetic algorithm-II, a representative of state-of-the-art in multi-objective evolutionary algorithms, and constrained multi-objective differential evolution, over fourteen test problems and four well-known constrained multi-objective engineering design problems. The performance indicators show that SADE-αCD is an effective approach to solving constrained multi-objective problems, which is basically enabled by the integration of self-adaptive strategies and α-constrained-domination principle.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-012-0816-6