Abstract
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.
Original language | English |
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Pages (from-to) | 1353-1372 |
Number of pages | 20 |
Journal | Soft Computing |
Volume | 16 |
Issue number | 8 |
Early online date | 26 Jan 2012 |
DOIs | |
Publication status | Published - Aug 2012 |
Keywords
- α-constrained-domination
- constrained optimization
- differential evolution
- multi-objective optimization
- self-adaptive strategy
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Geometry and Topology