Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty funct...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2014; H. 2014; S. 1 - 15
Hauptverfasser: Jiang, Chuanxian, Cheng, Xiaohui, Wang, Ning, Dong, Minggang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Schlagworte:
Competition
Complex variables
Constraints
Evolution
Evolutionary algorithms
Evolutionary computation
Functions (mathematics)
Genetic algorithms
Handling
Mathematical analysis
Mathematical problems
Objectives
Optimization
Penalty function
Queuing theory
Real variables
Rendering
Science
ISSN:1024-123X, 1563-5147
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Zusammenfassung:Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2014/617905