EDA++: Estimation of Distribution Algorithms With Feasibility Conserving Mechanisms for Constrained Continuous Optimization

Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, reaching feasible solutions has been a challe...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 26; číslo 5; s. 1144 - 1156
Hlavní autoři: Shirazi, Abolfazl, Ceberio, Josu, Lozano, Jose A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Codes
Computational modeling
Constraints
continuous optimization
estimation of distribution algorithms (EDAs)
Feasibility
mapping
Mathematical models
nonlinear constraints
Optimization
Probabilistic logic
seeding
Sociology
Statistics
ISSN:1089-778X, 1941-0026
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Shrnutí:Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this article, we adopt the framework of estimation of distribution algorithms (EDAs) and propose a new algorithm (EDA++) equipped with some mechanisms to deal with nonlinear constraints. These mechanisms are associated with different stages of the EDA, including seeding, learning, and mapping. It is shown that, besides increasing the quality of the solutions in terms of objective values, the feasibility of the final solutions is guaranteed if an initial population of feasible solutions is seeded to the algorithm. The EDA with the proposed mechanisms is applied to two suites of benchmark problems for constrained continuous optimization and its performance is compared with some state-of-the-art algorithms and constraint-handling methods. Conducted experiments confirm the speed, robustness, and efficiency of the proposed algorithm in tackling various problems with linear and nonlinear constraints.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3153933