Efficient implicit constraint handling approaches for constrained optimization problems

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the b...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 4816 - 39
Hlavní autoři: Rahimi, Iman, Gandomi, Amir H., Nikoo, Mohammad Reza, Mousavi, Mohsen, Chen, Fang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 27.02.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/705/1042
Benchmarks
Boundary updating
Constraint handling
Evolutionary algorithm
Humanities and Social Sciences
Multi-objective optimization
multidisciplinary
Optimization
Science
Science (multidisciplinary)
Switching point
Constraint handling
Boundary updating
Multi-objective optimization
Switching point
Evolutionary algorithm
ISSN:2045-2322, 2045-2322
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Shrnutí:Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-54841-z