Comparison of constraint-handling techniques for metaheuristic optimization

He, Xing-Shi, Fan, Qin-Wei, Karamanoglu, Mehmet ORCID: https://orcid.org/0000-0002-5049-2993 and Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556 (2019) Comparison of constraint-handling techniques for metaheuristic optimization. Computational Science - ICCS 2019 - 19th International Conference Proceedings, Part III, Lecture Notes in Computer Science. In: 19th International Conference on Computational Science - ICCS 2019, 12-14 June 2019, Faro, Portugal. . ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-030-22744-9_28)

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Abstract

Many design problems in engineering have highly nonlinear constraints and the proper handling of such constraints can be important to ensure solution quality. There are many different ways of handling constraints and different algorithms for optimization problems, which makes it difficult to choose for users. This paper compares six different constraint-handling techniques such as penalty methods, barrier functions, epsilon-constrained method, feasibility criteria and stochastic ranking. The pressure vessel design problem is solved by the flower pollination algorithm, and results show that stochastic ranking and epsilon-constrained method are most effective for this type of design optimization.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Paper published as:
He XS., Fan QW., Karamanoglu M., Yang XS. (2019) Comparison of Constraint-Handling Techniques for Metaheuristic Optimization. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11538. Springer, Cham
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 26768
Notes on copyright: This is a post-peer-review, pre-copyedit version of an article published in Computational Science - ICCS 2019 - Lecture Notes in Computer Science (LNCS, volume 11538). The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-22744-9_28.
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 10 Jun 2019 12:19
Last Modified: 14 Jun 2021 01:36
URI: https://eprints.mdx.ac.uk/id/eprint/26768

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