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Benchmarking the performance of genetic algorithms on constrained dynamic problems

Benchmarking the performance of genetic algorithms on constrained dynamic problems
Benchmarking the performance of genetic algorithms on constrained dynamic problems
The growing interest in dynamic optimisation has accelerated the development of genetic algorithms with specific mechanisms for these problems. To ensure that these developed mechanisms are capable of solving a wide range of practical problems it is important to have a diverse set of benchmarking functions to ensure the selection of the most appropriate Genetic Algorithm. However, the currently available benchmarking sets are limited to unconstrained problems with predominantly continuous characteristics. In this paper, the existing range of dynamic problems is extended with15 novel constrained multi-objective functions. To determine how genetic algorithms perform on these constrained problems, and how this behaviour relates to unconstrained dynamic optimisation, 6 top-performing dynamic genetic algorithms are compared alongside 4 re-initialization strategies on the proposed test set, as well as the currently existing unconstrained cases. The results show that there are no differences between constrained/unconstrained optimisation, in contrast to the static problems. Therefore, dynamicity is the prevalent characteristic of these problems, which is shown to be more important than the discontinuous nature of the search and objective spaces. The best performing algorithm overall is MOEA/D, and VP is the best re-initialisation strategy. It is demonstrated that there is a need for more dynamic specific methodologies with high convergence, as it is more important to performance on dynamic problems than diversity.
dynamic multi-objective optimisation, constrained problems, Genetic algorithms, performance benchmark, re-initialisation
1567-7818
Grudniewski, Przemyslaw
31ca5517-c2c8-49dd-9536-6af3aefd8d33
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Grudniewski, Przemyslaw
31ca5517-c2c8-49dd-9536-6af3aefd8d33
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28

Grudniewski, Przemyslaw and Sobey, Adam (2020) Benchmarking the performance of genetic algorithms on constrained dynamic problems. Natural Computing. (Submitted)

Record type: Article

Abstract

The growing interest in dynamic optimisation has accelerated the development of genetic algorithms with specific mechanisms for these problems. To ensure that these developed mechanisms are capable of solving a wide range of practical problems it is important to have a diverse set of benchmarking functions to ensure the selection of the most appropriate Genetic Algorithm. However, the currently available benchmarking sets are limited to unconstrained problems with predominantly continuous characteristics. In this paper, the existing range of dynamic problems is extended with15 novel constrained multi-objective functions. To determine how genetic algorithms perform on these constrained problems, and how this behaviour relates to unconstrained dynamic optimisation, 6 top-performing dynamic genetic algorithms are compared alongside 4 re-initialization strategies on the proposed test set, as well as the currently existing unconstrained cases. The results show that there are no differences between constrained/unconstrained optimisation, in contrast to the static problems. Therefore, dynamicity is the prevalent characteristic of these problems, which is shown to be more important than the discontinuous nature of the search and objective spaces. The best performing algorithm overall is MOEA/D, and VP is the best re-initialisation strategy. It is demonstrated that there is a need for more dynamic specific methodologies with high convergence, as it is more important to performance on dynamic problems than diversity.

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SUBMITTED Natural Computing P.A. Grudniewski, A.J. Sobey - Manuscript (1) - Author's Original
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SUBMITTED Natural Computing P.A. Grudniewski, A.J. Sobey - Appendix (1)
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More information

Submitted date: 1 April 2020
Keywords: dynamic multi-objective optimisation, constrained problems, Genetic algorithms, performance benchmark, re-initialisation

Identifiers

Local EPrints ID: 439161
URI: http://eprints.soton.ac.uk/id/eprint/439161
ISSN: 1567-7818
PURE UUID: 885f42c9-829f-490f-824e-d286992f54d4
ORCID for Przemyslaw Grudniewski: ORCID iD orcid.org/0000-0003-0635-3125
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338

Catalogue record

Date deposited: 06 Apr 2020 16:30
Last modified: 23 May 2020 00:45

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