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A pattern-driven solution for designing multi-objective evolutionary algorithms

Guizzo, G; Vergilio, SR; (2018) A pattern-driven solution for designing multi-objective evolutionary algorithms. Natural Computing 10.1007/s11047-018-9677-y. (In press). Green open access

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Abstract

Multi-objective evolutionary algorithms (MOEAs) have been widely studied in the literature, which led to the development of several frameworks and techniques to implement them. Consequently, the reusability, scalability and maintainability became fundamental concerns in the development of such algorithms. To this end, the use of design patterns (DPs) can benefit, ease and improve the design of MOEAs. DPs are reusable solutions for common design problems, which can be applied to almost any context. Despite their advantages to decrease coupling, increase flexibility, and allow an easier design extension, DPs have been underexplored for MOEA design. In order to contribute to this research topic, we propose a pattern-driven solution for the design of MOEAs. The MOEA designed with our solution is compared to another MOEA designed without it. The comparison considered: the Integration and Test Order (ITO) problem and the Traveling Salesman problem (TSP). Obtained results show that the use of this DP-driven solution allows the reuse of MOEA components, without decreasing the quality, in terms of hypervolume. This means that the developer can extend the algorithms to include other components using only object-oriented mechanisms in an easier way, while maintaining the expected results.

Type: Article
Title: A pattern-driven solution for designing multi-objective evolutionary algorithms
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11047-018-9677-y
Publisher version: https://doi.org/10.1007/s11047-018-9677-y
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Meta-heuristic design pattern, Multi-objective evolutionary algorithm, Software testing, Hyper-heuristic
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10076009
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