A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

Author:

Villagra Andrea1ORCID,Alba Enrique2,Leguizamón Guillermo3

Affiliation:

1. Universidad Nacional de la Patagonia Austral, Ruta 3 Acceso Norte, s/n, Caleta Olivia, 9011 Santa Cruz, Argentina

2. Universidad de Málaga, Campus Teatinos, 29071 Málaga, Spain

3. Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina

Abstract

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

Funder

Universidad Nacional de la Patagonia Austral

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Metaheuristics and Software Engineering: Past, Present, and Future;International Journal of Software Engineering and Knowledge Engineering;2021-09

2. Movilidad inteligente: reducción de emisión de gases;Informes Científicos Técnicos - UNPA;2019-08-27

3. How Can Metaheuristics Help Software Engineers?;Search-Based Software Engineering;2018

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