Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation

Alejandro Castellanos, Laura Cruz-Reyes, Eduardo Fernández, Gilberto Rivera, Claudia Gomez-Santillan and Nelson Rangel-Valdez
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Alejandro Castellanos: Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Madero 89440, Tamaulipas, Mexico
Laura Cruz-Reyes: Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Madero 89440, Tamaulipas, Mexico
Eduardo Fernández: Facultad de Contaduría y Administración, Universidad Autónoma de Coahuila, Torreón 27000, Coahuila, Mexico
Gilberto Rivera: División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32579, Chihuahua, Mexico
Claudia Gomez-Santillan: Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Madero 89440, Tamaulipas, Mexico
Nelson Rangel-Valdez: Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Madero 89440, Tamaulipas, Mexico

Mathematics, 2022, vol. 10, issue 3, 1-22

Abstract: This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier.

Keywords: preference incorporation; ant colony optimisation; grey wolf optimisation; interval outranking; multi-criteria decision analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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