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Multi-objective Ant Colony Optimization: An Updated Review of Approaches and Applications

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Advances in Machine Learning for Big Data Analysis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 218))

Abstract

Ant colony optimization (ACO) is one of the most representative metaheuristics derived from the broad concept known as swarm intelligence (SI) where the behavior of social insects is the main source of inspiration. Being a particular SI approach, the ACO metaheuristic is mainly characterized by its distributiveness, flexibility, capacity of interaction among simple agents, and its robustness. The ACO metaheuristic has been successfully applied to an important number of discrete and continuous single-objective optimization problems. However, this metaheuristic has shown a great potential to also cope with multi-objective optimization problems as evidenced by the several proposals currently available in that regard. This chapter is aimed at describing the most relevant and recent developments on the use of the ACO metaheuristic for solving multi-objective optimization problems. Additionally, we also derive a refined taxonomy of the types of ACO variants that have been used for multi-objective optimization and we include a review of some of their real-world applications. In the last part of the chapter, we provide some potential paths for further research in this area.

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Notes

  1. 1.

    By “alternative” the author means, with respect to evolutionary algorithms.

  2. 2.

    Without loss of generality, we will assume only minimization problems.

  3. 3.

    The term local is used by the authors to refer to the current iteration.

  4. 4.

    Efficient frontier is the term used in operations research to denote the Pareto front of a problem.

  5. 5.

    Elitism, in the context of multi-objective metaheuristics, normally consists of using an external archive (usually called a “secondary population”) that can (or cannot) interact in different ways with the main (or “primary”) population of the multi-objective metaheuristic. The main purpose of this archive is to store all the non-dominated solutions generated throughout the search process, while removing those that become dominated later in the search (called local non-dominated solutions). The approximation of the Pareto-optimal set produced by an algorithm is thus the final contents of this archive. Practically all modern multi-objective evolutionary algorithms (i.e., those designed after 1999) are elitist [6].

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Acknowledgements

Carlos A. Coello Coello acknowledges support from CONACyT grant no. 2016-01-1920 (Investigación en Fronteras de la Ciencia) and from a SEP-Cinvestav grant (application no. 4).

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Authors and Affiliations

  1. School of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza No. 2501 Sur, Col. Tecnológico, 64849, Monterrey, Nuevo León, Mexico

    Jesús Guillermo Falcón-Cardona

  2. LIDIC - Universidad Nacional de San Luis, San Luis, Argentina

    Guillermo Leguizamón

  3. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), Av. IPN No. 2508. Col. San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos A. Coello Coello

  4. Departamento de Administración, UAM-Azcapotzalco, Av San Pablo Xalpa 180, Col. Reynosa Tamaulipas, 02200, Mexico City, Mexico

    Ma. Guadalupe Castillo Tapia

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  1. Jesús Guillermo Falcón-Cardona
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  2. Guillermo Leguizamón
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  3. Carlos A. Coello Coello
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  4. Ma. Guadalupe Castillo Tapia
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Correspondence to Carlos A. Coello Coello .

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  1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  2. College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

    Yen-Wei Chen

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Falcón-Cardona, J.G., Leguizamón, G., Coello Coello, C.A., Castillo Tapia, M.G. (2022). Multi-objective Ant Colony Optimization: An Updated Review of Approaches and Applications. In: Dehuri, S., Chen, YW. (eds) Advances in Machine Learning for Big Data Analysis. Intelligent Systems Reference Library, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-8930-7_1

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