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Many-objective Optimization Using Evolutionary Algorithms: A Survey

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Recent Advances in Evolutionary Multi-objective Optimization

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 20))

Abstract

Multi-objective Evolutionary Algorithms (MOEAs) have proven their effectiveness and efficiency in solving complex problems with two or three objectives. However, recent studies have shown that the performance of the classical MOEAs is deteriorated when tackling problems involving a larger number of conflicting objectives. Since most individuals become non-dominated with respect to each others, the MOEAs’ behavior becomes similar to a random walk in the search space. Motivated by the fact that a wide range of real world applications involves the optimization of more than three objectives, several Many-objective Evolutionary Algorithms (MaOEAs) have been proposed in the literature. In this chapter, we highlight in the introduction the difficulties encountered by MOEAs when handling Many-objective Optimization Problems (MaOPs). Moreover, a classification of the most prominent MaOEAs is provided in an attempt to review and describe the evolution of the field. In addition, a summary of the most commonly used test problems, statistical tests, and performance indicators is presented. Finally, we outline some possible future research directions in this research area.

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Authors and Affiliations

  1. SOIE Lab, Computer Science Department, ISG-Tunis, University of Tunis, Bouchoucha city, 2000, Le Bardo, Tunis, Tunisia

    Slim Bechikh, Maha Elarbi & Lamjed Ben Said

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  1. Slim Bechikh
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  2. Maha Elarbi
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  3. Lamjed Ben Said
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Editors and Affiliations

  1. SOIE lab, Computer Science Department, University of Tunis, ISG-Tunis , Bouchoucha, Le Bardo, Tunisia

    Slim Bechikh

  2. Department of Mechanical Engineering, Institute of Technology, Kalyanpur, Kanpur, Uttar Pradesh, India

    Rituparna Datta

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Abhishek Gupta

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Bechikh, S., Elarbi, M., Ben Said, L. (2017). Many-objective Optimization Using Evolutionary Algorithms: A Survey. In: Bechikh, S., Datta, R., Gupta, A. (eds) Recent Advances in Evolutionary Multi-objective Optimization. Adaptation, Learning, and Optimization, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-42978-6_4

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