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Recent Results and Open Problems in Evolutionary Multiobjective Optimization

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Theory and Practice of Natural Computing (TPNC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10687))

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Abstract

Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the so-called multi-objective optimization problems). This area, known as EMOO (Evolutionary Multi-Objective Optimization) has had an important growth in the last 20 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this paper, we will briefly review some of the research topics on evolutionary multi-objective optimization that are currently attracting a lot of interest (e.g., indicator-based selection, many-objective optimization and use of surrogates) and which represent good opportunities for doing research. Some of the challenges currently faced by this discipline will also be delineated.

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Notes

  1. 1.

    A metaheuristic is a high level strategy for exploring search spaces by using different methods [14]. Metaheuristics have both a diversification (i.e., exploration of the search space) and an intensification (i.e., exploitation of the accumulated search experience) procedure.

  2. 2.

    The author maintains the EMOO repository, which currently contains over 10,850 bibliographic references related to evolutionary multi-objective optimization. The EMOO repository is located at: https://emoo.cs.cinvestav.mx.

  3. 3.

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

  4. 4.

    In fact, the earliest use of the hypervolume into a MOEA is as a density estimator in a secondary population (see [65]).

  5. 5.

    See also:

    http://ls11-www.cs.uni-dortmund.de/rudolph/hypervolume/start

    http://people.mpi-inf.mpg.de/~tfried/HYP/

    http://iridia.ulb.ac.be/~manuel/hypervolume.

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The author gratefully acknowledges support from CONACyT grant no. 221551.

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  1. Evolutionary Computation Group, Departamento de Computación, CINVESTAV, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico D.F., 07360, Mexico

    Carlos A. Coello Coello

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  3. University of Extremadura, Cáceres, Spain

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Coello Coello, C.A. (2017). Recent Results and Open Problems in Evolutionary Multiobjective Optimization. In: Martín-Vide, C., Neruda, R., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2017. Lecture Notes in Computer Science(), vol 10687. Springer, Cham. https://doi.org/10.1007/978-3-319-71069-3_1

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