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Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics Plus Integer Linear Programming

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Applications of Evolutionary Computation (EvoApplications 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12694))

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Abstract

Metaheuristics for solving multiobjective problems can provide an approximation of the Pareto front in a short time, but can also have difficulties finding feasible solutions in constrained problems. Integer linear programming solvers, on the other hand, are good at finding feasible solutions, but they can require some time to find and guarantee the efficient solutions of the problem. In this work we combine these two ideas to propose a hybrid algorithm mixing an exploration heuristic for multiobjective optimization with integer linear programming to solve multiobjective problems with binary variables and linear constraints. The algorithm has been designed to provide an approximation of the Pareto front that is well-spread throughout the objective space. In order to check the performance, we compare it with three popular metaheuristics using two benchmarks of multiobjective binary constrained problems. The results show that the proposed approach provides better performance than the baseline algorithms in terms of number of the solutions, hypervolume, generational distance, inverted generational distance, and the additive epsilon indicator.

This research is partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2017-88213-R (6city); Universidad de Málaga, Consejería de Economía y Conocimiento de la Junta de Andaluía and FEDER under grant number UMA18-FEDERJA-003 (PRECOG); Spanish Ministry of Science, Innovation and Universities and FEDER under contracts RTC-2017-6714-5 (Eco-IoT) and RED2018-102472-T (SEBASENet 2.0); and TAILOR ICT-48 Network (No 952215) funded by EU Horizon 2020 research and innovation programme.

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Notes

  1. 1.

    Outputs for the ILP solver CPLEX 12.6.2.

  2. 2.

    Available in http://home.ku.edu.tr/~moolibrary/.

  3. 3.

    Available in https://sop.tik.ee.ethz.ch/download/supplementary/testProblemSuite/.

  4. 4.

    https://github.com/MiguelAngelDominguezRios/MOFeLS.

  5. 5.

    https://github.com/jMetal/jMetal.

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Authors and Affiliations

  1. ITIS Software, Universidad de Málaga, Málaga, Spain

    Miguel Ángel Domínguez-Ríos, Francisco Chicano & Enrique Alba

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  1. Miguel Ángel Domínguez-Ríos
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  2. Francisco Chicano
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  3. Enrique Alba
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Correspondence to Miguel Ángel Domínguez-Ríos .

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Editors and Affiliations

  1. ETSIIT-CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

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Domínguez-Ríos, M.Á., Chicano, F., Alba, E. (2021). Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics Plus Integer Linear Programming. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-72699-7_16

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