Abstract
Today, a large proportion of combinatorial optimization problems can be efficiently formulated as a mixed-integer program and solved with an exact solver. However, exact solvers do not scale well and thus custom metaheuristic algorithms are being designed to provide better scalability at the cost of no optimality guarantees and time-consuming development. This paper proposes a novel formalism for a large class of problems with permutative representation, together with a metaheuristic solver addressing these problems. This approach combines the advantages of both exact and metaheuristic solvers: straightforward problem formulation, scalability, low design time, and ability to find high quality solutions. Three different problems are formulated in the proposed formalism and solved with the proposed solver. The solver is benchmarked against the Gurobi Optimizer and significantly outperforms it in experiments with a fixed computational budget.
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Acknowledgements
This work has been supported by the European Regional Development Fund under the project Robotics for Industry 4.0 (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000470). The work of David Woller has been also supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS21/185/OH K3/3T/37 and by the Czech Science Foundation (GACR) under Grant Agreement 19-26143X. Computational resources were supplied by the project“e-Infrastruktura CZ” (e-INFRA CZ LM2018140) supported by the Ministry of Education, Youth and Sports of the Czech Republic.
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Woller, D., Hrazdíra, J., Kulich, M. (2023). Metaheuristic Solver for Problems with Permutative Representation. In: Vasant, P., Weber, GW., Marmolejo-Saucedo, J.A., Munapo, E., Thomas, J.J. (eds) Intelligent Computing & Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-031-19958-5_5
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