Abstract
We propose PHYSH (Parallel HYbridization for Simple Heuristics), a framework to ease the design and implementation of hybrid metaheuristics via cooperative parallelism. With this framework, the user only needs encode each of the desired metaheuristics and may rely on PHYSH for parallelization, cooperation and hybridization. PHYSH supports the combination of population-based and single-solution metaheuristics and enables the user to control the tradeoff between intensification and diversification. We also provide an open-source implementation of this framework which we use to model the Quadratic Assignment Problem (QAP) with a hybrid solver, combining three metaheuristics. We present experimental evidence that PHYSH brings significant improvements over competing approaches, as witness the performance on representative hard instances of QAP.
This work was partly funded by FCT under grant UID/CEC/4668/2016 (LISP).
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Notes
- 1.
Terms “immigration” and “emigration” are from the metaheuristics point-of-view.
- 2.
The source code is available from https://github.com/jlopezrf/COPSolver-V_2.0.
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López, J., Múnera, D., Diaz, D., Abreu, S. (2018). Weaving of Metaheuristics with Cooperative Parallelism. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_35
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