Abstract
Solving rich vehicle routing problems is an important topic due to their numerous practical applications. Although there exist a plethora of (meta)heuristics to tackle this task, they are often heavily parameterized, and improperly tuned hyper-parameters adversely affect their performance. We exploit particle swarm optimization to select the pivotal hyper-parameters of a route minimization algorithm applied to the pickup and delivery problem with time windows. The experiments, performed on benchmark and real-life data, show that our approach automatically determines high-quality hyper-parameters of the underlying algorithm that improve its abilities and accelerate the convergence.
This work was supported by the European Union funds awarded to Blees Sp. z o. o. under grants POIR.04.01.01-00-0079/18-01 and UDA-RPSL.01.02.00-24-00FG/19-00. JN was supported by the Silesian University of Technology grant for maintaining and developing research potential.
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Notes
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The reasons for this inability may be capacity or time window constraint violation.
- 2.
Although for Squeeze and Mutate, their time complexity is fairly high, it is their worst-case complexity, and these procedures terminate much faster in practice.
- 3.
In PSO, the fitness function can be updated to reflect other aspects of the solutions.
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- 5.
This set and the baseline solutions are available at https://gitlab.com/tjastrzab/iccs2022/.
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Jastrzab, T. et al. (2022). Particle Swarm Optimization Configures the Route Minimization Algorithm. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_11
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