Skip to Main content Skip to Navigation
Conference papers

Multi-Swarm optimization for dynamic combinatorial problems: A case study on dynamic vehicle routing problem

Abstract : Many combinatorial real-world problems are mostly dynamic. They are dynamic in the sense that the global optimum location and its value change over the time, in contrast to static problems. The task of the optimization algorithm is to track this shifting optimum. Particle Swarm Optimization (PSO) has been previously used to solve continuous dynamic optimization problems, whereas only a few works have been proposed for combinatorial ones. One of the most interesting dynamic problems is the Dynamic Vehicle Routing Problem (DVRP). This paper presents a Multi-Adaptive Particle Swarm Optimization (MAPSO) for solving the Vehicle Routing Problem with Dynamic Requests (VRPDR). In this approach the population of particles is split into a set of interacting swarms. Such a multi-swarm helps to maintain population diversity and good tracking is achieved. The effectiveness of this approach is tested on a well-known set of benchmarks, and compared to other metaheuristics from literature. The experimental results show that our multi-swarm optimizer significantly outperforms single solution and population based metaheuristics on this problem.
Document type :
Conference papers
Complete list of metadatas

https://hal.inria.fr/hal-00690620
Contributor : Ist Rennes <>
Submitted on : Tuesday, April 24, 2012 - 9:00:53 AM
Last modification on : Thursday, May 28, 2020 - 9:22:09 AM

Links full text

Identifiers

Citation

Mostepha Redouane Khouadjia, Enrique Alba, Laetitia Jourdan, El-Ghazali Talbi. Multi-Swarm optimization for dynamic combinatorial problems: A case study on dynamic vehicle routing problem. Ants 2010: Seventh International Conference on Swarm Intelligence, Sep 2010, Bruxelles, Belgium. pp.227-238, ⟨10.1007/978-3-642-15461-4_20⟩. ⟨hal-00690620⟩

Share

Metrics

Record views

370