Abstract
Applying metaheuristics to solve large scale instances of computationally difficult optimization problems often requires using a considerable computational effort in order to reach the satisfactory results in reasonable amount of time. Parallel/distributed computation may improve performance of such approaches. It is expected that parallel metaheuristics will outperform their sequential counterparts in terms of quality of the generated solutions as well as reducing the computation time. Last years, an agent paradigm has emerged as an interesting direction for effective solving different problems. The chapter focuses on multi-agent system JABAT, dedicated for solving computationally hard optimization problems using parallel and distributed environment. Two models of computations used by JABAT, where all software agents are running on the one container and where selected software agents are distributed (moved or cloned) over available additional containers (nodes), are presented in the chapter. The influence on the above models on quality of the results and the computation time has been investigated by computational experiment,which has been carried out on selected instances of capacitated vehicle routing problem.
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Barbucha, D. (2013). Solving Instances of the Capacitated Vehicle Routing Problem Using Multi-agent Non-distributed and Distributed Environment. In: Czarnowski, I., Jędrzejowicz, P., Kacprzyk, J. (eds) Agent-Based Optimization. Studies in Computational Intelligence, vol 456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34097-0_3
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DOI: https://doi.org/10.1007/978-3-642-34097-0_3
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