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Open AccessArticle

Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems

Department of Information Systems, Gdynia Maritime University, 81-225 Gdynia, Poland
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Algorithms 2020, 13(6), 142; https://doi.org/10.3390/a13060142
Received: 26 May 2020 / Revised: 9 June 2020 / Accepted: 11 June 2020 / Published: 12 June 2020
(This article belongs to the Special Issue Swarm Intelligence Applications for NP Hard Challenges)
One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach in which a swarm of agents tries to improve solutions from the population of solutions. The process is carried out in parallel threads. The proposed algorithm—based on the mushroom-picking metaphor—was implemented using Scala in an Apache Spark environment. An extended computational experiment shows how introducing a combination of simple optimization agents and increasing the number of threads may improve the results obtained by the model in the case of TSP and JSSP problems. View Full-Text
Keywords: optimization agents; parallel computations; traveling salesman problem; job-shop scheduling; Scala; Apache Spark optimization agents; parallel computations; traveling salesman problem; job-shop scheduling; Scala; Apache Spark
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  • Externally hosted supplementary file 1
    Link: https://bitbucket.org/wierzbowska/mpa-for-tsp/src/master
    Description: Source files of MPA for TSP: https://bitbucket.org/wierzbowska/mpa-for-tsp/src/master/ ----------------- Under the above link the source files have been published (as stated in the References, position 41), for the purpose of this paper only.
MDPI and ACS Style

Jedrzejowicz, P.; Wierzbowska, I. Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems. Algorithms 2020, 13, 142.

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