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Parallel Consultant-Guided Search with Crossover

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Abstract

Consultant-guided search (CGS) is a recent metaheuristic method. This approach is an algorithm in which a virtual person called a client creates a solution based on consultation with a virtual person called a consultant. In this study, we propose a parallel CGS algorithm with a genetic algorithm’s crossover and selection, and calculate an approximation solution for the traveling salesman problem. We execute a computer experiment using the benchmark problems (TSPLIB). Our algorithm provides a solution with less than 3.3% error rate for problem instances using less than 6000 cities.

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Acknowledgements

This research was supported by JSPS KAKENHI Grant Number 17K01309 and the Kansai University Grant-in-Aid for Progress of Research in Graduate Courses in 2017.

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Authors and Affiliations

  1. Kansai University, Suita, Osaka, Japan

    Yota Ueda, Hiroyuki Ebara, Koki Nakayama & Syuhei Iida

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  1. Yota Ueda
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  2. Hiroyuki Ebara
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  3. Koki Nakayama
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  4. Syuhei Iida
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Correspondence to Hiroyuki Ebara.

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Ueda, Y., Ebara, H., Nakayama, K. et al. Parallel Consultant-Guided Search with Crossover. Rev Socionetwork Strat 11, 185–200 (2017). https://doi.org/10.1007/s12626-017-0016-z

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  • DOI: https://doi.org/10.1007/s12626-017-0016-z

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