Parallel Consultant-Guided Search with Crossover

Yota Ueda (), Hiroyuki Ebara (), Koki Nakayama and Syuhei Iida
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Yota Ueda: Kansai University
Hiroyuki Ebara: Kansai University
Koki Nakayama: Kansai University
Syuhei Iida: Kansai University

The Review of Socionetwork Strategies, 2017, vol. 11, issue 2, 185-200

Abstract: 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.

Keywords: Consultant-guided search; Crossover; Parallel processing; Metaheuristics; Combinatorial optimization problem (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12626-017-0016-z

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