Parallel Consultant-Guided Search with Crossover

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.