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摘要
The logistics model used in this study is a three-stage model employed by an automobile company, which aims to solve traffic problems at a total minimum cost. Recently, research on the metaheuristics method has advanced as an approximate means for solving optimization problems as found in this model. These problems can be solved using various methods such as a genetic algorithm (GA), simulated annealing, and tabu search. A genetic algorithm is superior in terms of robustness and adjustability toward a change in the structure of these problems. However, a genetic algorithm has a disadvantage in that it has a slightly inefficient search performance because it carries out a multipoint search. A hybrid genetic algorithm that combines another method is attracting considerable attention since it can compensate for a fault in a partial solution in which early convergence has a negative impact on a result. In this study, we propose a novel hybrid random key-based genetic algorithm (h-rkGA) that combines local search and parameter tuning of the crossover rate and mutation rate; h-rkGA is an improved version of the random key-based genetic algorithm (rk-GA). We attempted comparative experiments with a spanning tree-based genetic algorithm, priority-based genetic algorithm, and random key-based genetic algorithm. Further, we attempted comparative experiments with h-GA by only local search and h-GA by only parameter tuning. We report the effectiveness of the proposed method on the basis of the results of these experiments.
- 出版日期2012-5
