| 英文摘要 |
The positive feedback mechanism of pheromone in the basic ant colony optimization greatly accelerates the optimization process of the algorithm, but also has disadvantages such as prone to stagnation and falling into local optimality. In order to overcome these shortcomings of ant colony optimization, an improved ant colony optimization based on adaptive chemical reaction optimization is proposed. In the optimization iteration process of the ant colony optimization, the decomposition reaction operation and the synthesis reaction operation of the adaptive chemical reaction optimization are introduced to enhance the algorithm’s ability to jump out of the local optimum and find the global optimum. Then the paper uses the improved algorithm to simulate and solve the six classic data sets in the Traveling Salesman Problem (TSP). For the same data set, the adaptive chemical reaction ant colony optimization can find a better path than the basic ant colony optimization, its solution success rate is higher, and the numerical fluctuation range of the result obtained is also smaller. These verify that the adaptive chemical reaction ant colony optimization is superior to the basic ant colony optimization in terms of algorithm optimization ability, algorithm stability and algorithm reliability. |
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