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Hybrid Ant Colony algorithm Using improved circle Strategy for TSP Problem

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-16页

作者： Li, Qingshun Dong, Xueshi Guo, Qingteng Qingdao Univ Qingdao Shandong Peoples R China

Based on the problems existing in the traditional ant colony algorithm in solving the traveling salesman problem, a hybrid ant colony algorithm combining the improved circle strategy and the ant colony algorithm is proposed. In the proposed hybrid ant colony algorithm, an improved circle strategy is used to optimize the solution obtained by the ant colony algorithm, so as to improve the search efficiency and search ability. At the same time, the uniform design method is used to find the optimal parameter combination of the algorithm. The improved circle strategy is based on the nearest neighbor strategy to optimize the solution obtained by the ant colony algorithm into a better solution. This paper uses eight standard instances in the TSPLIB standard library to experimentally verify the algorithm. The experimental results show that the proposed hybrid ant colony algorithm can effectively improve the convergence ability of the algorithm, obtain higher quality solutions, and have better optimization ability and stability for solving TSP problems.

关键词： Ant Colony algorithm Heuristic algorithm improved circle algorithm TSP Uniform Design

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Research on the optimal search problem of UAV based on improved genetic algorithm 5

Research on the optimal search problem of UAV based on impro...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Zhou Bo Zhao Dongmei Dalian Navy Acad Dept Basic Sci Dalian Peoples R China

ISBN: (纸本)9781665423144

Traditional genetic algorithm is prone to precocious problem. An improved genetic algorithm based on improved ring is proposed. First, the parent generation is improved and optimized by the improved circle method, then the offspring are obtained by crossing, and the inferior individuals produced after crossing are reduced by keeping the excellent genes to the maximum extent in the process of crossing, and the convergence rate is improved. The population diversity of the algorithm is guaranteed by improved ring and by choosing crossover probability and the mutation probability. Finally, the optimal search path is obtained by selecting the parent and offspring. The simulation results show that compared with the traditional Hamilton trilateral exchange algorithm, the improved genetic algorithm is simple, fast, and it can jump out of local convergence and obtain the optimal solution. It is a more reasonable UAV route method to solve the optimal search problem.

关键词： genetic algorithm improved circle algorithm hamilton trilateral Exchange optimal path

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