Research on an Improved Ant Colony Optimization Algorithm for Solving Traveling Salesmen Problem

Lei, Wenli; Wang, Fubao

doi:10.14257/ijdta.2016.9.9.03

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Lei and Wang [16] proposed an improvement to the efficiency of search results and sough to reduce the evolution speed and avoid the tendency of ACO to stagnate and fall into the local optimum when solving complex functions. In the proposed IWSMACO algorithm, the information weight factor is added to the path selection and pheromone adjustment mechanism to dynamically adjust the path selection probability and randomly select behavior rules.…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem

Liu

Lee²,

Lee

et al. 2023

Complex Intell. Syst.

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The traveling salesman problem (TSP) is an NP-hard problem. Thus far, a large number of researchers have proposed different ant colony optimization (ACO) algorithms to solve the TSP. These algorithms inevitably encounter problems such as long convergence time and the tendency to easily fall into local optima. On the basis of the ACO algorithm, this study proposes a dynamic adaptive ACO algorithm (DAACO). DAACO realizes the diversity of initialization of the ACO algorithm by dynamically determining the number of ants to be prevented from falling into local optimization. DAACO also adopts a hybrid local selection strategy to increase the quality of ant optimization and reduce the optimization time. Among the 20 instances of the TSPLIB dataset, the DAACO algorithm obtains 19 optimal values, and the solutions of 10 instances are better than those of other algorithms. The experimental results on the TSPLIB dataset show that the DAACO algorithm has obvious advantages in terms of convergence time, solution quality, and average value relative to existing state-of-the-art ACO algorithms.

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Section: Ant Colony Algorithmmentioning

confidence: 99%

DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem

Liu

Lee²,

Lee

et al. 2023

Complex Intell. Syst.

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“…Eight examples of TSPLIB are selected, and each of them is subjected to 50 independent simulation experiments. Some of the data are derived from the simulation results of different algorithms given in Lei and Wang 23 for corresponding examples. The simulation results of corresponding examples are shown in Table 1.…”

Section: Punching Point Recognition Of Warp-knitted Vampmentioning

confidence: 99%

Punching path optimization method for warp-knitted vamp based on machine vision and improved ant colony algorithm

Chi

Zhang

et al. 2023

Journal of Engineered Fibers and Fabrics

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Aiming at the current problems of duplicated paths and low work efficiency in machine punching of warp knitted vamp marker points, this paper proposes a punching path planning method of machine vision combined with intelligent algorithms. The method can improve the timeliness of visual recognition of punching location by limiting the search area and similarity function threshold, and improve the ability of global search and adaptive adjustment in punching path planning by combining with the improved ant colony algorithm to calculate a more accurate and optimized path more efficiently. Through the visual recognition test and the simulation test of the improved ant colony algorithm, the results show that the template matching can correctly identify the positioning hole marker points for different styles, rotation directions and lighting conditions, and the recognition accuracy is 0.43 mm and the repeat positioning accuracy is 0.09 mm; meanwhile, the improved ant colony algorithm can effectively avoid the local optimal solution, which can improve the optimal rate of the result by about 38% and the algorithm can reduce the number of iterations of the optimal solution within 60 times, which greatly saves the calculation time of path planning. The method can be used to improve the efficiency of punching in the actual warp knit vamp punching.

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“…Xu et al [25] introduced a dynamic moving method based on unidimensional chaotic mapping to improve the solution accuracy and overall efficiency of the algorithm. Lei and Wang [26] introduced an elite strategy and a max-min ant system in order to increase the search capability of the solution space and enhance the convergence speed, which ensures high quality solutions while preventing the algorithm from falling into stagnation.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

“…c � 1 ( 14) end (15) for i � 1: popsize do (16) for j � 1: citynum do (17) if i%2 �� 1 then (18) ant i select next city using c, ξ (equations ( 12)-( 13)); //special ant ( 19) else (20) ant i select next city by equation ( 2); //normal ant ( 21) end (22) Set tabu table for ant I (23) end (24) Calculate fitness of the corresponding solution obtained by ant i (equation ( 1)) ( 25) end (26) Select the best solutions for all ants (include normal ants and special ants) (27) Update global best solution (optimal solution) (28) Apply improved 2-opt algorithm to optimal solution (Section 3.3) (29) Update pheromone on normal and special best solution separately (equations ( 14)-( 16…”

Section: Classless Citiesmentioning

confidence: 99%

An Improved Ant Colony Optimization Based on an Adaptive Heuristic Factor for the Traveling Salesman Problem

Liu

Zhang

et al. 2021

Journal of Advanced Transportation

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The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms.

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Research on an Improved Ant Colony Optimization Algorithm for Solving Traveling Salesmen Problem

Abstract: In order to improve the search result and low evolution speed, and avoid the tendency towards stagnation and falling into the local optimum of ant colony optimization (ACO)

Cited by 6 publications

References 27 publications

DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem

DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem

Punching path optimization method for warp-knitted vamp based on machine vision and improved ant colony algorithm

An Improved Ant Colony Optimization Based on an Adaptive Heuristic Factor for the Traveling Salesman Problem

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