A genetic algorithm with jumping gene and heuristic operators for traveling salesman problem

Zhang, Panli; Wang, Jiquan; Tian, Zhanwei; Sun, Shoujin; Li, Jianting; Yang, Jing

doi:10.1016/j.asoc.2022.109339

Cited by 40 publications

(19 citation statements)

References 39 publications

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“…The numerical research suggests that the new CMX outperforms well-known crossover operators such as and PMX in middle-scale instances. In 2022, Zhang et al [35] proposed a genetic algorithm with jumping gene and heuristic operators for TSPs, where the heuristic operators include 2-opt and BHX. The key distinction between the BHX and Grefenstette's heuristic algorithm is that the BHX always chooses the candidate that is closest to the present city out of the four possible candidates.…”

Section: Genetic Algorithm For Tspsmentioning

confidence: 99%

“…The role of the selection operator is to choose some eligible chromosomes for the next generation; a decent selection operator will help to converge rapidly and prevent local optimal, but a poor one will not. Because the objective values of TSPs are not stable, a proper transformation for the objective values is required, which is called the fitness function [35].…”

Section: Fitness Function and Selection Operatormentioning

confidence: 99%

“…Most types of GAs contain three main segments: selection operator, crossover operator and mutation operator. Due to the high effectiveness and versatility of GAs, they have been widely employed to solve TSPs and other challenging optimization problems [35,36]. However, there are still several doubts to TSPs, including premature convergence, population initialization, problem encoding, etc [37].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, crossover operators have a significant influence on the performance of GA and are a key factor in global searching and convergence speed. As a matter of fact, various crossover operators have been proposed for TSP, including partially mapped crossover (PMX) [38], ordered crossover (OX) [39], cycle crossover (CX) [40], sequential constructive crossover operator (SCX) [41], completely mapped crossover operators (CMX) [42] and others based on heuristic algorithms such as bidirectional heuristic crossover operator (BHX) [43]. Additionally, merging GAs with local search or heuristic algorithms will reveal both of their advantages, including high convergence speed and the capacity for global optimization, therefore it has been a hot topic of study [32,44,45].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Xu¹,

Lan²

2023

Preprint

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Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

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Section: Genetic Algorithm For Tspsmentioning

confidence: 99%

Section: Fitness Function and Selection Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Xu¹,

Lan²

2023

Preprint

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“…However, many of its deficiencies and defects have also been exposed, such as poor local search ability, slow convergence speed and it is easy to fall into local optima 4 . With the expansion of the dimensions of the problem to be optimized, these deficiencies are becoming more and more prominent, and the most prominent problem is the problem of premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Optimization of nonlinear functions based on improved genetic algorithm

Shi

Meng

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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Premature convergence and falling into local optimum easily are important problems for genetic algorithm. Therefore a new algorithm is constructed in this paper, which can be performed among multiple populations by introducing different optimization parameters. The final formation is aggregated into an elite population, where genetic operations such as selection, crossover, mutation, etc. do not exist, for the purpose of ensuring that the optimal individuals selected between different populations are not destroyed and lost. The simulation results show that the algorithm used in this paper has better optimization ability and higher calculation accuracy for solving local optimal problems of nonlinear functions.

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Review of Traveling Salesman Problem Solution Methods

Yang,

Wang,

et al. 2024

Communications in Computer and Information Science

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A genetic algorithm with jumping gene and heuristic operators for traveling salesman problem

Cited by 40 publications

References 39 publications

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Optimization of nonlinear functions based on improved genetic algorithm

Review of Traveling Salesman Problem Solution Methods

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