A Slime Mold-Ant Colony Fusion Algorithm for Solving Traveling Salesman Problem

Liu, Meijiao; Li, Yanhui; Li, Ang; Huo, Qi; Zhang, Ning; Qu, Nan; Zhu, Mingchao; Chen, Liheng

doi:10.1109/access.2020.3035584

Cited by 20 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is simple for the ACO to become stuck in the local optimum, and the rate at which it converges on the optimal solution to the TSP is sluggish. As a result, they propose an algorithm called the Slime Mould-Ant Colony Fusion Algorithm (SMACFA) [ 33 ]. First, an optimized path for TSP is obtained using SMA.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

show abstract

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Yang et al [ 23 ] proposed an entropy learning strategy to optimize the accuracy of understanding by adaptively improving diversity and convergence and jumping out of local optimum by setting up a game strategy and introducing mean filtering. Li et al [ 24 ] proposed a slime bacteria-ant colony fusion algorithm (SMACFA), which first obtains the initial planning path by SMA. Then, a high-quality pipe is selected from the paths obtained by SMA, and the two ends of the pipe are used as fixed-point pairs; Finally, the fixed-point pair is directly applied to the ACO by fixing the selection principle.…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Zhang

Wang

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.

show abstract

“…After the termination of hybrid operations of ACO and PSO, the 3-Opt algorithm is used to update the individual solutions. Some other recent hybrid methods integrating ACO with other metaheuristic methods are Slime Mold-Ant Colony Fusion Algorithm [24], ACO with Levy Flight [15], Density Peaks Clustering and ACO with K-Opt algorithm [16], Coordinating PSO, ACO, and K-Opt [25], ACO-based Memetic Algorithm with local search [26], and ACO with Immigrants Schemes [23].…”

Section: Solving Tsp With Aco and Its Updated Modelsmentioning

confidence: 99%

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

2022

View full text Add to dashboard Cite

Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants, where an ant selects the next city to visit according to the pheromone on the trail and the visibility heuristic (inverse of distance). ACO assigns higher heuristic desirability to the nearest city without considering the issue of returning to the initial city or starting point once all the cities are visited. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which intelligently adopts a generalized formula of the visibility heuristic associated with the final destination city. ACOAV uses a new distance metric that includes proximity and eventual destination to select the next city. Including the destination in the metric reduces the tour cost because such adaptation helps to avoid using longer links while returning to the starting city. In addition, partial updates of individual solutions and 3-Opt local search operations are incorporated in the proposed ACOAV. ACOAV is evaluated on a suite of 35 benchmark TSP instances and rigorously compared with ACO. ACOAV generates better solutions for TSPs than ACO, while taking less computational time; such twofold achievements indicate the proficiency of the individual adoption techniques in ACOAV, especially in AV and partial solution update. The performance of ACOAV is also compared with the other ten state-of-the-art bio-inspired methods, including several ACO-based methods. From these evaluations, ACOAV is found as the best one for 29 TSP instances out of 35 instances; among those, optimal solutions have been achieved in 22 instances. Moreover, statistical tests comparing the performance revealed the significance of the proposed ACOAV over the considered bio-inspired methods.

show abstract

A Slime Mold-Ant Colony Fusion Algorithm for Solving Traveling Salesman Problem

Cited by 20 publications

References 40 publications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

Contact Info

Product

Resources

About