Artificial Bee Colony Algorithm for Traveling Salesman Problem

Li, Weihua; Li, Wei Jia; Yang, Yisu; Liao, Hai Qiang; Li, Ji Long; Zheng, Xi Peng

doi:10.4028/www.scientific.net/amr.314-316.2191

Cited by 11 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among those, group behavior of swarm or natural organisms (such as bees, lions, spider monkeys, fish, cuckoos, etc.) is well studied to develop different Swarm Intelligent (SI) algorithms for different optimization tasks, including TSP [7,9,[27][28][29]. This section briefly describes several prominent bio-inspired algorithms for solving TSP.…”

Section: Solving Tsp With Other Prominent Bio-inspired Methodsmentioning

confidence: 99%

“…In SI, Ant Colony Optimization (ACO) is the pioneer method to solve TSP [5,6]. Later, Artificial Bee Colony (ABC) [7], Particle Swarm Optimization (PSO) [8], Cuckoo Search (CS) [9], and other SIs are used to solve TSP. An SI algorithm starts with an initial population, and then knowledge from different agents (i.e., members) is utilized to build a better solution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Solving Tsp With Other Prominent Bio-inspired Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

2022

View full text Add to dashboard Cite

show abstract

“…The algorithm is particularly efficient for the smaller test entities of the Solomon benchmarks. In the vehicle routing problem, [39,40] introduced ABC algorithm for traveling salesman problems. The algorithm has been proposed to solve the capacitated vehicle routing problem in recent years by [41,42,43].…”

Section: Artificial Bee Colony Algorithm (Abc Algorithm)mentioning

confidence: 99%

The New Methodology for Vehicular Network With Fuzzy Time Windows

Kantawong

Pravesjit

Chaisricharoen

2019

MJCS

View full text Add to dashboard Cite

This work proposes the new methodology for the vehicular network with fuzzy time windows. The Fuzzy technique is applied to produce an initial population and then the evolutionary algorithm is employed to improve the solution. In this work, the inter-route crossover, intra-route mutation, elitism strategy, and onlooker bee probability selection method were enhanced in the original processes of the evolutionary algorithm. The proposed algorithm is tested on 56 datasets of Solomon. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very inspiring, it shows that the algorithm is very competitive. Comparing with the algorithm in the literature, the proposed algorithm obtains the best solution in terms of the coefficient of variation values for almost 40 instances from the 56 problem instances. In addition, the information regarding the p-value was resolved by the Wilcoxon signed-rank test for the considered testing instances that display statistically superior performance at the 95% significance level (Î± = 0.05) on comparing algorithms.

show abstract

“…In Li et al [51], the authors proposed a hybrid algorithm combining the ABC algorithm with a nearest neighbor method to solve the TSP. To assess the performance of their proposal they applied the algorithm to a number of instances from the TSPLIB and compared it with the BCO algorithm, showing a superior performance for the instances evaluated.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Masutti

Castro

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

show abstract

Artificial Bee Colony Algorithm for Traveling Salesman Problem

Cited by 11 publications

References 17 publications

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

The New Methodology for Vehicular Network With Fuzzy Time Windows

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Contact Info

Product

Resources

About