The application of ant colony optimization in the solution of 3D traveling salesman problem on a sphere

Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed. The BBMA adopts Gaussian distribution and Lévy flight, which improves the convergence speed and accuracy of the algorithm and makes better exploration and exploitation of the search region. The minimum spanning tree (MST) problem is a classic combinatorial optimization problem. This study provides a mathematical model for solving a variant of the MST problem, in which all points and solutions are on a sphere. Finally, the BBMA is used to solve the large-scale spherical MST problems. By comparing and analyzing the results of BBMA and other swarm intelligence algorithms in sixteen scales, the experimental results illustrate that the proposed algorithm is superior to other algorithms for the MST problems on a sphere.

show abstract

“…Eqs. 3-5 show a sphere with radius r, and the center is at the coordinate origin (Eldem and Ülker, 2017).…”

Section: Representation Of Points On a Spherementioning

confidence: 99%

“…Eqs. 3 – 5 show a sphere with radius

, and the center is at the coordinate origin ( Eldem and Ülker, 2017 ).

where parameters

and

determine a position by representing lines of constant longitude and lines of constant latitude, respectively.…”

Section: Related Workmentioning

confidence: 99%

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Zhang

Zhou

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Literature [32] describes the distance distribution between random points on a sphere. The ant colony algorithm [33], firefly algorithm [34], an improved flower pollination algorithm based on greedy strategy [35], and the discrete improved cuckoo algorithm [36] are used to solve STSP. As we know, the surface of the earth on which we live is very close to a sphere.…”

Section: Introductionmentioning

confidence: 99%

Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

Zhou

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

The multiple traveling salesman problem (MTSP) is an extension of the traveling salesman problem (TSP). It is found that the MTSP problem on a three-dimensional sphere has more research value. In a spherical space, each city is located on the surface of the Earth. To solve this problem, an integer-serialized coding and decoding scheme was adopted, and artificial electric field algorithm (AEFA) was mixed with greedy strategy and state transition strategy, and an artificial electric field algorithm based on greedy state transition strategy (GSTAEFA) was proposed. Greedy state transition strategy provides state transition interference for AEFA, increases the diversity of population, and effectively improves the accuracy of the algorithm. Finally, we test the performance of GSTAEFA by optimizing examples with different numbers of cities. Experimental results show that GSTAEFA has better performance in solving SMTSP problems than other swarm intelligence algorithms.

show abstract

“…Ant colony algorithm is first introduced by Dorigo, Maniezzo, and [2]. The other studies implementing ant colony optimization are ant colony optimization for permutation flowshop scheduling [3], ant colony optimization for job shop scheduling [4], applying ant colony optimization to increase the speed of the road network [5], ant colony optimization for solving the shortest path problem [6], ant colony optimization for generalized TSP problem [7], ant colony optimization for solving 3D TSP [8], and implementation of traveling salesman problem using ant colony optimization [9]. Meanwhile, the studies which improving ant colony optimization are solving traveling salesman problem based on ants with memory [10], modified ant colony optimization for grid scheduling [11], an improved ant colony optimization for machines scheduling [12], modified ant colony optimization by applying a local search procedure [13], Development of ant colony optimization based on statistical analysis and hypothesis testing [14], ant colony optimization with fault tolerance [15], a novel hybrid ant colony optimization [16], improved ant colony optimization forweapon-target assignment [17], ant colony clustering algorithm [18], improving ant colony optimization algorithm in transmission status rule [19].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization With Double Selections for Solving Integrated Scheduling Problem in Manufacturer

Abusini

Sukmarini

Karim³

2019

JEMIS

View full text Add to dashboard Cite

In this paper, we studied ant colony optimization for solving integrated scheduling of production and distribution problems. We improved the ant colony optimization by adding double selections, there are roulette wheel and elitism selections. Roulette wheel selection is used to determine the path where ants pass through before knowing pheromone information in that path. Meanwhile, elitism selection is used to keep the best solution before the more optimum solution obtained. Then, ant colony optimization and improved ant colony optimization are implemented in solving integrated scheduling of production and distribution problem in PT. BFPI. The aim of this paper is to achieve optimum production and distribution schedule in order to minimize the total cost of production and distribution. We also compare the performance of both applied methods and draw a conclusion. The results show that the method we proposed has more advantage.

show abstract

The application of ant colony optimization in the solution of 3D traveling salesman problem on a sphere

Cited by 16 publications

References 23 publications

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

Ant Colony Optimization With Double Selections for Solving Integrated Scheduling Problem in Manufacturer

Contact Info

Product

Resources

About