scholarly journals Particle Swarm and Ant Colony Algorithms and Their Applications in Chinese Traveling Salesman Problem

10.5772/8059 ◽  
2010 ◽  
Author(s):  
Shuang Cong ◽  
Yajun Jia ◽  
Ke Deng
Keyword(s):  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Algorithms

Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem

2009 ◽  
Vol 626-627 ◽  
pp. 717-722 ◽  
Author(s):  
Hong Kui Feng ◽  
Jin Song Bao ◽  
Jin Ye
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Great Success ◽  
Swarm Optimization ◽  
Continuous Space ◽  
Multiple Traveling Salesman Problem

A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.

scholarly journals Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

2021 ◽  
Vol 11 (11) ◽  
pp. 4780
Author(s):  
Muhammad Salman Qamar ◽  
Shanshan Tu ◽  
Farman Ali ◽  
Ammar Armghan ◽  
Muhammad Fahad Munir ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Standard Test ◽  
Traveling Salesman ◽  
Problem Solution ◽  
Ant System ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Novel Approach

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

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