A Bee Colony Optimization algorithm with Frequent-closed-pattern-based Pruning Strategy for Traveling Salesman Problem

2015 ◽  
Author(s):  
Li-Pei Wong ◽  
Shin Siang Choong
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Traveling Salesman ◽  
Bee Colony ◽  
Pruning Strategy ◽  
Bee Colony Optimization ◽  
Closed Pattern

BEE COLONY OPTIMIZATION WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM

2010 ◽  
Vol 19 (03) ◽  
pp. 305-334 ◽  
Author(s):  
LI-PEI WONG ◽  
MALCOLM YOKE HEAN LOW ◽  
CHIN SOON CHONG
Keyword(s):  
Traveling Salesman Problem ◽  
Hamiltonian Path ◽  
Industrial Applications ◽  
Traveling Salesman ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Routing Problem ◽  
Bee Colony ◽  
Pruning Strategy ◽  
Bee Colony Optimization

Many real world industrial applications involve the Traveling Salesman Problem (TSP), which is a problem that finds a Hamiltonian path with minimum cost. Examples of problems that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a Bee Colony Optimization (BCO) algorithm for symmetrical TSP. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The algorithm is integrated with a fixed-radius near neighbour 2-opt (FRNN 2-opt) heuristic to further improve prior solutions generated by the BCO model. To limit the overhead incurred by the FRNN 2-opt, a frequency-based pruning strategy is proposed. The pruning strategy allows only a subset of the promising solutions to undergo local optimization. Experimental results comparing the proposed BCO algorithm with existing approaches on a set of benchmark problems are presented. For 84 benchmark problems, the BCO algorithm is able to obtain an overall average solution quality of 0.31% from known optimum. The results also show that it is comparable to other algorithms such as Ant Colony Optimization and Particle Swarm Optimization.

Artificial Bee Colony Algorithm for Traveling Salesman Problem

2011 ◽  
Vol 314-316 ◽  
pp. 2191-2196 ◽  
Author(s):  
Wei Hua Li ◽  
Wei Jia Li ◽  
Yuan Yang ◽  
Hai Qiang Liao ◽  
Ji Long Li ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Traveling Salesman ◽  
Comparison Study ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Benchmark Instances ◽  
Bee Colony Optimization

By combining the modified nearest neighbor approach and the improved inver-over operation, an Artificial Bee Colony (ABC) Algorithm for Traveling Salesman Problem (TSP) is proposed in this paper. The heuristic approach was tested in some benchmark instances selected from TSPLIB. In addition, a comparison study between the proposed algorithm and the Bee Colony Optimization (BCO) model is presented. Experimental results show that the presented algorithm outperforms the BCO method and can efficiently tackle the small and medium scale TSP instances.

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Sign in / Sign up

Export Citation Format

Share Document