Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem

2016 ◽  
pp. 317-329 ◽  
Author(s):  
Olgierd Unold ◽  
Radosław Tarnawski
Keyword(s):  
Ant Colony Optimization ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Travelling Salesman

scholarly journals Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

2019 ◽  
Vol 8 (2S4) ◽  
pp. 91-95
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Soft Computing Techniques

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.

Probe Path Planning for Flatness Measurement on Coordinate Measuring Machine Using Ant Colony Optimization

2021 ◽  
Vol 41 ◽  
pp. 85-91
Author(s):  
Gourav N. Ambewadkar ◽  
Sudarshan P. Gajre
Keyword(s):  
Ant Colony Optimization ◽  
Sampling Method ◽  
Sampling Methods ◽  
Travelling Salesman Problem ◽  
Coordinate Measuring Machine ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Crucial Problem ◽  
Measuring Machine ◽  
Flatness Error

Optimization of a flatness error inspection activity on coordinate measuring machine (CMM) is a very crucial problem which demands minimization of a probe path for productive inspection. In the present work, the approach is explained to minimize the total probe travelling length and hence, the time of flatness inspection. Three sampling methods with eight sample sizes have been considered for this work. The ant colony optimization (ACO) algorithm based on travelling salesman problem (TSP) approach was developed in MATLAB environment to find the shortest probe paths. It was verified that the probe path depends on the sampling method used to measure the flatness. The sampling method giving the shortest probe path was selected as the best-suited method for a particular sample size. The results obtained by analyzing an illustrative example shows that the proposed approach is both effective and optimum.

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