scholarly journals Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Ant Colony Optimization ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
The Other ◽  
Critical Properties ◽  
Ant System ◽  
Travelling Salesman ◽  
Time Limits ◽  
System Models ◽  
Benchmark Datasets

In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions.

scholarly journals Can the Agent with Limited Information Solve Travelling Salesman Problem?

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
The Other ◽  
Time Dependent ◽  
Critical Properties ◽  
Limited Information ◽  
Travelling Salesman ◽  
Benchmark Data ◽  
Benchmark Datasets

Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems.

scholarly journals Ants Colony Optimisation of a Measuring Path of Prismatic Parts on a CMM

2016 ◽  
Vol 23 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Slavenko M. Stojadinovic ◽  
Vidosav D. Majstorovic ◽  
Numan M. Durakbasa ◽  
Tatjana V. Sibalija
Keyword(s):  
Travelling Salesman Problem ◽  
Ant Colony ◽  
The Other ◽  
Ant Colony Optimisation ◽  
Travelling Salesman ◽  
Prismatic Parts ◽  
On Line ◽  
Optimisation Model ◽  
Set Of Points ◽  
The Mathematical Model

AbstractThis paper presents optimisation of a measuring probe path in inspecting the prismatic parts on a CMM. The optimisation model is based on: (i) the mathematical model that establishes an initial collision-free path presented by a set of points, and (ii) the solution of Travelling Salesman Problem (TSP) obtained with Ant Colony Optimisation (ACO). In order to solve TSP, an ACO algorithm that aims to find the shortest path of ant colony movement (i.e. the optimised path) is applied. Then, the optimised path is compared with the measuring path obtained with online programming on CMM ZEISS UMM500 and with the measuring path obtained in the CMM inspection module of Pro/ENGINEER®software. The results of comparing the optimised path with the other two generated paths show that the optimised path is at least 20% shorter than the path obtained by on-line programming on CMM ZEISS UMM500, and at least 10% shorter than the path obtained by using the CMM module in Pro/ENGINEER®.

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.

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