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2015•Unpublished venueRequires access

The Analysis of GR202 and Berlin 52 Datasets by Ant Colony Algorithm

Mustafa, Altiok, Barış Koçer

Open publisher page 1 citations

Abstract

Ant Colony Optimization (ACO) method is inspired by the foraging behaviour of ants to find a good path while searching for food. In ACO method was worked to find in this analysis are the most appropriate parameter values. In Traveling Salesman Problem (TSP) a salesman seeks to find the shortest possible route that visits each city exactly once and returns to the origin city. This study analyses very well-known Berlin 52 and lesser-known Gr202 test problems located in TSPLIB by Ant Colony Optimization. It also aims at finding the proper number of iterations and appropriate parameter values suitable for real world problems. In these test problems with point numbers of 52 and 202, the behaviour of Ant Colony Algorithm was observed. In addition, using these test data, the most appropriate iterations and parameter values were tried to be determined.

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What this paper is about

Ant Colony Optimization (ACO) method is inspired by the foraging behaviour of ants to find a good path while searching for food. In ACO method was worked to find in this analysis are the most appropriate parameter values. In Traveling Salesman Problem (TSP) a salesman seeks to find the shortest possible route that visits each city exactly once and returns to the origin city. This study analyses very well-known Berlin 52 and lesser-known Gr202 test problems located in TSPLIB by Ant Colony Optimization. It also aims at finding the proper number of iterations and appropriate parameter values suitable for real world problems. In these test problems with point numbers of 52 and 202, the behaviour of Ant Colony Algorithm was observed. In addition, using these test data, the most appropriate iterations and parameter values were tried to be determined.

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Available abstract

Ant Colony Optimization (ACO) method is inspired by the foraging behaviour of ants to find a good path while searching for food. In ACO method was worked to find in this analysis are the most appropriate parameter values. In Traveling Salesman Problem (TSP) a salesman seeks to find the shortest possible route that visits each city exactly once and returns to the origin city. This study analyses very well-known Berlin 52 and lesser-known Gr202 test problems located in TSPLIB by Ant Colony Optimization. It also aims at finding the proper number of iterations and appropriate parameter values suitable for real world problems. In these test problems with point numbers of 52 and 202, the behaviour of Ant Colony Algorithm was observed. In addition, using these test data, the most appropriate iterations and parameter values were tried to be determined.

Key concepts: Travelling salesman problem, Ant colony optimization algorithms, Mathematical optimization, Foraging, Computer science, Extremal optimization, Ant colony, Shortest path problem

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