Optimizacija s pomočjo kolonije mravelj

Ivan Pešl (Author), Viljem Žumer (Author), Janez Brest (Author)

Abstract

V naravi so mravlje sposobne najti najkrajšo pot od vira hrane do gnezda brez uporabe vizualnih informacij. Poleg tega so se zmožne prilagoditi spremembam v okolju. na primer najti novo naj krajšo pot. ko trenutno pot preseka ovira. Pri tem nastane zamisel, da bi lahko bilo posnemanje takšnega obnašanja mravelj učinkovito tudi v diskretnem svetu. V članku bomo prikazali reševanje problema trgovskega potnika s pomočjo optimizacije mravelj.

Keywords

kolonija mravelj;umetna inteligenca;inteligenca roja;problem trgovskega potnika;

Data

Language:	Slovenian
Year of publishing:	2006
Typology:	1.01 - Original Scientific Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	Elektrotehniška zveza Slovenije
UDC:	004.8
COBISS:	10672918
ISSN:	0013-5852
Views:	1431
Downloads:	55
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	ACO - Ant Colony Optimization
Secondary abstract:	Ant colony optimization is a relatively new approach to solving NP-Hard problems. It is based on the behavior of real ants, which always find the shortest path between their nest and a food source. Such behavior can be transferred into the discrcte world, were real ants are replaced by simple agents. Such simple agents are placed into the environment where different combinatorial problems can be solved In this paper we describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Artificial ants successively generate shorter feasible tours by using information accumulated in the form of a phermone trail deposited on edges of the TSP graph [1]. The basic ant behavior can be improved by adding heuristic information, e.g. local search. We describe several different algorithms used in solving the TSP (and similar) problems. We start from the first algorithm that was first used in ant optimization named Ant System. This algorithm has been followed by many others approaches resulting in better performance of ant colony optimization. The main job is to test the ant behavior on different graphs, taken from the TSPLlJJ95 library. At the end we show a comparison of ant algorithms on several instances of TSP.
Secondary keywords:	ant colony optimization;artificial intelligence;swarm intelligence;travelling salesman problem;
URN:	URN:NBN:SI
Type (COBISS):	Not categorized
Pages:	str. 93-98
Volume:	ǂVol. ǂ73
Issue:	ǂno. ǂ2-3
Chronology:	2006
ID:	1739709