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Title:Optimizacija s pomočjo kolonije mravelj
Authors:ID Pešl, Ivan (Author)
ID Žumer, Viljem (Author)
ID Brest, Janez (Author)
Files:URL http://www.dlib.si/details/URN:NBN:SI:DOC-674U45KF
 
Language:Slovenian
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
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
Publisher:Elektrotehniška zveza Slovenije
Year of publishing:2006
Number of pages:str. 93-98
Numbering:Vol. 73, no. 2-3
PID:20.500.12556/DKUM-52837 New window
UDC:004.8
ISSN on article:0013-5852
COBISS.SI-ID:10672918 New window
NUK URN:URN:SI:UM:DK:JLVPZAPP
Publication date in DKUM:10.07.2015
Views:2460
Downloads:70
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
PEŠL, Ivan, ŽUMER, Viljem and BREST, Janez, 2006, Optimizacija s pomočjo kolonije mravelj. Elektrotehniški vestnik [online]. 2006. Vol. 73, no. 2–3, p. 93–98. [Accessed 30 March 2025]. Retrieved from: http://www.dlib.si/details/URN:NBN:SI:DOC-674U45KF
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Record is a part of a journal

Title:Elektrotehniški vestnik
Publisher:Strokovna zadruga koncesijoniranih elektrotehnikov
ISSN:0013-5852
COBISS.SI-ID:742916 New window

Secondary language

Language:English
Title:ACO - Ant Colony Optimization
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.
Keywords:ant colony optimization, artificial intelligence, swarm intelligence, travelling salesman problem


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