Ant Supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, Applied to Traveling Salesman Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A10238699" target="_blank" >RIV/61989100:27240/16:10238699 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7844999/" target="_blank" >http://ieeexplore.ieee.org/document/7844999/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC.2016.7844999" target="_blank" >10.1109/SMC.2016.7844999</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ant Supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, Applied to Traveling Salesman Problem

Popis výsledku v původním jazyce

AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (alpha,beta,rho). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.

Název v anglickém jazyce

Ant Supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, Applied to Traveling Salesman Problem

Popis výsledku anglicky

AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (alpha,beta,rho). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

ISBN

978-1-5090-1897-0

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

4866-4871

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Budapešť

Datum konání akce

9. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000402634704111