Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F24%3A00558866" target="_blank" >RIV/60162694:G43__/24:00558866 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G42__/24:00558866

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9991848" target="_blank" >https://ieeexplore.ieee.org/document/9991848</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TEVC.2022.3230042" target="_blank" >10.1109/TEVC.2022.3230042</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Popis výsledku v původním jazyce

This article deals with the novel metaheuristic algorithm based on the Ant Colony Optimization (ACO) principle. It implements several novel mechanisms that improve its overall performance, lower the optimization time, and reduce the negative behavior which is typically connected with ACO-based algorithms (such as prematurely falling into local optima, or the impact of setting of control parameters on the convergence for different problem configurations). The most significant novel techniques, implemented for the first time to solve the Multi-Depot Vehicle Routing Problem (MDVRP), are as follows: (a) node clustering where transition vertices are organized into a set of candidate lists called clusters; (b) adaptive pheromone evaporation which is adapted during optimization according to the diversity of the population of ant solutions (measured by information entropy). Moreover, a new termination condition, based also on the population diversity, is formulated. The effectiveness of the proposed algorithm for the MDVRP is evaluated via a set of experiments on 23 well-known benchmark instances. Performance is compared with several state-of-the-art metaheuristic methods; the results show that the proposed algorithm outperforms these methods in most cases. Furthermore, the novel mechanisms are analyzed and discussed from points of view of performance, optimization time, and convergence. The findings achieved in this article bring new contributions to the very popular ACO-based algorithms; they can be applied to solve not only the MDVRP, but also, if adapted, to related complex NP-hard problems.

Název v anglickém jazyce

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Popis výsledku anglicky

This article deals with the novel metaheuristic algorithm based on the Ant Colony Optimization (ACO) principle. It implements several novel mechanisms that improve its overall performance, lower the optimization time, and reduce the negative behavior which is typically connected with ACO-based algorithms (such as prematurely falling into local optima, or the impact of setting of control parameters on the convergence for different problem configurations). The most significant novel techniques, implemented for the first time to solve the Multi-Depot Vehicle Routing Problem (MDVRP), are as follows: (a) node clustering where transition vertices are organized into a set of candidate lists called clusters; (b) adaptive pheromone evaporation which is adapted during optimization according to the diversity of the population of ant solutions (measured by information entropy). Moreover, a new termination condition, based also on the population diversity, is formulated. The effectiveness of the proposed algorithm for the MDVRP is evaluated via a set of experiments on 23 well-known benchmark instances. Performance is compared with several state-of-the-art metaheuristic methods; the results show that the proposed algorithm outperforms these methods in most cases. Furthermore, the novel mechanisms are analyzed and discussed from points of view of performance, optimization time, and convergence. The findings achieved in this article bring new contributions to the very popular ACO-based algorithms; they can be applied to solve not only the MDVRP, but also, if adapted, to related complex NP-hard problems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/VJ02010036" target="_blank" >VJ02010036: Robotický systém řízený algoritmy umělé inteligence pro zpravodajské a průzkumné účely</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Evolutionary Computation

ISSN

1089-778X

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1866-1880

Kód UT WoS článku

001125199200012

EID výsledku v databázi Scopus

2-s2.0-85146230165