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

Result code in 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>

Alternative codes found

RIV/60162694:G42__/24:00558866

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/VJ02010036" target="_blank" >VJ02010036: An Artificial Intelligence-Controlled Robotic System for Intelligence and Reconnaissance Operations</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

IEEE Transactions on Evolutionary Computation

ISSN

1089-778X

e-ISSN

—

Volume of the periodical

27

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

1866-1880

UT code for WoS article

001125199200012

EID of the result in the Scopus database

2-s2.0-85146230165