Mohammed A. Awadallah, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Lamees Mohammad Dalbah, Aneesa Al-Redhaei, Shaimaa Kouka, Oussama S. Enshassi
{"title":"Multi-objective Ant Colony Optimization: Review","authors":"Mohammed A. Awadallah, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Lamees Mohammad Dalbah, Aneesa Al-Redhaei, Shaimaa Kouka, Oussama S. Enshassi","doi":"10.1007/s11831-024-10178-4","DOIUrl":null,"url":null,"abstract":"<p>Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms inspired by the behavior of an ant colony to find the shortest path for food. The multi-objective ACO (MOACO) is a modified variant of ACO introduced to deal with multi-objective optimization problems (MOPs). The MOACO is seeking to find a set of solutions that achieve trade-offs between the different objectives, which help the decision-makers select the most appreciated solution according to their preferences. Recently, a large number of MOACO research works have been published in the literature, reaching 384 research papers according to the SCOPUS database. In this review paper, 189 different research works of MOACOs published in only scientific journals are considered. Through this research, researchers will gain insights into the expansion of MOACO, the theoretical foundations of MOPs and the MOACO algorithm, various MOACO variants documented in existing literature will be reviewed, and the specific application domains where MOACO has been implemented will be summarized. The critical discussion of the MOACO advantages and limitations is analyzed to provide better insight into the main research gaps in this domain. Finally, the conclusion and some possible future research directions of MOACO are also given in this work.\n</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":null,"pages":null},"PeriodicalIF":9.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Computational Methods in Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11831-024-10178-4","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms inspired by the behavior of an ant colony to find the shortest path for food. The multi-objective ACO (MOACO) is a modified variant of ACO introduced to deal with multi-objective optimization problems (MOPs). The MOACO is seeking to find a set of solutions that achieve trade-offs between the different objectives, which help the decision-makers select the most appreciated solution according to their preferences. Recently, a large number of MOACO research works have been published in the literature, reaching 384 research papers according to the SCOPUS database. In this review paper, 189 different research works of MOACOs published in only scientific journals are considered. Through this research, researchers will gain insights into the expansion of MOACO, the theoretical foundations of MOPs and the MOACO algorithm, various MOACO variants documented in existing literature will be reviewed, and the specific application domains where MOACO has been implemented will be summarized. The critical discussion of the MOACO advantages and limitations is analyzed to provide better insight into the main research gaps in this domain. Finally, the conclusion and some possible future research directions of MOACO are also given in this work.
期刊介绍:
Archives of Computational Methods in Engineering
Aim and Scope:
Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication.
Review Format:
Reviews published in the journal offer:
A survey of current literature
Critical exposition of topics in their full complexity
By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.