{"title":"A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions","authors":"Olanrewaju Lawrence Abraham , Md Asri Ngadi","doi":"10.1016/j.dajour.2025.100551","DOIUrl":null,"url":null,"abstract":"<div><div>The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic method emulating the cooperative behavior of mongooses during food searches. The DMO algorithm effectively addresses various optimization challenges across multiple domains by balancing global and local searches, resulting in near-optimal solutions. Numerous DMO variants have been developed since its inception. A comprehensive survey of recent DMO research from 2022 to August 2024 is provided in this study, beginning with the natural inspiration and conceptual framework of the DMO. It then explores various modifications, hybridizations, and algorithm applications across different fields. Lastly, a meta-analysis of DMO advancements and potential directions for further research are provided.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100551"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Analytics Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772662225000074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic method emulating the cooperative behavior of mongooses during food searches. The DMO algorithm effectively addresses various optimization challenges across multiple domains by balancing global and local searches, resulting in near-optimal solutions. Numerous DMO variants have been developed since its inception. A comprehensive survey of recent DMO research from 2022 to August 2024 is provided in this study, beginning with the natural inspiration and conceptual framework of the DMO. It then explores various modifications, hybridizations, and algorithm applications across different fields. Lastly, a meta-analysis of DMO advancements and potential directions for further research are provided.
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