A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions
Olanrewaju Lawrence Abraham, Md Asri Ngadi · Decision Analytics Journal · 2025
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. • Demonstrate the Dwarf Mongoose Optimization (DMO) algorithm effectively balances global and local search strategies for optimal problem-solving. • Explore the key modifications, hybridizations, and diverse applications of the DMO algorithm. • Provide a meta-analysis of the advancements of the DMO algorithm and future research directions.