Dwarf Mongoose Optimization Algorithm

doi:10.1016/j.cma.2022.114570

2022-03-01

549

PRE

Jeffrey O. Agushaka

Absalom E. Ezugwu

Laith Abualigah *

DOI：10.1016/j.cma.2022.114570

原文链接

原文求助

摘要

En 中文

This paper proposes a new metaheuristic algorithm called dwarf mongoose optimization algorithm (DMO) to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The DMO mimics the foraging behavior of the dwarf mongoose. The restrictive mode of prey capture (feeding) has dramatically affected the mongooses' social behavior and ecological adaptations to compensate for efficient family nutrition. The compensatory behavioral adaptations of the mongoose are prey size, space utilization, group size, and food provisioning. Three social groups of the dwarf mongoose are used in the proposed algorithm, the alpha group, babysitters, and the scout group. The family forage as a unit, and the alpha female initiates foraging, determines the foraging path, the distance covered, and the sleeping mounds. A certain number of the mongoose population (usually a mixture of males and females) serve as the babysitters. They remain with the young until the group returns at midday or evening. The babysitters are exchanged for the first to forage with the group (exploitation phase). The dwarf mongooses do not build a nest for their young; they move them from one sleeping mound to another and do not return to the previously foraged site. The dwarf mongoose has adopted a seminomadic way of life in a territory large enough to support the entire group (exploration phase). The nomadic behavior prevents overexploitation of a particular area. It also ensures exploration of the whole territory because no previously visited sleeping mound is returned. The performance of the proposed DMO algorithm is compared with seven other algorithms to show its effectiveness in terms of different performance metrics and statistics. In most cases, the near-optimal solutions achieved by the DMO are better than the best solutions obtained by the current state-of-the-art algorithms. Matlab codes of DMO are available at https://www.mat hworks.com/matlabcentral/fileexchange/105125-dwarf-mongoose-optimization-algorithm. (c) 2022 Elsevier B.V. All rights reserved.

Keyword:

Dwarf Mongoose Optimization Algorithm

Metaheuristic

Nature-inspired algorithms

Global optimization

Engineering design problems