Dwarf Mongoose Optimization Algorithm

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...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 391; S. 114570
Hauptverfasser:	Agushaka, Jeffrey O., Ezugwu, Absalom E., Abualigah, Laith
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.03.2022 Elsevier BV
Schlagworte:	Algorithms Continuity (mathematics) Dwarf Mongoose Optimization Algorithm Engineering design problems Forage Foraging behavior Global optimization Heuristic methods Metaheuristic Nature-inspired algorithms Nutrition Optimization Optimization algorithms Performance measurement Provisioning Dwarf Mongoose Optimization Algorithm Metaheuristic Global optimization Engineering design problems Nature-inspired algorithms
ISSN:	0045-7825
Online-Zugang:	Volltext
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Abstract	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.mathworks.com/matlabcentral/fileexchange/105125-dwarf-mongoose-optimization-algorithm.
AbstractList	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.mathworks.com/matlabcentral/fileexchange/105125-dwarf-mongoose-optimization-algorithm.
ArticleNumber	114570
Author	Abualigah, Laith Ezugwu, Absalom E. Agushaka, Jeffrey O.
Author_xml	– sequence: 1 givenname: Jeffrey O. surname: Agushaka fullname: Agushaka, Jeffrey O. email: 208088307@stu.ukzn.ac.za organization: School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, KwaZulu-Natal 3201, South Africa – sequence: 2 givenname: Absalom E. orcidid: 0000-0002-3721-3400 surname: Ezugwu fullname: Ezugwu, Absalom E. email: Ezugwua@ukzn.ac.za organization: School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, KwaZulu-Natal 3201, South Africa – sequence: 3 givenname: Laith orcidid: 0000-0002-2203-4549 surname: Abualigah fullname: Abualigah, Laith email: aligah.2020@gmail.com organization: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
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Snippet	This paper proposes a new metaheuristic algorithm called dwarf mongoose optimization algorithm (DMO) to solve the classical and CEC 2020 benchmark functions...
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SubjectTerms	Algorithms Continuity (mathematics) Dwarf Mongoose Optimization Algorithm Engineering design problems Forage Foraging behavior Global optimization Heuristic methods Metaheuristic Nature-inspired algorithms Nutrition Optimization Optimization algorithms Performance measurement Provisioning
Title	Dwarf Mongoose Optimization Algorithm
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