An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

Ghareeb Moustafa (), Ali M. El-Rifaie (), Idris H. Smaili, Ahmed Ginidi, Abdullah M. Shaheen, Ahmed F. Youssef and Mohamed A. Tolba
Additional contact information
Ghareeb Moustafa: Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia
Ali M. El-Rifaie: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Idris H. Smaili: Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia
Ahmed Ginidi: Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt
Abdullah M. Shaheen: Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt
Ahmed F. Youssef: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Mohamed A. Tolba: Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo 11787, Egypt

Mathematics, 2023, vol. 11, issue 15, 1-26

Abstract: This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA’s core concept is inspired by the dwarf mongoose’s foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy.

Keywords: Dwarf Mongoose Optimization; alpha-directed Learning Strategy; benchmark functions; CEC 2017; heat and electrical power dispatch problem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/15/3297/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/15/3297/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:15:p:3297-:d:1203470

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().