Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where eva...

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Vydáno v:Journal of bionics engineering Ročník 20; číslo 3; s. 1263 - 1295
Hlavní autoři: Agushaka, Jeffrey O., Ezugwu, Absalom E., Olaide, Oyelade N., Akinola, Olatunji, Zitar, Raed Abu, Abualigah, Laith
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.05.2023
Springer Nature B.V
Springer Verlag
Témata:
Algorithms
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Computer Science
Constraints
Design engineering
Design optimization
Engineering
Exchanging
Food sources
Helogale parvula
Mounds
Operators (mathematics)
Performance measurement
Research Article
Search process
Statistical analysis
Unconstrained optimization
Engineering design problems
Nature-inspired algorithms
Improved dwarf mongoose
Constrained optimization
ISSN:1672-6529, 2543-2141
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Shrnutí:This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
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ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-022-00316-8