Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper propos...

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Bibliographic Details
Published in:Energies (Basel) Vol. 16; no. 5; p. 2409
Main Authors: Mahdy, Araby, Shaheen, Abdullah, El-Sehiemy, Ragab, Ginidi, Ahmed, Al-Gahtani, Saad F.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2023
Subjects:
Algorithms
Energy consumption
Engineering
Genetic algorithms
grey wolf algorithm
Mathematical optimization
maximizing the operating force
minimizing the flux saturation
multi-objective shape design of tubular linear synchronous motor
Optimization algorithms
Optimization techniques
pareto archive
Pareto optimum
Physics
Social structure
Egypt
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective Grey Wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination Algorithm (MFPA), Multi-objective Lichtenberg Algorithm (MOLA), and Multi-objective Grasshopper Optimization Algorithm (MGOA). The outcomes illustrate that the developed MGWO provides an average improvement of 73.46%, 19.07%, and 15.15% compared to MFPA, MOLA, and MGOA, respectively. The validation of the developed MGWO is extended for a multi-objective form of welded beam design (WBD) by simultaneously minimizing the deflection and the manufacturing costs. Similar findings are obtained with different reference points, the developed MGWO provides an average improvement of 2.8%, 0.7%, and 3.04% compared to MFPA, MOLA, and MGOA, respectively.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en16052409