Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems

This paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective...

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Published in:Computer modeling in engineering & sciences Vol. 142; no. 2; pp. 2111 - 2145
Main Authors: Rosić, Maja, Rosić, Božidar, Sedak, Miloš
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2025
Subjects:
Algorithms
Design engineering
Design optimization
Evolutionary computation
Gearboxes
Genetic algorithms
Mechanical engineering
Mechanical engineering design
Multiple objective analysis
Mutation
Objectives
Optimization algorithms
Optimization techniques
Performance evaluation
ISSN:1526-1506, 1526-1492, 1526-1506
Online Access:Get full text
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Summary:This paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective optimization challenges within mechanical engineering, with a specific emphasis on planetary gearbox optimization. The algorithm is equipped with the ability to dynamically select the optimal mutation operator, contingent upon an adaptive normalized population spacing parameter. The efficacy of HMODESFO has been substantiated through rigorous validation against established industry benchmarks, including a suite of Zitzler-Deb-Thiele (ZDT) and Zeb-Thiele-Laumanns-Zitzler (DTLZ) problems, where it exhibited superior performance. The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods, particularly in tackling highly intricate multi-objective planetary gearbox optimization problems. Additionally, the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems, further accentuating its adeptness in resolving complex optimization challenges within this domain.
Bibliography:ObjectType-Article-1
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ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2025.059319