Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study

Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-domin...

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Veröffentlicht in:Structural and multidisciplinary optimization Jg. 66; H. 8; S. 191
Hauptverfasser: Meng, Zeng, Yıldız, Betül Sultan, Li, Gang, Zhong, Changting, Mirjalili, Seyedali, Yildiz, Ali Riza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Schlagworte:
Adaptive control
Comparative studies
Computational Mathematics and Numerical Analysis
Control methods
Design optimization
Engineering
Engineering Design
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic methods
Machine learning
Multiple objective analysis
Particle swarm optimization
Reliability
Research Paper
Sorting algorithms
Systems design
Theoretical and Applied Mechanics
Multiobjective
Probabilistic constraint
Reliability-based design optimization
Metaheuristic algorithm
Evolutionary algorithm
ISSN:1615-147X, 1615-1488
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Zusammenfassung:Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective gray wolf optimizer, multiobjective multiverse optimization, multiobjective water cycle optimizer, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimization, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimization algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the RBDO field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective multiobjective RBDO algorithm that assists the complex engineering system design.
Bibliographie:ObjectType-Article-1
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-023-03639-0