Abstract
To improve the accuracy and efficiency of multiobjective design optimization for a multicomponent system with complex nonuniform loads, an efficient surrogate model (the decomposed collaborative optimized Kriging model, DCOKM) and an accurate optimal algorithm (the dynamic multiobjective genetic algorithm, DMOGA) are presented in this study. Furthermore, by combining DCOKM and DMOGA, the corresponding multiobjective design optimization framework for the multicomponent system is developed. The multiobjective optimization design of the carrier roller system is considered as a study case to verify the developed approach with respect to multidirectional nonuniform loads. We find that the total standard deviation of three carrier rollers is reduced by 92%, where the loading distribution is more uniform after optimization. This study then compares surrogate models (response surface model, Kriging model, OKM, and DCOKM) and optimal algorithms (neighbourhood cultivation genetic algorithm, nondominated sorting genetic algorithm, archive microgenetic algorithm, and DMOGA). The comparison results demonstrate that the proposed multiobjective design optimization framework is demonstrated to hold advantages in efficiency and accuracy for multiobjective optimization.
Highlights
Multicomponent system is defined as the complex mechanism system comprising a plurality of rigid and flexible components, which is an indispensable part in mechanical equipment, such as excavator and loader [1,2,3]
Suffers from nonuniform loads caused by complex structural layout and stricter working environment, and the failure of one component will lead to the failure of the whole component system, which significantly increases the failure possibility of the multicomponent system
To improve multiobjective design optimization (MODO) accuracy and efficiency of the complex multicomponent system, this study presents two key techniques: (1) establishment of a numerical surrogate model to calculate the multiobjective and multiconstraint; (2) development of a dynamic multiobjective algorithm to resolve the MODO model
Summary
Multicomponent system is defined as the complex mechanism system comprising a plurality of rigid and flexible components, which is an indispensable part in mechanical equipment, such as excavator and loader [1,2,3]. To improve MODO accuracy and efficiency of the complex multicomponent system, this study presents two key techniques: (1) establishment of a numerical surrogate model to calculate the multiobjective and multiconstraint; (2) development of a dynamic multiobjective algorithm to resolve the MODO model. Because of easy to fall into local solutions or premature convergence, the current NSGA-II algorithm is still difficult to acquire global optimal solution of the multicomponent system [41, 42] In this case, by designing an arithmetic crossover operator and Poisson mutation operator in NSGA-II algorithm, a dynamic multiobjective genetic algorithm (DMOGA) is presented to accurately solve the MODO model and acquire nondominated solutions.
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