...-enabled design method outperformed an expected-utility-maximization approach that explicitly accounted for uncertainty in certain regions of the design space. Calvo et al. [ 10 ] introduced a novel heuristic-enabled multi-objective optimization approach to tackle a protein structure prediction problem...

...Homero Valladares; Andres Tovar The solution to global (a posteriori) multi-objective optimization problems traditionally relies on population-based algorithms, which are very effective in generating a Pareto front. Unfortunately, due to the high number of function evaluations, these methods...

... with a multi-objective optimization process. To this end, first, a parametric model for a ring-shaped fourfold origami structure, called the Miura-oRing metastructure, is developed based on appropriate geometric parameters. Its nonlinear folding process is then simulated according to geometric compatibility...

... computationally intensive procedures. To speed up the optimization process and deal with expensive constraints, a new dimension selection-based constrained multi-objective optimization (MOO) algorithm is developed combining least absolute shrinkage and selection operator (LASSO) regression, artificial neural...

...-geometry. This is formulated as a multi-objective optimization problem (MOOP) where the obtained optimal ease-off is guaranteed to be manufacturable. To this end, an original strategy is proposed where the search for the pinion optimal tooth surface happens in the space of the coefficients of a polynomial...

... synthesis multi-objective optimization A common challenge in mechanical system design is the kinematic synthesis of mechanisms to meet a series of motion tasks. Motion tasks are usually divided into three categories: guiding the rigid body through a series of specified positions and directions...

... author. Email: rmalak@tamu.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 15 06 2022 20 01 2023 23 01 2023 17 02 2023 design optimization metamodeling multi-objective optimization Bayesian...

..., there is limited usage of Bayesian frameworks on problems of multi-objective optimization under interval uncertainty. This article seeks to formulate an approach for the solution of these problems via the expected improvement Bayesian acquisition function. In this paper, a method is developed for iteratively...

...Yin Liu; Shuo Wang; Kunpeng Li; Wei Sun; Xueguan Song Most practical multi-objective optimization problems are often characterized by two or more expensive and conflicting objectives, which require time-consuming simulations. Commonly used algorithms construct a surrogate model of each objective...

...: design optimization multi-objective optimization simulation-based design topology optimization Topology optimization (TO) has been a topic of research in the structural mechanics field for several decades. Introduced by Bendsøe and Kikuchi [ 1 ], this technique is commonly used to optimize...

... and machine functionality. This modeling approach facilitates the allocation of system functions to humans and machines to be used as an accessible baseline for concept development. Further, a multi-objective optimization model was developed to allocate functions with metrics for accommodation and cost...

... by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 07 04 2021 26 07 2021 28 07 2021 01 10 2021 design optimization metamodeling multi-objective optimization uncertainty analysis Agentschap voor Innovatie door...

...-based and manufacturing constraints. Finally, the model accuracy with frequentist, Bayesian and without MLR-based calibration are compared to true Pareto solutions. Bayesian optimization surrogate modeling multi-objective optimization weighted Tchebycheff Bayesian regression metamodeling stress...

...George H. Cheng; G. Gary Wang; Yeong-Maw Hwang Multi-objective optimization (MOO) problems with computationally expensive constraints are commonly seen in real-world engineering design. However, metamodel-based design optimization (MBDO) approaches for MOO are often not suitable for high...

... 05 2020 redundancy allocation problem multi-state system design evidential networks belief function theory evidential Pareto dominance multi-objective optimization reliability in design systems design uncertainty modeling Reliability is an important design characteristic...

.... The proposed approach is compared with some state-of-the-art metamodel-based multi-objective optimization approaches with four numerical examples and one engineering case. The results show that the proposed approach can obtain satisfactory Pareto solutions with significantly reduced computational cost...

... , “ Multi-Objective Optimization of Part-Building Orientation in Stereolithography ,” Rapid Prototyp. J. , 1 ( 4 ), pp. 12 – 23 . 10.1108/13552549510104429 [38] Singhal , S. K. , Jain , P. K. , Pandey , P. M. , and Nagpal , A. K. , 2009 , “ Optimum Part Deposition Orientation...

.... Parametric studies are conducted with respect to changes in land price. The present study is expected to help designers in creating optimized solar collectors based on specified requirements. compound parabolic concentrator solar collector system multi-objective optimization game theory...

... and Reliability Assessment Method for Efficient Probabilistic Design ,” ASME J. Mech. Des. , 126 ( 2 ), pp. 225 – 233 . 10.1115/1.1649968 70 Hamel , J. M. , and Azarm , S. , 2011 , “ Reducible Uncertain Interval Design by Kriging Metamodel Assisted Multi-Objective Optimization ,” ASME J...

... aspect was developed for small-size supersonic aircraft and successfully applied to the multi-objective optimization of a low sonic boom concept aircraft. References 1 Deremaux , Y. , 2009 , “ Why a Small Size Supersonic Transport Aircraft? Objectives and Trade-Offs ,” HISAC 2009...