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This paper presents the development of an optimization and modeling method for the objective functions of output power, efficiency and weight of an outer rotor permanent magnet brushless DC (BLDC) motor based on radial basis function (RBF) approximation technique. The proposed RBF‐based Pareto optimization method requires less knowledge about electric/magnetic formulas and can replace conventional optimizations based on these equations with higher accuracy. To apply the proposed optimization method, the initial design should be developed using such equations. Therefore, RBFs are used to model and predict engine behavior. To optimize the objective functions, we used a genetic algorithm optimization technique with nonlinear electric and magnetic constraints to find the Pareto front set. The design obtained by the proposed radial basis function Pareto optimization (RBFPO) method was finally verified by Ansoft Maxwell. The results of optimal design using the RBFPO method have higher output power and efficiency. Also, in addition to the advantage of a favorable accuracy, RBF‐based models are significantly faster than models available in simulation tools. [ABSTRACT FROM AUTHOR]

Bald Eagle Search optimization (BES) is introduced recently, which mimics the bald eagles' hunting and food searching behaviour. The capability of a BES algorithm is enhanced in this paper by avoiding local optima stagnation and premature convergence problems. The BES algorithm is modified to enhance the performance of the algorithm and the modified algorithm is called the Self Adaptive Bald Eagle Search (SABES) algorithm. Dynamic‐opposite Learning (DOL) method is invoked in the initialization phase to increase the population diversity and convergence speed. To find better global solutions, the exploitation capability of the SBES algorithm is enhanced by considering the dynamic‐opposite solutions. In addition, the algorithmic parameter values of the BES algorithm have been determined using the linear and non‐linear time‐varying adaption strategy to create a balance between the search abilities which promotes the overall performance of the algorithm. The performance of the SABES algorithm is validated by comparing the results of 50 benchmark functions and CEC2017 functions with different erstwhile algorithms. The proposed algorithm achieves the best results in 80% of the benchmark functions, whereas the BES only gets the best results in 56% of functions. For the CEC2017, the SABES algorithm achieves optimal results for 20 functions which are highest in comparison to state‐of‐the‐art algorithms. The feasibility and effectiveness of the proposed algorithm are checked using the 15 CEC2020 competition real‐world single‐objective constrained optimization problems. The SABES achieves a 100% success rate and 100% feasibility rate in comparison to other well‐regarded algorithms. The statistical significance of the algorithm has been proved using Friedman's mean rank and Wilcoxon sign rank test. [ABSTRACT FROM AUTHOR]

This work proposed a new metaheuristic dubbed as Chaotic Lévy flight distribution (CLFD) algorithm, to address physical world engineering optimization problems that incorporate the chaotic maps in the elementary Lévy flight distribution (LFD). Hybridization aims to increase the LFD rate of convergence while also providing a problem‐free optimization approach. The proposed methodology is investigated for five case studies of constrained optimization issues followed by shape optimization of structural design. The outcomes from the CFLD algorithm are further contrasted with its fundamental version and other distinguished recently introduced algorithms. The computational analysis illustrates the dominance of CLFD over other considered optimizers. Moreover, the present investigation shows that CLFD is a robust technique that can efficiently find optimal mechanical design problems with a proper chaotic map selection. [ABSTRACT FROM AUTHOR]

The interconnection of multiple microgrids can effectively improve the operating efficiency of the system and reduce the cost of power generation. First, establish an economic environment optimal dispatch model based on the regional interconnected multi‐microgrids system, and formulate a dispatch strategy based on the time‐of‐use electricity price and the supply‐demand relationship between microgrids; Secondly, in order to solve this constrained optimization problem, a whale algorithm improved with five strategies including adaptive inertial weights, dynamic spiral search and generalized opposition‐based learning is proposed to obtain higher solution accuracy and anti‐premature convergence ability; Finally, combined 12 test functions to test the effectiveness of the improved algorithm, and use the improved algorithm to solve the example model containing two microgrids, and analyze it in two states of interconnection operation and non‐interconnection operation. The simulation results show that the regional interconnection operation of the microgrid under the established dispatching strategy can effectively reduce the overall operating cost of the system, and at the same time show the effectiveness of the improved algorithm to solve the optimal dispatching problem. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We propose a simple yet effective method to perform surface remeshing with hard constraints, such as bounding approximation errors and ensuring Delaunay conditions. The remeshing is formulated as a constrained optimization problem, where the variables contain the mesh connectivity and the mesh geometry. To solve it effectively, we adopt traditional local operations, including edge split, edge collapse, edge flip, and vertex relocation, to update the variables. Central to our method is an evolutionary vertex optimization algorithm, which is derivative‐free and robust. The feasibility and practicability of our method are demonstrated in two applications, including error‐bounded Delaunay mesh simplification and error‐bounded angle improvement with a given number of vertices, over many models. Compared to state‐of‐the‐art methods, our method achieves higher remeshing quality. [ABSTRACT FROM AUTHOR]

A one‐dimensional heterogeneous fixed‐bed methanol synthesis reactor model was developed for the optimization of carbon conversion and rate of methanol production. The developed model was validated both at pellet and reactor scales. The reactor inlet syngas composition and cooling water temperature, i.e., the design variables, providing the highest carbon conversion and rate of methanol production were determined by a constrained derivative‐free optimization algorithm. The results revealed that after optimization the carbon conversion was increased by 12 % and the rate of methanol production by 48 %. Under the optimized conditions, the stoichiometric number was in the range of industrial values. [ABSTRACT FROM AUTHOR]

For modern complex system, quality has become a serious and difficult problem that restricts the generation of combat effectiveness. Withal, based on domestic and foreign research and analysis, this paper puts forward a trade‐off method for complex system reliability, maintainability, supportability, testability (RMST) design, which is suitable for product design stage. First, aiming at the uncertain problem of complex system constrained optimization, the good‐point set population initialization, Deb feasibility comparison method and ε constraint method are integrated into the flower pollination algorithm, to construct the algorithm for RMST quotas design and trade‐off. Second, taking the typical series‐parallel repairable system as an example, one general model for complex system RMST quotas design and trade‐off based on effectiveness is constructed. Finally, take an unmanned nuclear radiation detection system as a typical case, to verify its feasibility, advantages, and disadvantages. [ABSTRACT FROM AUTHOR]

The distributed time‐varying constrained convex optimization problem over a connected undirected network topology is studied in this article. The proposed distributed optimization algorithm is composed of a projection map term, a consensus term, and a gradient term, and is feasible for any initial condition. We first show that the states of the optimization algorithm uniformly converge to a neighborhood of the constraint set while an approximate global consensus is achieved. Then the states are proven to uniformly converge to a neighborhood of the time‐varying optimal trajectory. The theoretical results are validated by some simulations. [ABSTRACT FROM AUTHOR]

A local optimization algorithm for solving the Kohn–Sham equations is presented. It is based on a direct minimization of the energy functional under the equality constraints representing the Grassmann Manifold. The algorithm does not require an eigendecomposition, which may be advantageous in large‐scale computations. It is optimized to reduce the number of Kohn–Sham matrix evaluations to one per iteration to be competitive with standard self‐consistent field (SCF) approach accelerated by direct inversion of the iterative subspace (DIIS). Numerical experiments include a comparison of the algorithm with DIIS. A high reliability of the algorithm is observed in configurations where SCF iterations fail to converge or find a wrong solution corresponding to a stationary point different from the global minimum. The local optimization algorithm itself does not guarantee that the found minimum is global. However, a randomization of the initial approximation shows a convergence to the right minimum in the vast majority of cases. [ABSTRACT FROM AUTHOR]

Purpose: FLASH radiotherapy (RT) can potentially reduce normal tissue toxicity while preserving tumoricidal effectiveness to improve the therapeutic ratio. The key of FLASH for sparing normal tissues is to irradiate tissues with an ultra‐high dose rate (i.e., ≥40 Gy/s), for which proton RT can be used. However, currently available treatment plan optimization method only optimizes the dose distribution and does not directly optimize the dose rate. The contribution of this work to FLASH proton RT is the development of a novel treatment optimization method, that is, simultaneous dose and dose rate optimization (SDDRO), to optimize tissue‐receiving dose rate distribution as well as dose distribution. Methods: Distinguished from existing methods, SDDRO accounts for dose rate constraint and optimizes dose rate distribution. In terms of mathematical formulation, SDDRO is a constrained optimization problem with dose‐volume constraint on dose distribution, minimum dose rate constraint on dose‐averaged tissue‐receiving dose rates, minimum monitor unit constraint on spot weight, and maximum intensity constraint on beam intensity. In terms of optimization algorithm, SDDRO is solved by iterative convex relaxation and alternating direction method of multipliers. SDDRO algorithms are presented for both scenarios with either constant or variable beam intensity. Results: SDDRO was compared with intensity modulated proton therapy (IMPT) (dose optimization alone, and no dose rate optimization) using three lung cases. SDDRO substantially improved the dose rate distribution compared to IMPT, for example, increasing of the region‐of‐interest (ROI) volume (ROI = CTV_10mm: the ring sandwiched by 10 mm outer and inner expansion of CTV boundary) receiving at least 40 Gy/s from ~30–50% to at least 98%, and the lung volume receiving at least 40 Gy/s from ~30–40% to ~70–90%. Moreover, both dose and dose rate distributions from SDDRO were further considerably improved via the combined use of hypofractionation and multiple beams. Conclusions: We have developed a joint dose and dose rate optimization method for FLASH proton RT, namely SDDRO, which is first‐of‐its‐kind to the best of our knowledge. The results suggest that (a) SDDRO can substantially improve the FLASH‐dose rate coverage (e.g., in terms of dose rate volume histogram) compared to IMPT for the purpose of normal tissue sparing while preserving the dose distribution and (b) the combination of hypofractionation and multiple beams can further considerably improve the SDDRO plan quality in terms of both dose and dose rate distribution. [ABSTRACT FROM AUTHOR]

The first‐order reliability method (FORM) is well recognized as an efficient approach for reliability analysis. Rooted in considering the reliability problem as a constrained optimization of a function, the traditional FORM makes use of gradient‐based optimization techniques to solve it. However, the gradient‐based optimization techniques may result in local convergence or even divergence for the highly nonlinear or high‐dimensional performance function. In this paper, a hybrid method combining the Salp Swarm Algorithm (SSA) and FORM is presented. In the proposed method, a Lagrangian objective function is constructed by the exterior penalty function method to facilitate meta‐heuristic optimization strategies. Then, SSA with strong global optimization ability for highly nonlinear and high‐dimensional problems is utilized to solve the Lagrangian objective function. In this regard, the proposed SSA‐FORM is able to overcome the limitations of FORM including local convergence and divergence. Finally, the accuracy and efficiency of the proposed SSA‐FORM are compared with two gradient‐based FORMs and several heuristic‐based FORMs through eight numerical examples. The results show that the proposed SSA‐FORM can be generally applied for reliability analysis involving low‐dimensional, high‐dimensional, and implicit performance functions. [ABSTRACT FROM AUTHOR]

The leaf economic traits such as leaf area, maximum carbon assimilation rate, and venation are all correlated and related to water availability. Furthermore, leaves are often broad and large in humid areas and narrower in arid/semiarid and hot and cold areas. We use optimization theory to explain these patterns. We have created a constrained optimization leaf model linking leaf shape to vein structure that is integrated into coupled transpiration and carbon assimilation processes. The model maximizes net leaf carbon gain (NPPleaf) over the loss of xylem water potential. Modeled relations between leaf traits are consistent with empirically observed patterns. As the results of the leaf shape–venation relation, our model further predicts that a broadleaf has overall higher NPPleaf compared to a narrowleaf. In addition, a broadleaf has a lower stomatal resistance compared to a narrowleaf under the same level of constraint. With the same leaf area, a broadleaf will have, on average, larger conduits and lower total leaf xylem resistance and thus be more efficient in water transportation but less resistant to cavitation. By linking venation structure to leaf shape and using water potential as the constraint, our model provides a physical explanation for the general pattern of the covariance of leaf traits through the safety–efficiency trade‐off of leaf hydraulic design. [ABSTRACT FROM AUTHOR]

Purpose: To develop and implement a fully automated approach to intensity modulated radiation therapy (IMRT) treatment planning. Method: The optimization algorithm is developed based on a hierarchical constrained optimization technique and is referred internally at our institution as expedited constrained hierarchical optimization (ECHO). Beamlet contributions to regions‐of‐interest are precomputed and captured in the influence matrix. Planning goals are of two classes: hard constraints that are strictly enforced from the first step (e.g., maximum dose to spinal cord), and desirable goals that are sequentially introduced in three constrained optimization problems (better planning target volume (PTV) coverage, lower organ at risk (OAR) doses, and smoother fluence map). After solving the optimization problems using external commercial optimization engines, the optimal fluence map is imported into an FDA‐approved treatment planning system (TPS) for leaf sequencing and accurate full dose calculation. The dose‐discrepancy between the optimization and TPS dose calculation is then calculated and incorporated into optimization by a novel dose correction loop technique using Lagrange multipliers. The correction loop incorporates the leaf sequencing and scattering effects into optimization to improve the plan quality and reduce the calculation time. The resultant optimal fluence map is again imported into TPS for leaf sequencing and final dose calculation for plan evaluation and delivery. The workflow is automated using application program interface (API) scripting, requiring user interaction solely to prepare the contours and beam arrangement prior to launching the ECHO plug‐in from the TPS. For each site, parameters and objective functions are chosen to represent clinical priorities. The first site chosen for clinical implementation was metastatic paraspinal lesions treated with stereotactic body radiotherapy (SBRT). As a first step, 75 ECHO paraspinal plans were generated retrospectively and compared with clinically treated plans generated by planners using VMAT (volumetric modulated arc therapy) with 4 to 6 partial arcs. Subsequently, clinical deployment began in April, 2017. Results: In retrospective study, ECHO plans were found to be dosimetrically superior with respect to tumor coverage, plan conformity, and OAR sparing. For example, the average PTV D95%, cord and esophagus max doses, and Paddick Conformity Index were improved, respectively, by 1%, 6%, 14%, and 15%, at a negligible 3% cost of the average skin D10cc dose. Conclusion: Hierarchical constrained optimization is a powerful and flexible tool for automated IMRT treatment planning. The dosimetric correction step accurately accounts for detailed dosimetric multileaf collimator and scattering effects. The system produces high‐quality, Pareto optimal plans and avoids the time‐consuming trial‐and‐error planning process. [ABSTRACT FROM AUTHOR]

The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal‐to‐noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data‐driven user‐constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non‐hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non‐hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two‐layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal‐to‐noise ratio compared to the raw stacks. The results for the normal moveout, non‐hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we present a robust actuator fault-tolerant control strategy for constrained linear systems in the presence of bounded state and input disturbances. The scheme is based on a bank of state estimators that match different fault situations that can occur in the system. A fault detection and isolation unit verifies that suitable residual variables lie inside pre-computed sets and selects the estimate that matches the current plant behaviour. A bank of robustly stabilizing tube-based model predictive control laws is designed, each associated to a fault scenario, and the appropriate controller is selected among them by using the information provided by the fault detection and isolation module. By means of 'tubes' of trajectories, we ensure robust closed-loop exponential stability of the constrained system and good performance in the fault-free case and under the occurrence of abrupt actuator faults, including actuator outage and loss of effectiveness by an unknown amount. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose: To explore the potential of a novel dose-volume based metric to assist in the selection of optimal fractionation schedules for lung cancer patients. Methods: Selecting the dose per fraction that maximizes the therapeutic ratio via a linear-quadratic effect on normal tissue complication probability and tumor cell survival is an optimization problem. The mathematical solution reveals that the optimal fractionation schedule is determined by a generalized dose ratio between the normal tissue and the tumor, here termed the bifurcation number B, that can be derived from the dose-volume histogram of the normal tissue. The bifurcation number characterizes the volume effect of a normal tissue and its dependency on the fractionation schedule. The clinical relevance of the bifurcation number was evaluated in 46 patients previously treated for nonsmall cell lung cancer (NSCLC) according to various fractionation protocols. Bifurcation numbers were computed for both lung and esophagus as the normal tissues. Results: The value of the bifurcation number determines whether the volume effect reverses the traditional radiobiological advantage of small dose per fraction for the normal tissue. If B is smaller than the ratio of alpha/beta ratios between normal tissue and tumor, then a single fraction is optimal; otherwise the optimal treatment is an infinite number of doses (hence the name 'bifurcation' number). These fractionation schedules correspond clinically to hypo- and standard/hyperfractionation, respectively. Compared with traditional dose-volume metrics, the bifurcation number is a unitless ratio and independent of dose fractionation. The B-numbers derived from the clinical treatment plans are also strongly consistent with historically prescribed clinical fractionation protocols for NSCLC treatments. The B-numbers for esophagus and lung for all patients receiving a high dose per fraction protocol (>7.5 Gy/fraction) were all smaller than the B-numbers for the patients receiving standard 2 Gy/fraction, with the numbers for the 3 Gy/fraction group in between. Conclusions: The bifurcation numbers are strongly consistent with prescribed clinical fractionation protocols for NSCLC treatments. Due to their scale-free property the B-numbers may assist in the selection of an appropriate fractionation once the dose distribution has been optimized. [ABSTRACT FROM AUTHOR]

This work describes a topology optimization framework to design the material distribution of functionally graded structures considering mechanical stress constraints. The problem of interest consists in minimizing the volumetric density of a material phase subjected to a global stress constraint. Due to the existence of microstructure, the micro-level stress is considered, which is computed by means of a mechanical concentration factor using a p-norm of the Von Mises stress criterium (applied to the micro-level stress). Because a 0–1 (void–solid) material distribution is not being sought, the singularity phenomenon of stress constraint does not occur as long as the material at any point of the medium does not vanish and it varies smoothly between material 1 and material 2. To design a smoothly graded material distribution, a material model based on a non-linear interpolation of the Hashin–Strikhman upper and lower bounds is considered. Consistently with the framework adopted here in, the so-called ‘continuous approximation of material distribution’ approach is employed, which considers a continuous distribution of the design variable inside the finite element. As examples, the designs of functionally graded disks subjected to centrifugal body force are presented. The method generates smooth material distributions, which are able to satisfy the stress constraint. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Noise suppression in multichannel data sets, such as color images, has drawn much attention in the last few years. An issue of paramount importance in designing color image filters is the determination of the coefficients that should be used to weight the inputs to the filter. In this study, we propose an evolutionary computation-based approach to select and optimize the coefficients in the class of weighted vector directional filters. Using a genetic algorithm, we were able to adapt the filter weights to match varying image and noise characteristics. Extended experimentation with realistic image processing applications, including television image enhancement and virtual restoration of artworks, indicates that the proposed filters are capable of removing noise while preserving chromaticity information, edges, and fine details, as well as structural image content. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 236–251, 2005 [ABSTRACT FROM AUTHOR]

Preconditioning methods for matrices on saddle point form, as typically arising in equality constrained optimization problems, are surveyed. Special consideration is given to two methods: a nearly symmetric block incomplete factorization preconditioning method and a preconditioner on the same saddle point form as the given matrix. Both methods result in eigenvalues with positive real parts and small or zero imaginary parts. The behaviour of the methods are illustrated by solving a regularized Stokes problem. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

There is a growing research interest in quantifying blood flow distribution for the entire cerebral circulation to sharpen diagnosis and improve treatment options for cerebrovascular disease of individual patients. We present a methodology to reconstruct subject‐specific cerebral blood flow patterns in accordance with physiological and fluid mechanical principles and optimally informed by in vivo neuroimage data of cerebrovascular anatomy and arterial blood flow rates. We propose an inverse problem to infer blood flow distribution across the visible portion of the arterial network that best matches subject‐specific anatomy and a given set of volumetric flow measurements. The optimization technique also mitigates the effect of uncertainties by reconciling incomplete flow data and by dissipating unavoidable acquisition errors associated with medical imaging data. [ABSTRACT FROM AUTHOR]

Numerical optimization of geometry parameters is a critical stage of the design process of compact antennas. It is also challenging because size reduction is constrained by the necessity of fulfilling imposed electrical performance requirements. Furthermore, full‐wave electromagnetic (EM) analysis needs to be used for reliable performance evaluation of the antenna structure, which is computationally expensive. In this paper, an improved procedure for optimization‐driven design miniaturization of antenna structures is considered. We investigate the properties of explicit formulation with the footprint area minimization being a primary design objective and a penalty function introduced to control reflection characteristic of the antenna at hand. Deficiencies of this approach are pointed out, in particular, difficulties in exploring the boundary of the feasible part of the design space (otherwise necessary due to the fact that the reflection constraint is normally active at the minimum size antenna design). A novel formulation with adaptive adjustment of the maximum acceptable in‐band reflection level threshold is proposed and demonstrated to be superior over the conventional (fixed threshold) setup, ie, leading to smaller footprints without degrading electrical performance parameters. Our considerations are illustrated using 2 broadband antenna structures. [ABSTRACT FROM AUTHOR]

Uncertain influences are inherent in many practical processes, stemming for instance from measurement errors in process optimization or missing knowledge about the future behavior of a given system, e.g., the financial markets. Therefore, it is desirable to explicitly consider uncertainties when optimizing such processes. One possible approach for this task is the usage of chance constrained optimization (CCOPT), which allows that the constraints are only held with a certain probability level. This enables the user to make a compromised decision between reliability and profitability. Solving CCOPT problems usually consists of two steps. First, transforming the chance constraints into deterministic ones and then solving the transformed problem with a standard NLP solver. Depending on the underlying process, several approaches exist for the transformation. Here, we investigate the usage of so called analytical approximations (AAs). In general, AA approaches do not lead to an exact deterministic representation of CCOPT problems. Nonetheless, it can be shown that a solution obtained by AA is always feasible for the original problem. Furthermore, the corresponding optimization problem generated by AA is computationally more tractable than the original formulation, making these approaches interesting for larger scale processes. We propose a new AA method and present a comparison with existing AA approaches, considering efficiency and suitability. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Purpose: The authors present the application of the reduced order constrained optimization (ROCO) method, previously successfully applied to the prostate and lung sites, to the head-and-neck (H&N) site, demonstrating that it can quickly and automatically generate clinically competitive IMRT plans. We provide guidelines for applying ROCO to larynx, oropharynx, and nasopharynx cases, and report the results of a live experiment that demonstrates how an expert planner can save several hours of trial-and-error interaction using the proposed approach. Methods: The ROCO method used for H&N IMRT planning consists of three major steps. First, the intensity space of treatment plans is sampled by solving a series of unconstrained optimization problems with a parameter range based on previously treated patient data. Second, the dominant modes in the intensity space are estimated by dimensionality reduction using principal component analysis (PCA). Third, a constrained optimization problem over this basis is quickly solved to find an IMRT plan that meets organ-at-risk (OAR) and target coverage constraints. The quality of the plan is assessed using evaluation tools within Memorial Sloan-Kettering Cancer Center (MSKCC)'s treatment planning system (TPS). Results: The authors generated ten H&N IMRT plans for previously treated patients using the ROCO method and processed them for deliverability by a dynamic multileaf collimator (DMLC). The authors quantitatively compared the ROCO plans to the previously achieved clinical plans using the TPS tools used at MSKCC, including DVH and isodose contour analysis, and concluded that the ROCO plans would be clinically acceptable. In our current implementation, ROCO H&N plans can be generated using about 1.6 h of offline computation followed by 5-15 min of semiautomatic planning time. Additionally, the authors conducted a live session for a plan designated by MSKCC performed together with an expert H&N planner. A technical assistant set up the first two steps, which were performed without further human interaction, and then collaborated in a virtual meeting with the expert planner to perform the third (constrained optimization) step. The expert planner performed in-depth analysis of the resulting ROCO plan and deemed it to be clinically acceptable and in some aspects superior to the clinical plan. This entire process took 135 min including two constrained optimization runs, in comparison to the estimated 4 h that would have been required using traditional clinical planning tools. Conclusions: The H&N site is very challenging for IMRT planning, due to several levels of prescription and a large, variable number (6-20) of OARs that depend on the location of the tumor. ROCO for H&N shows promise in generating clinically acceptable plans both more quickly and with substantially less human interaction. [ABSTRACT FROM AUTHOR]