An improved multidisciplinary feasible method using DACE approximation approach

Multidisciplinary feasible method (MDF) is conventional method to multidisciplinary optimization (MDO) and well-understood by users. It reduces the dimensions of the multidisciplinary optimization problem by using the design variables as independent optimization variables. However, at each iteration of the conventional optimization procedure, multidisciplinary analysis (MDA) is numerously performed that results in extreme expense and low optimization efficiency. The intrinsic weakness of MDF is due to the times that it loop fixed-point iterations in MDA, which drive us to improve MDF by building inexpensive approximations as surrogates for expensive MDA. An simple example is presented to demonstrate the usefulness of the improved MDF. Results show that a significant reduction in the number of multidisciplinary analysis required for optimization is obtained as compared with original MDF and the efficiency of optimization is increased.

A SUPERLINEARLY CONVERGENT SPLITTING FEASIBLE SEQUENTIAL QUADRATIC OPTIMIZATION METHOD FOR TWO-BLOCK LARGE-SCALE SMOOTH OPTIMIZATION

This paper discusses the two-block large-scale nonconvex optimization problem with general linear constraints.Based on the ideas of splitting and sequential quadratic optimization(SQO),a new feasible descent method for the discussed problem is proposed.First,we consider the problem of quadratic optimal(QO)approximation associated with the current feasible iteration point,and we split the QO into two small-scale QOs which can be solved in parallel.Second,a feasible descent direction for the problem is obtained and a new SQO-type method is proposed,namely,splitting feasible SQO(SF-SQO)method.Moreover,under suitable conditions,we analyse the global convergence,strong convergence and rate of superlinear convergence of the SF-SQO method.Finally,preliminary numerical experiments regarding the economic dispatch of a power system are carried out,and these show that the SF-SQO method is promising.

A SUPERLINEARLY AND QUADRATICALLY CONVERGENT SQP TYPE FEASIBLE METHOD FOR CONSTRAINED OPTIMIZATION

A new SQP type feasible method for inequality constrained optimization is presented,it is a combination of a master algorithm and an auxiliary algorithm which is taken only in finite iterations.The directions of the master algorithm are generated by only one quadratic programming, and its step\|size is always one, the directions of the auxiliary algorithm are new “second\|order” feasible descent. Under suitable assumptions,the algorithm is proved to possess global and strong convergence, superlinear and quadratic convergence.

EFFICIENT METHOD FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION BY CONSIDERING UNCERTAINTY

A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)applications.Edge devices enable limited computational capacity and energy availability that hamper end user performance.We designed a novel performance measurement index to gauge a device’s resource capacity.This examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services.In this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions.Consequently,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the network.Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation factor.Our system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

A Feasible Point Method with Bundle Modification for Nonsmooth Convex Constrained Optimization

In this paper, a bundle modification strategy is proposed for nonsmooth convex constrained min- imization problems. As a result, a new feasible point bundle method is presented by applying this strategy. Whenever the stability center is updated, some points in the bundle will be substituted by new ones which have lower objective values and/or constraint values, aiming at getting a better bundle. The method generates feasible serious iterates on which the objective function is monotonically decreasing. Global convergence of the algorithm is established, and some preliminary numerical results show that our method performs better than the standard feasible point bundle method.

An Improved Feasible QP-free Algorithm for Inequality Constrained Optimization

In this paper, an improved feasible QP-free method is proposed to solve nonlinear inequality constrained optimization problems. Here, a new modified method is presented to obtain the revised feasible descent direction. In view of the computational cost, the most attractive feature of the new algorithm is that only one system of linear equations is required to obtain the revised feasible descent direction. Thereby, per single iteration, it is only necessary to solve three systems of linear equations with the same coefficient matrix. In particular, without the positive definiteness assumption on the Hessian estimate, the proposed algorithm is still global convergence. Under some suitable conditions, the superlinear convergence rate is obtained.

A feasible method for comparing the power dependent photostability of fluorescent proteins

A feasible method of combining the concept of fluorescence half-life and the power dependent photo- bleaching rate for characterizing the practical photostability of fluorescent proteins （FPs） was introduced. Furthermore, by using a fluorescent photostability standard, a relative comparison of the photostabilty of FPs from different research groups was proposed, which would be of great benefit for developing novel FPs with optimized emission wavelength, better brightness, and improved photostability. We used rho- damine B as an example to verify this method and evaluate the practical photostability of a far-red FP, mKate-S158C. Experimental results indicated good potential of this method for further study.

A Feasible Decomposition Method for Constrained Equations and Its Application to Complementarity Problems

In this paper, by analyzing the propositions of solution of the convex quadratic programming with nonnegative constraints, we propose a feasible decomposition method for constrained equations. Under mild conditions, the global convergence can be obtained. The method is applied to the complementary problems. Numerical results are also given to show the efficiency of the proposed method.

A Fastly Convergent Directly Feasible Directions Method for Nonlinearly Inequality Constrained Optimization

In this paper, a new superlinearly convergent algorithm for nonlinearly constrained optimization problems is presented. The search directions are directly computed by a few formulas, and neither quadratic programming nor linear equation need to be sovled. Under mild assumptions, the new algorithm is shown to possess global and superlinear convergence.

A GEOMETRICAL INTERPRETATION OF THE WITHOUT-EXCEPTION FEASIBILITY OF PL HOMOTOPY METHODS

PL homotopy metheds are effective methods to locate zerces(or fixed points) of highly nonlinearmappirgs. Due to the Jexicographical system, the methods are feasible without exceptions Thispaper presents a geemetrical interpretation of the without-exception feasibility.

Sludge-based biochar adsorbent:pore tuning mechanisms,challenges,and role in carbon sequestration

Sludge biochar,a carbonized product of raw sludge,contains porous architectures that can act as epicenters for adsorbing external molecules through physical or chemical bonding.Sludge biochar also immobilizes innate micropollutants,which is advantageous over conventional sludge disposal methods.To date,numerous strategies have been discovered to improve sludge biochar morphology,but the influential factors,pore tuning mechanisms,and process feasibility remain imprecise.This knowledge gap limits our ability to design a robust sludge-based biochar.Herein,we present state-of-the-art sludge biochar synthesis methods with insight into structural and chemical transformation mechanisms.Roadblocks and novel concepts for improving sludge biochar porous architecture are highlighted.For the first time,sludge biochar properties,adsorption performances,and techno-economic perspectives were compared with commercial activated carbon(AC)to reveal the precise challenges in sludge biochar application.More importantly,sludge biochar role in carbon sequestration is detailed to demonstrate the environmental significance of this technology.Eventually,the review concludes with an overview of prospects and an outlook for developing sludge biochar-based research.

A Pragmatic Observational Feasibility Study on Integrated Treatment for Musculoskeletal Disorders：Design and Protocol

Background：Musculoskeletal disorders（MSD） comprise a wide range of conditions,associated with an enormous pain and impaired mobility,and are affecting people＇s lives and work.Management of musculoskeletal disorders typically involves a multidisciplinary team approach.Positive findings have been found in previous studies evaluating the effectiveness of complementary therapies,though little attention has been paid to evaluating of the effectiveness of integrated packages of care combining conventional and complementary approaches for musculoskeletal conditions in a National Health Service（NHS） setting.Objective：To determine the feasibility of all aspects of a pragmatic observational study designed：（1） to evaluate the effectiveness and cost effectiveness of integrated treatments for MSDs in an integrated NHS hospital in the UK;（2） to determine the acceptability of the study design and research process to patients;（3） to explore patients＇ expectation and experience of receiving integrated treatments.Methods：This is an observational feasibility study,with 1-year recruitment and 1-year follow-up,conducted in Royal London Hospital for Integrated Medicine,University College London Hospital Trust,UK.All eligible patients with MSDs newly referred to the hospital were included in the study.Interventions are integrated packages of care（conventional and complementary） as currently provided in the hospital.SF-36 Health Survey,short form Brief Pain Inventory,Visual Analogue Scale,and modified Client Service Receipt Inventory will be assessed at 4/5 time points.Semi-structured interview/focus group will be carried out before treatment,and 1 year after commence of treatment.Discussion：We intend to conduct a pragmatic observational study of integrated medical treatment of MSDs at a public sector hospital.It will inform the design of a future trial including recruitment,retention,suitability of the outcome measures and patients experiences.

AN EFFECTIVE CONTINUOUS ALGORITHM FOR APPROXIMATE SOLUTIONS OF LARGE SCALE MAX-CUT PROBLEMS

An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discrete constraints in the original problem with one single continuous constraint. A feasible direction method is designed to solve the resulting nonlinear programming problem. The method employs only the gradient evaluations of the objective function, and no any matrix calculations and no line searches are required. This greatly reduces the calculation cost of the method, and is suitable for the solution of large size max-cut problems. The convergence properties of the proposed method to KKT points of the nonlinear programming are analyzed. If the solution obtained by the proposed method is a global solution of the nonlinear programming problem, the solution will provide an upper bound on the max-cut value. Then an approximate solution to the max-cut problem is generated from the solution of the nonlinear programming and provides a lower bound on the max-cut value. Numerical experiments and comparisons on some max-cut test problems （small and large size） show that the proposed algorithm is efficient to get the exact solutions for all small test problems andwell satisfied solutions for most of the large size test problems with less calculation costs.

A Wide Neighborhood Interior-Point Algorithm for Convex Quadratic Semidefinite Optimization

In this paper,we propose an interior-point algorithm based on a wide neighborhood for convex quadratic semidefinite optimization problems.Using the Nesterov–Todd direction as the search direction,we prove the convergence analysis and obtain the polynomial complexity bound of the proposed algorithm.Although the algorithm belongs to the class of large-step interior-point algorithms,its complexity coincides with the best iteration bound for short-step interior-point algorithms.The algorithm is also implemented to demonstrate that it is efficient.