Background: There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious diseas...
详细信息
This paper proposes an innovative method for the tuberculosis (TB) model based on a hybrid technique which combines a feed-forward neural network (FFNN) with a Genetic Algorithm (GA) and sequentialquadratic Programmi...
详细信息
This paper proposes an innovative method for the tuberculosis (TB) model based on a hybrid technique which combines a feed-forward neural network (FFNN) with a Genetic Algorithm (GA) and sequential quadratic programming (SQP) methodologies. The algorithm's main optimizer is GA, while SQP is employed to fine-tune GA's outputs in order to boost assurance in the result. The TB model consists of five classes: susceptible individuals;latent carriers of TB who are unrecognized;individuals with active tuberculosis being treated at home;individuals with active tuberculosis who are being treated at a hospital;and recovered individuals. The nonlinear differential TB system is used to develop a log sigmoid fitness-based function employing mean squared error. The provided paradigm's stability, accuracy, and usefulness are compared using Adam's numerical technique and absolute error analysis. Furthermore, for repeated large algorithm runs, the convergence evaluations of mean absolute deviation (MAD), root mean square error (RMSE), and Theil's inequality coefficient (TIC) is conducted for each class of TB model. The algorithm's precision is demonstrated by the accuracy of convergence measures for MAD, RMSE, and TIC, which range from 3 to 14 decimal places. The value of the proposed approach-based stochastic algorithm is supported by the quantitative study's findings.
The general trend in the development of the automobile industry is toward lightweight vehicles, because weight has an important role in determining the performance and quality of vehicles. The body in white (BIW) refe...
详细信息
The general trend in the development of the automobile industry is toward lightweight vehicles, because weight has an important role in determining the performance and quality of vehicles. The body in white (BIW) refers to the stage in automotive design or automobile manufacturing in which a car body's sheet metal components have been welded together but before the moving parts (doors, hoods, deck lids, and fenders), motor, chassis sub-assemblies, and trim (glass, seats, upholstery, electronics, etc.) have been added and before painting. In this paper, we propose an approximate model optimization for the lightweight design of the lower body structure that is based on a sequential quadratic programming algorithm. The proposed comprehensive multi-objective optimization method is used to minimize the body weight without reducing the performance, i.e., the bending and torsion stiffness of the BIW. Firstly, in the conceptual design stage of the BIW, the load transfer path of the BIW is determined using topological technology. The load transfer path is used to guide the structural design and layout of the lower vehicle body. Then, combined with implicit parametric modeling technology, the full parametric BIW model is established, and the size, position, and thickness of the cross section of the lower vehicle body are determined by a multidisciplinary optimization method, and the initial design of the vehicle body weight is reduced. Secondly, the test scheme is designed, and the approximate model of the response surface takes the bending and torsional stiffnesses and the mass of the BIW into consideration. Thirdly, the sequential quadratic programming algorithm combined with the multidisciplinary collaborative optimization algorithm is used to carry out the multi-objective optimization design of the BIW structure and obtain the Pareto optimal solution set. Our results show that the proposed method reduces the weight of the vehicle lower body by 4.07 kg.
This paper addresses the optimization design problem of the Multimission Launcher (MML) system launch sequence, with a focus on the challenges of application in dynamic battlefield environments. The study proposes a S...
详细信息
This paper addresses the optimization design problem of the Multimission Launcher (MML) system launch sequence, with a focus on the challenges of application in dynamic battlefield environments. The study proposes a Sensitivity-Guided Multi-Stage sequential quadratic programming (SM-SQP) algorithm to overcome the limitations of traditional algorithms in adaptability, flexibility, and real-time performance. The SM-SQP algorithm combines sensitivity knowledge guidance with a multi-stage planning structure to adapt to information degradation conditions. Simulation results demonstrate that, compared to existing methods, the algorithm exhibits significant advantages in key performance indicators such as average time and ammunition consumption for destroying unit targets, as well as overall target destruction rate. These advantages are particularly evident in environments with interference and information degradation. This research not only provides a new perspective for optimizing the launch sequence of MML systems but also offers insights for addressing similar complex system optimization problems.
On the platform of general chemical process simulation software (it was named Optimization Engineer, OPEN), a general optimization algorithm for chemical process simulation is developed using C + + code. The algorithm...
详细信息
On the platform of general chemical process simulation software (it was named Optimization Engineer, OPEN), a general optimization algorithm for chemical process simulation is developed using C + + code. The algorithm is based on sequential quadratic programming (SQP). We adopt the activity set algorithm and the rotation axis algorithm to generate the activity set to solve the quadraticprogramming sub-problem. The active set method can simplify the number of constraints and speed up the calculation. At the same time, we used limited memory BFGS algorithm (L-BFGS) to simplify the solution of second derivative matrix. The special matrix storage mode of L-BFGS algorithm can save the storage space and speed up the computing efficiency. We use exact penalty function and traditional step-size rule in the algorithm. These two methods can ensure the convergence of the algorithm, a more correct search direction and suitable search step. The example shows that the advanced optimization function can meet the requirements of General Chemical Process Calculation. The number of iterations can reduce by about 6.0%. The computation time can reduce by about 6.5%. We combined this algorithm with chemical simulation technology to develop the optimization function of chemical engineering simulation. This optimization function can play an important role in the process optimization calculation aiming at energy saving and green production.
This paper discusses a kind of nonlinear inequality constrained optimization problems without any constraint qualification. A new sequential quadratic programming algorithm for such problems is proposed, whose importa...
详细信息
This paper discusses a kind of nonlinear inequality constrained optimization problems without any constraint qualification. A new sequential quadratic programming algorithm for such problems is proposed, whose important features are as follows: (i) a new relaxation technique for the linearized constraints of the quadraticprogramming subproblem is introduced, which guarantees that the subproblem is always consistent and generates a favourable search direction;(ii) a weaker positive-definiteness assumption on the quadratic coefficient matrices is presented;(iii) a slightly new line search is adopted, where neither a penalty function nor a filter is used;(iv) an associated acceptable termination rule is introduced;(v) the finite convergence of the algorithm is proved. Furthermore, the numerical results on a collection of CUTE test problems show that the proposed algorithm is promising.
This paper concerns the issue of asymptotic acceptance of the true Hessian and the full step by the sequential quadratic programming algorithm for equality-constrained optimization problems. In order to enforce global...
详细信息
This paper concerns the issue of asymptotic acceptance of the true Hessian and the full step by the sequential quadratic programming algorithm for equality-constrained optimization problems. In order to enforce global convergence, the algorithm is equipped with a standard Armijo linesearch procedure for a nonsmooth exact penalty function. The specificity of considerations here is that the standard assumptions for local superlinear convergence of the method may be violated. The analysis focuses on the case when there exist critical Lagrange multipliers, and does not require regularity assumptions on the constraints or satisfaction of second-order sufficient optimality conditions. The results provide a basis for application of known acceleration techniques, such as extrapolation, and allow the formulation of algorithms that can outperform the standard SQP with BFGS approximations of the Hessian on problems with degenerate constraints. This claim is confirmed by some numerical experiments.
This paper presents a new hybrid algorithm of Harris hawks' optimization with sequential quadratic programming (HHO-SQP) for optimal coordination of directional overcurrent relays to find optimal relays settings. ...
详细信息
This paper presents a new hybrid algorithm of Harris hawks' optimization with sequential quadratic programming (HHO-SQP) for optimal coordination of directional overcurrent relays to find optimal relays settings. The SQP procedure is employed in the hybrid algorism as a local search mechanism to enhance the performance of the original HHO method. The optimization problem is described based on a developed objective function as a non-linear and highly constrained optimization problem to minimize the total operating time for primary relays at the same time of maximizing the backup relays operating time. The developed objective function is subject to some constraints related to the coordination process including the absence of any miss coordination between primary and backup relays. The performance of the proposed algorithm based on the new objective function is implemented for two different test systems. The results of the proposed algorithm is compared with those obtained from other recent meta-heuristic techniques. The results show that the new hybrid algorithm outperforms the recently published meta-heuristic algorithms. (C) 2020 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
Robotic flat-end milling of complex surfaces offers advantages such as high flexibility and high machining efficiency. In the process of planning the toolpath based on the cutter contact path, the robot functional red...
详细信息
Robotic flat-end milling of complex surfaces offers advantages such as high flexibility and high machining efficiency. In the process of planning the toolpath based on the cutter contact path, the robot functional redundancy and the tool orientation need to be solved carefully. This paper presents a posture optimization method for robotic flat-end milling. Taking the weighted sum of the machining width and the toolpath smoothness performance criterion as the objective function, an optimization model considering the joint limits and gouging avoidance is established. An efficient algorithm based on sequential quadratic programming is proposed to solve this nonconvex problem. During the execution of the algorithm, the machining width is efficiently calculated by an iterative method based on conformal geometric algebra, while its derivatives are approximated analytically. Simulations and experiments demonstrate that the presented technique can resolve the tool axis direction and the robot redundancy effectively to increase the machining width and improve the toolpath smoothness, thus reducing the time for machining and improving the surface quality.
暂无评论