Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

In this paper,a computation framework for addressing combined economic and emission dispatch(CEED)problem with valve-point effects as well as stochastic wind power considering unit commitment(UC)using a hybrid approach connecting sequential quadratic programming(SQP)and particle swarm optimization(PSO)is proposed.The CEED problem aims to minimize the scheduling cost and greenhouse gases(GHGs)emission cost.Here the GHGs include carbon dioxide(CO_(2)),nitrogen dioxide(NO_(2)),and sulphur oxides(SO_(x)).A dispatch model including both thermal generators and wind farms is developed.The probability of stochastic wind power based on the Weibull distribution is included in the CEED model.The model is tested on a standard system involving six thermal units and two wind farms.A set of numerical case studies are reported.The performance of the hybrid computational method is validated by comparing with other solvers on the test system.

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.

Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model

In this study,the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks(ANNs)along with the hybridization procedures of global and local search approaches.The global search genetic algorithm(GA)and local search sequential quadratic programming scheme(SQPS)are implemented to solve the nonlinear Liénard model.An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS.The motivation of the ANN procedures along with GA-SQPS comes to present reliable,feasible and precise frameworks to tackle stiff and highly nonlinear differentialmodels.The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models.The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness,viability and efficacy.Moreover,statistical performances based on different measures are also provided to check the reliability of the ANN along with GASQPS.

The dynamic relaxation form finding method aided with advanced recurrent neural network

How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.

Algorithm research of the SQP method used in roll schedule calculation for Baosteel's 5m heavy plate

Loading distribution for heavy plate mill is to find optimal control solutions under the granted performance indicators and constraints including mill capacity and hypothesis of rolling models.The solutions are quite different for different performance indicators.In the article,the performance indicators and sequential quadratic programming(SQP for short below) methods employed in 5 000 mm heavy plate mill of BaoSteel are penetratingly analyzed.Generally,the SQP method is an effective and fast way to solve the nonlinear programming problems with small or medium scale constraints.Early in 1976,Han put forward the SQP method for the first time and Powell made it perfect and accomplished the algorithm in 1977.In fact, SQP method was to turn a nonlinear programming problem to a series of sub set of quadratic programming problems.In the algorithm,each iteration step is to solve one quadratic programming problem.The optimal solutions will be gradually approached after quadratic programming problems were totally solved.When solving the quadratic programming problem,the active set strategy were employed which turned the constrained quadratic programming problem to unconstrained quadratic programming problem.The active set strategy made the whole quadratic programming problem be solved by a least square problem.And finally, the matrix of the least square problem would be decomposed by Q matrix and R matrix.After Q matrix and R matrix were obtained,the optimal solutions would be finally found.For loading distribution,the performance indicators were composed by plate shape and draft of each pass.Plate shape is represented by rolling force gradually reduced pass by pass with a tunable factor.The mill capacity is another performance indicator represented by draft of each pass.For heavy plate mill,the mill capacity here is the motor moment. For heavy draft,the motor would be overloaded especially for the first several passes;for small draft,the motor would be loaded slightly.All these would not be permitted to happen when calculating the loading distribution.The mill capacity indicator made the loading of mill just be in the middle,not too much and not too low.In the article,these two performance indicators were analyzed in detail.Examples of loading distribution results with different performance indicators were given by the SQP method.When making changes to the performance indicators,there would be different solutions to the loading distribution.For the optimal solutions to the mill,there was supposed to make changes to the factors of the performance indicators or upgrade the accuracy of the mathematical models of the rolling process.

Numerical method for optimum motion of undulatory swimmingplate in fluid flow

A numerical method for the optimum motion of an undulatory swimming plate is presented. The optimum problem is stated as minimizing the power input under the condition of fixed thrust. The problem is singular for the invisible modes, and therefore the commonly used Lagrange multiplier method cannot predict an optimum solution but just a saddle point. To eliminate the singularity, an additional amplitude inequality constraint is added to the problem. A numerical optimization code with a sequential quadratic programming method is used to solve the problem. The method is applied to several cases of the motion of two-dimensional and three-dimensional undulatory plates,and the optimum results are obtained.

A Novel IoT Application Recommendation System Using Metaheuristic Multi-Criteria Analysis

There are a variety of Internet of Things(IoT)applications that cover different aspects of daily life.Each of these applications has different criteria and sub-criteria,making it difficult for the user to choose.This requires an automated approach to select IoT applications by considering criteria.This paper presents a novel recommendation system for presenting applications on the IoT.First,using the analytic hierarchy process(AHP),a multi-layer architecture of the criteria and sub-criteria in IoT applications is presented.This architecture is used to evaluate and rank IoT applications.As a result,finding the weight of the criteria and subcriteria requires a metaheuristic approach.In this paper,a sequential quadratic programming algorithm is used to find the optimal weight of the criteria and sub-criteria automatically.To the best of our knowledge,this is the first study to use an analysis of metaheuristic criteria and sub-criteria to design an IoT application recommendation system.The evaluations and comparisons in the experimental results section show that the proposed method is a comprehensive and reliable model for the construction of an IoT applications recommendation system.