Unconstrained Minimization Technique Research Articles

Distributed Cooperative Quantum Learning for Discrete-Time Multiagent Source Exploration With Information Prompts.

In light of optimization theory and swarm evolutionary schemes, under multiple single-integrator mobile agents equipped with sensors and prompters, this article addresses a discrete-time multiagent source exploration problem with information prompts. Regarding information prompts as constraints on the unknown target, by virtue of penalty function skills (PFSs) and sequential unconstrained minimization techniques (SUMTs), the agents are driven toward the source under the guidance of the control strategy. In two cases of available and unavailable gradient information, a quantum potential well, an average optimal position estimator (AOPE), and a global optimal position estimator (GOPE) are introduced into swarm evolutionary schemes with a periodically oscillating weight, such that distributed cooperative quantum learning (DCQL) policy is proposed as a control strategy under communication restrictions, where AOPE and GOPE are developed relying on distributed consensus theory. In particular, when the gradient is unavailable, we put forth an adaptive generalized Bernstein neural network (AGBNN) to replace it based on excellent properties of Bernstein polynomials and adaptive approaches. Further, a performance analysis for the proposed policy is executed on the convergence and computational complexity, which ensures the accuracy and efficiency of the source exploration in theory. Ultimately, a simulation test is carried out, and the results validate the practicability and effectiveness of the offered method.

A novel process for rutin recovery from model solutions using nanofiltration: Experimental study, mathematical modeling, and scale‐up design

AbstractPressure‐driven membrane processes represent a credible method for the separation and concentration of natural polyphenols. The separation characteristics and their productivity depend on several factors such as molecular weight cut‐off, operating conditions, and test cell parameters. In this study, a compact polyamide HFT‐NF150 nanofiltration membrane has been tested to separate different model solutions of rutin, and the effects of operating conditions like trans‐membrane pressure, feed concentration, and feed flow rates were analyzed. All the findings of the experiment revealed that the maximum permeate flux of 3.04 × 10−5 m3/m2 s and 98.5% rejection were obtained at a high feed flow rate of 36 L/h and a high pressure of 6 × 105 Pa. Also, the Spiegler–Kedem model is used to report the mass transport phenomenon across the membrane. The unconstrained minimization technique based on the simplex search method was applied for the estimation of the unknown parameters. The theoretical and experimental values for permeate characteristics were then compared for each experimental data point for the model validation. Later, a mathematical model was used to check the scale‐up viability of the process to the commercial limit.Practical applicationsIn the past few years, naturally occurring bioactive compounds have revolutionized the whole scientific community for their exploration. Rutin is one such phenolic compound that is present in significant amounts in buckwheat and has a wide range of applications in the food and pharmaceutical industries. In this work, an energy‐efficient and cleaner method of nanofiltration is applied to concentrate the rutin from the model plant extract. Experiments were performed on a polyamide nanofiltration membrane with a molecular weight cut‐off of 150 Da at different operating conditions. Experimental results were fitted in a three‐parameter Spiegler–Kedem model to determine the transport parameters of the membrane for rutin. The values of transport parameters were also successfully validated using the experimental results. Experimental results and model predictions were then used to analyze the effects of different operating parameters. A scale‐up setup has been designed and simulated to check the feasibility of the process at the commercial level.

Analysis and Optimum Design of Self Supporting Steel Communication Tower

The present study deals with the optimum design of self supporting steel communication towers. A special technique is used to represent the tower as an equivalent hollow tapered beam with variable cross section. Then this method is employed to find the best layout of the tower among prespecified configurations. The formulation of the problem is applied to four types of tower layoutwith K and X brace, with equal and unequal panels. The objective function is the total weight of the tower. The variables are the base and the top dimensions, the number of panels for the tower and member's cross section areas. The formulations of design constraints are based on the requirements of EIA and ANSI codes for allowable stresses in the members and the allowable displacement atantenna position. The Sequential Unconstrained Minimization Technique (SUMT) is used to perform the process. Direct stiffness method is used for the analysis of the structure, with beam elements. The strain energy is used to derive the stiffness matrix for members of unsymmetrical cross section. A computer program in FORTRAN is developed to represent the tower as an equivalent beam, and generate the tower nodes and members, analysis, design and to find the optimum design. Four types of tower are studied with different load cases. The effects of earthquake and wind loadings are taken in two directions and two positions of antenna are considered in the process to seek the optimum design. The tower type of X-brace with unequal panels has the minimum weightcompared with other types of tower and the optimum design is satisfied when the angle of main leg is equal to (87O).

Optimisation of reverse salient rotor for interior permanent magnet synchronous motor and experimental validation

The prosperous development of electric vehicles has promoted the application of interior permanent magnet synchronous motors (IPMSMs). The flux-intensifying IPMSM has attracted more attention due to its high power-density and better flux-weakening performance. However, the optimisation efficiency and motor performance require further study in the actual optimisation design. To this end, this study is to optimise the electromagnetic field structure of motor rotors with the back propagation neural networks (BPNN) and multi-objective optimisation methods. Concretely, dimensional parameters of an initial motor are extracted as optimisation variables. Then, the BPNN are utilised to stimulate the motor performance obtained with finite element methods (FEM). With the trained BPNN, FEM tools are replaced in the subsequent optimisation process, which alleviates the computational burden. Besides, to address the problem of multi-objective optimisation with inequality constraints, the sequential unconstrained minimisation technique is incorporated into the non-dominated sorting genetic algorithm to obtain the Pareto frontier. Finally, the optimal design point is selected from the Pareto frontier. The simulation and experiment have been implemented to verify the superiority of the optimised rotor structure. Generally, this study has laid the foundation of the following motor optimisation with the higher executability and lower computational burden.

Optimum Design of Reinforced Concrete Box Culvert by Using Deterministic Approach

An optimum safe design is achieved for a reinforced concrete box culvert considering hydraulic, geotechnical and structural requirements and prices. To reach this intention, an optimization technique methodology is applied in estimation a safe design with a minimum price. The non-linear constrained optimization problem is generally expressed in terms of an objective function and constraints. Eleven design variables and forty constraint equations were employed in this work, with the total price of reinforced concrete box culvert as the objective function. The solution is obtained by means of developed computer software written in Visual BASIC. The computer software considers the Sequential Unconstrained Minimization Technique (SUMT), which is one of non-linear optimization techniques. In order to evaluate the influence of the input parameters on the box culvert design, the optimization program has been modified. The input design parameters are the discharge, head loss, length, entrance loss coefficient, Manning’s roughness coefficient, angle of internal friction of soil, compressive strength of concrete, yield strength of steel, unit price of concrete, unit price of excavation, unit price of filling and unit price of steel. A parametric study is formed to examine the influence and significance of each input design parameter on the total price of box culvert.

A Multiobjective Optimization Model for Continuous Allocation of Emergency Rescue Materials

Emergency rescue operations play a vital role in alleviating human suffering, reducing casualties, and cutting down economic losses. One key aspect in the management of these operations is the rational allocation of emergency relief materials, where the allocation is continuous, dynamic, and concurrent. This allocation should be made not only to minimize the emergency rescue losses, but also to reduce the cost of emergency rescue work. A reasonable and effective allocation scheme for emergency relief materials can be established to adapt to the continuity, dynamics, and concurrency of material distribution. In this work, we propose a multiobjective optimization model of emergency material allocation with continuous time-varying supply and demand constraints, where the objective is to minimize the losses and the economic cost incurred by the emergency rescue operations. The constrained optimization problem is handled through sequential unconstrained minimization techniques, and the multiobjective optimization is carried out by the fast nondominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a Pareto solution set with fairness and balance of loss and cost. The loss and cost associated with the Pareto frontier are employed to find an appropriate noninferior solution and its corresponding material allocation scheme. We verify through several simulations the model feasibility and the effectiveness of the proposed method, which can provide decision support for continuous material allocation in emergency rescue operations.

Collective control in arrays of wave energy converters

A Collective Control is developed in this paper for arrays of Wave Energy Converters (WECs). The proposed controller applies a Proportional-Derivative feedback control law for each WEC with the optimized controller gains. A surrogate model, composed of only mechanical elements, is adopted to replace the hydrodynamic model during the optimization process. An indirect exterior penalty function approach is implemented to handle the constraints on the displacement and control. The weight of the penalty function is updated in subsequent iterations in the Sequential Unconstrained Minimization Technique. A numerical simulation is first conducted for identification of the surrogate model parameters. With the control gains optimized based on the surrogate model, the energy conversion is compared for three models: the surrogate model, a model that uses a boundary element tool to compute the hydrodynamic forces, and a simulation using AQWA. The results show a good agreement of the energy conversion among the models. Finally, the performance of the surrogate model is analyzed. It is shown that the proposed controller maximizes the energy conversion of the entire WEC array while satisfying the constraints. Moreover, the surrogate model can replace the hydrodynamic model to predict the system behavior with adequate accuracy and more efficiency.

Rolling direction prediction of tensegrity robot on the slope based on FEM and GA

Six-strut tensegrity robot is a new mobile robot whose outer surface is an icosahedron containing 8 regular triangles and 12 isosceles triangles, and the robot performs rolling locomotion along the edges of the triangle. On the slope, it has lots of poses depending on the slope’s angles and positions of robot, which is difficult to control the rolling directions in the real world. This paper proposed a new method based on finite element method and a genetic algorithm to predict the rolling directions of the robot. The balanced forces equations of robot nodes are established using finite element method, which is a constrained optimization problem. The equations are transformed into an unconstrained optimization problem by the thinking of sequential unconstrained minimization technique. Finally, the unconstrained optimization problem is calculated by genetic algorithm, and the relations between the actuators and the rolling directions are obtained through the dot product of gravitational torque and the edge vector of bottom triangle. This method is verified by simulation and experiment results.

A two-echelon newsvendor problem with limited budget and capacity

In this paper, two-echelon newsvendor problem is considered. Many real-life situations like fashion, food industries, and healthcare services match newsvendor problem. Our problem is determining levels of inventory in order to optimize the profit and service level in selling a product. This product is made up of several raw materials. Only the distribution of demand is known, and the hot season of selling the product is just a short period and after that, the price of the product drops dramatically. The storage space and initial budget are limited. We modeled and solved the problem as an unconstrained nonlinear optimization problem using two nonlinear techniques, the sequential unconstrained minimization technique (SUMT) and steepest descent (SD).

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

PurposeLot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs.Design/methodology/approachThe problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO.FindingsTaking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible.Originality/valueDeveloping a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

Finite element approaching analysis of load localization and identification for smart structures

PurposeThe purpose of this paper is to propose a numerical approaching analysis method combining the sequential unconstrained minimization technique and finite element method to identify the loading condition and geometry of smart structures accurately.Design/methodology/approachA new load identification model is built and the finite element approaching method is proposed by the combination of finite element method and optimization technique.FindingsThe approaching algorithm has good convergence and fast approximation speed; the accuracy can meet the engineering requirements. The approaching model is simple, and the precision is controllable and it can be used to solve the load identification problem of the smart material structure.Originality/valueIn view of the cited papers, the information sensed by the smart structure is limited, discrete and contains certain errors. How to derive the cause from the limited, error-containing discrete information is an important problem that needs to be solved by the self-diagnosis function. A load identification model based on structural displacement response is established and a numerical approximation method is proposed by combining the finite element method with the optimization technique; the load magnitude and position of the structure are identified according to the displacement measurement values of the internal finite point in the structure under the load condition.

Joint Optimization for Residual Energy Maximization in Wireless Powered Mobile-Edge Computing Systems

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrained maximum margin (CMM)’. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the constraint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential unconstrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimization problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumulative distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘closed’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technological limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing operation; therefore, the economic objectives are minimized under imposed maximum probabilities of failure. In both situations, the efficiency and robustness of optimal solutions generated by the P-NSGA-II algorithm are analysed, discussed and compared with sequence of unconstrained minimization technique (SUMT) method.

Optimization Technique For Surface Roughness Prediction in Turning Operation

Surface roughness has a great influence on the functional properties of the product. Finding the rules that how process factors and environment factors affect the values of surface roughness will help to set the process parameters of the future and then improve production quality and efficiency. Since surface roughness is impacted by different machining parameters and the inherent uncertainties in the machining process, how to predict the surface roughness becomes a challengeable problem for the researchers and engineers. In this paper an attempt is made to review the literature on optimizing machining parameters in turning processes. Various conventional techniques employed for machining optimization include geometric programming, geometric plus linear programming, goal programming, sequential unconstrained minimization technique, dynamic programming etc. The latest techniques for optimization include fuzzy logic, scatter search technique, genetic algorithm, Taguchi technique and response surface methodology.

Optimization of Design Parameters and Cost of Geosynthetic-Reinforced Earth Walls Using Harmony Search Algorithm

This paper proposes a new approach to optimize the design of geosynthetic-reinforced retaining walls. Minimizing the cost of construction was considered as the optimization criterion. A metaheuristic technique, named Harmony Search Algorithm (HSA), is applied in optimizing the design of geosynthetic-reinforced earth walls. The involved optimization procedures are discussed in a step-wise approach and their applicability is demonstrated on geosynthetic-reinforced walls of height 5, 7 and 9 m. The effects of static and dynamic loads are considered. Results are compared between this study and studies that used Sequential Unconstrained Minimization Technique (SUMT). It is found that the construction cost, for a geosynthetic-reinforced walls optimized by HSA, showed as high as 9.2 % reduction from that of SUMT.

Research on design method of the full form ship with minimum thrust deduction factor

In the preliminary design stage of the full form ships, in order to obtain a hull form with low resistance and maximum propulsion efficiency, an optimization design program for a full form ship with the minimum thrust deduction factor has been developed, which combined the potential flow theory and boundary layer theory with the optimization technique. In the optimization process, the Sequential Unconstrained Minimization Technique (SUMT) interior point method of Nonlinear Programming (NLP) was proposed with the minimum thrust deduction factor as the objective function. An appropriate displacement is a basic constraint condition, and the boundary layer separation is an additional one. The parameters of the hull form modification function are used as design variables. At last, the numerical optimization example for lines of after-body of 50000 DWT product oil tanker was provided, which indicated that the propulsion efficiency was improved distinctly by this optimal design method.

IJARCCE - Computer and Communication Engineering

In this paper, the computerized controller for studying load shedding has been designed and implemented. This controller is designed based on the problem of minimizing load shedding during emergency conditions. The studied problem is formulated as a nonlinear static optimization problem subject to operational and equipment constraints. Suitable adjustments of control of generation rescheduling and control of phase shifting transformers before load shedding is determined and recommended. SUMT, solution algorithm adopting sequential unconstrained minimization technique is given. Results of application of the proposed technique to a 5 and 20-node network are presented. An overload relief policy is proposed to minimize load shedding.the proposed computerized technique have been presented in this paper.Applications to be IEEE - 5 and 20 bus system are presented to validate the applicability of the proposed technique to any system of any size.

Multi-Objective Optimization of Gas Fractionation Unit Operation

Gas fractionation unit is one of the main processes in refinery industries and consumes a significant portion of energy. Optimal operation of gas fractionation unit could reduce energy consumption and therefore minimize the production cost. This paper reports a real application of multi-objective optimization in a gas fractionation unit, in which energy consumption is minimized and product yield is maximized. The optimization algorithm is based on the non-dominate sorting genetic algorithm and the sequential unconstrained minimization technique. The optimization results show that the yield of product is increased and the energy consumption is reduced in the gas fractionation unit.

Augmented Lagrangian‐based approach for dense three‐dimensional structure and motion estimation from binocular image sequences

In this study, the authors propose a framework for stereo-motion integration for dense depth estimation. They formulate the stereo-motion depth reconstruction problem into a constrained minimisation one. A sequential unconstrained minimisation technique, namely, the augmented Lagrange multiplier (ALM) method has been implemented to address the resulting constrained optimisation problem. ALM has been chosen because of its relative insensitivity to whether the initial design points for a pseudo-objective function are feasible or not. The development of the method and results from solving the stereo-motion integration problem are presented. Although the authors work is not the only one adopting the ALMs framework in the computer vision context, to thier knowledge the presented algorithm is the first to use this mathematical framework in a context of stereo-motion integration. This study describes how the stereo-motion integration problem was cast in a mathematical context and solved using the presented ALM method. Results on benchmark and real visual input data show the validity of the approach.