Algorithm For Optimal Design Research Articles

Delay-Range-Dependent-Based Hybrid Iterative Learning Fault-Tolerant Guaranteed Cost Control for Multiphase Batch Processes

Concerning multiphase batch processes with delays, disturbances, and actuator faults, the design of 2D robust hybrid composite iterative learning fault-tolerant guaranteed cost controller is put forward. First, a hybrid iterative learning control law is introduced and the multiphase batch process with interval time-varying delays is converted to an equivalent 2D-FM switched system with actuator faults by the introduction of system output tracking errors and state errors and consideration of fault effects. Next, a sufficient condition for satisfying asymptotical stability that the closed-loop switched system has the upper bound of minimum performance index is established by application of the theoretical framework of 2D system and selection of 2D Lyapunov–Krasovskii function on the basis of an average dwell time method. Then, the optimal design algorithm of the 2D hybrid robust iterative learning fault-tolerant guaranteed cost control is presented. In the meantime, in view of the effects of delay terms on ...

Use of active control algorithm for optimal design of base-isolated buildings against earthquakes

This study presents a new design method that concurrently determines the stiffness and damping coefficient in a base isolation system. This design method is developed based on the similarity between the active and passive control system. Then, the stiffness and damping coefficient are derived from the linear quadratic regulator control algorithm to a single degree of freedom superstructure and formed as a function of single weighting. The best design of a base isolation system is determined by optimizing this weighting from the minimum H∞-norm responses of base displacement and roof acceleration. A parametric study is performed to understand the influence of superstructures to the resulting optimized base isolation system. Moreover, this study also provides a numerical example to validate the optimal design of base isolation systems. The potential to design nonlinear lead-rubber bearings with added viscous dampers based on the proposed method is also investigated. As a result, the proposed method yields a high-performance base isolation system for a known superstructure.

A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle

The transmission system together with power management will simultaneously affect the fuel economy of the plug-in hybrid electric vehicle (PHEV) with automated mechanical transmission (AMT). This paper strives to make three contributions to realize systematic design and optimization of the transmission and power management. Firstly, a design of experiment method is proposed to redesign the current transmission system of the PHEV with Optimal Latin Hypercube Design algorithm. Then, a co-optimization method is proposed to insight into the preferable speed ratios of the redesigned transmission system with multi-island genetic and dynamic programming algorithms. Finally, a Pontryagin’s Minimum Principle-based self-identification controller is proposed to realize adaptive power management control, based on a significant finding that the constant solution of the co-state from off-line iteration optimization can be approximately identified by the mean value of the co-state with time-moving in the self-identification controller. Results demonstrate that the current 6-speed ratio AMT can be reduced to 4, the fuel economy of the redesigned transmission system can be improved by 2.9% compared to the current transmission system and the self-identification controller can further improve the fuel economy of the PHEV compared to the conventional PI controller.

Particle Swarm Optimization Algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines

In this study, we propose a modified particle swarm optimization (PSO) algorithm, which is an improved version of the conventional PSO algorithm. To improve the performance of the conventional PSO, a novel method is applied to intelligently control the number of particles. The novel method compares the cost value of the global best (gbest) in the current iteration to that of the gbest in the previous iteration. If there is a difference between the two cost values, the proposed algorithm operates in the exploration stage, maintaining the number of particles. However, when the difference in the cost values is smaller than the tolerance values assigned by the user, the proposed algorithm operates in the exploitation stage, reducing the number of particles. In addition, the algorithm eliminates the particle that is nearest to the best particle to ensure its randomness in terms of the Euclidean distance. The proposed algorithm is validated using five numerical test functions, whose number of function calls is reduced to some extent in comparison to conventional PSO. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the total harmonic distortion (THD) of the back electromotive force (back EMF). Considering the performance constraint, an optimal design is attained, which reduces back EMF THD and satisfies the back EMF amplitude. Finally, we build and test an experimental model. To validate the performance of the optimal design and optimization algorithm, a no-load test is conducted. Based on the experimental result, the effectiveness of the proposed algorithm on optimal design of an electric machine is validated.

An algorithm for optimal design and thermomechanical processing of high carbon bainitic steels

In this paper we present a generic framework of a computationally efficient search algorithm to meet multiple objectives on a multi-dimensional search space. The algorithm is integrated with a penalty-based cost function that enables us to retain the interim optimal solutions. The algorithm has been applied to determine the optimal combination of alloying elements and heat treatment conditions to obtain steels with desired material properties, namely hardness, tensile strength and elongation. In this work, the search space is a combination of eight alloying elements and heat treatment conditions (isothermal temperature and time). To evaluate the quality of the alloying elements and heat treatment combinations, the algorithm is equipped with reduced order models for hardness, tensile strength as well as elongation percentage. The algorithm has been validated with respect to several standard test optimisation problems prescribed in the literature. Subsequently, the validated algorithm has been applied to develop optimal design solutions for steels with desired mechanical properties. In doing so, the framework is tested for three different evaluation functions to showcase the ability of the algorithm to obtain solutions meeting the desired targets/constraints.

An algorithm for optimal design and thermomechanical processing of high carbon bainitic steels

An algorithm for optimal design and thermomechanical processing of high carbon bainitic steels

Algorithm of numerical optimization of steel structures on basis of minimum weight criterion

The description of the algorithm of steel structures optimal design is described in the article. The calculation model is represented by flat and spatial rod elements. The problem of optimization is resolved with the help of mathematical programming. The material consumption is taken as a criterion of optimality. A wide range of inspections is presented in the form of regulatory requirements for strength, rigidity and local stability. The geometry parameters of the sections are varied, as well as the external geometry of the nodes of the final element model. Both continuous and discrete variations might be possible. The algorithm is implemented in two versions: on the basis of imitating the short-term problem and with the direct reference to the target and restrictive functions at each step of optimization process. The results of the solution of the steel framework optimization problem during on-going and discrete variations of geometric parameters are presented. The solutions obtained are estimated on uniqueness.

Controllability and Optimal Control of Higher-Order Incomplete Boolean Control Networks With Impulsive Effects

This paper investigates the controllability and optimal control of higher order incomplete Boolean control networks (BCNs) with impulsive effects by using the semi-tensor product of matrices. First, the incomplete logical system is expressed as an algebraic form, based on which several necessary and sufficient conditions for the controllability are presented. Then, the Mayer-type optimal control issue is studied and the optimal control design algorithms are established. At last, the study of illustrative examples shows the effectiveness of the obtained new results.

Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

The neural network has the advantages of self‐learning, self‐adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS‐RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi‐information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS‐RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self‐organizing RBF neural networks (self‐organizing RBF (SORBF) and rough RBF neural networks (RS‐RBF)), VS‐RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS‐RBF.

Waveforming Optimizations for Time-Reversal Cloud Radio Access Networks

Due to the unique spatial and temporal focusing effects, time-reversal (TR) communication can be utilized in the cloud radio access network (C-RAN), where it creates “tunneling effects” such that the traffic load in the front-haul links can be alleviated in both downlink and uplink. Although the basic TR waveforms are simple to use, and they cannot provide the optimal performance in some cases. Since the C-RAN is usually expected to serve massive wireless devices, the severe inter-user interference will limit the performance of the system, especially in the high signal-to-noise ratio region where the interference power dominates the noise power. In this paper, we propose to optimize both downlink and uplink transmissions in the TR-based C-RAN so as to alleviate the interference. In the downlink transmission, an optimal content-aware waveform design is proposed, so that the baseband units (BBUs) are able to combine both the channel information and the content information to suppress the interference. In the uplink transmission, an optimal receiver design algorithm is proposed, such that the BBUs can detect the symbols transmitted by the terminal devices more accurately by leveraging the channel information. We study the bit error rate performance of the proposed algorithms based on extensive measurements of the wireless channel in a real-world environment. Numerical results demonstrate the significant performance improvement over the basic TR transmission techniques and the traditional waveform design techniques.

Extended Jaya's Algorithm for Optimal Design of Broadband Layered Microwave Absorbing Structures

Layered microwave absorbing structures (MASs) for the suppression of electromagnetic signals are important due to their broadband frequency response. However, optimization of such structures is difficult because several design variables are involved. A method for the efficient design of broadband, layered, magneto-dielectric MASs is based on analytical modeling of the frequency-dependent complex dielectric permittivity ( ${\mathbf \varepsilon_{r}}$ ) and magnetic permeability ( ${\mu} _{r}$ ). The use of an efficient, extended Jaya's algorithm for optimization results in highly feasible values of ${\mathbf \varepsilon_{r}}$ and ${\mu} _{r}$ based on optimal Debye parameters for a specific reflection loss (RL) and a −10 dB absorption bandwidth. The optimization is carried out in the X-band for both single- and dual-layer MASs. Deeper insight is obtained by optimizing RL with and without constraints on absorber layer coating thickness. The results show the effectiveness of the optimization for the design of MASs for practical applications.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

Abstract A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

An Enhanced Imperialist Competitive Algorithm for optimum design of skeletal structures

Abstract An Enhanced Imperialist Competitive Algorithm (EICA) is proposed aimed at enabling the ICA algorithm to escape from local optima faster, thus enabling faster convergence of the basic ICA algorithm. To this end, the imperialistic competition phase of the algorithm is enhanced by giving added value to a slightly unfeasible solution, based on its distance from the relative imperialist. The performance of the proposed EICA algorithm is investigated through design optimizations of four benchmark side-sway frames. Results indicate that, in terms of both the design quality and the solution speed, EICA compares significantly favourable with a number of other meta-heuristic optimizers, including the basic ICA.

Application of Smart Bats Algorithm for Optimal Design of Power Stabilizer System at Sengkang Power Plant

The problem of using Power System Stabilizer (PSS) in generator excitation is how to determine the optimal PSS parameter. To overcome these problems, the authors use a method of intelligent bats based algorithm to design PSS. Bat Algorithm is an algorithm that works based on bat behavior in search of food source. Correlation with this research is, food sources sought by bats represent as PSS parameters to be optimized. Bat's algorithm will work based on a specified destination function, namely Integral Time Absolute Error (ITAE). In this research will be seen the deviation of velocity and rotor angle of each generator, in case of disturbance in bakaru generator. The analysis results show that the uncontrolled system produces a large overshoot oscillation, and after the addition of PSS oscillation control equipment can be muted. So that the overshoot and settling time of each generator can be reduced and the generator can quickly go to steady state condition

Improved GWO algorithm for optimal design of truss structures

In this article, an improved grey wolf optimizer (IGWO) algorithm is developed for optimal design of truss structures. Grey wolf optimizer (GWO) is a recently proposed algorithm for optimization, which is herein improved to handle structural optimization in an efficient manner. In this work, performance of the GWO in structural optimization is also investigated. A few tunable parameters are defined to provide proper adaptability for the algorithm and to optimize the structures using fewer structural analyses, while obtaining finer solutions. Hence, in addition to reduce the computational efforts, better solutions are obtained as it is shown by several benchmark examples, where both GWO and IGWO are employed for the optimization. Mathematical functions as well as various design examples from small to large truss structures with different search spaces are examined to demonstrate the ability and efficiency of the present improved version in comparison to its standard version.

Shape design sensitivity and optimization of anisotropic functionally graded smart structures using bicubic B-splines DRBEM

A new shape optimization technique is developed, using bicubic B-splines dual reciprocity boundary element method, for anisotropic functionally graded smart structures to minimize weight while satisfying certain constraints upon stresses and geometry. An implicit differentiation of the boundary integral equation with respect to geometric design variables is used to calculate shape design sensitivities of anisotropic materials. This method allows the coupling of an optimizing technique and a boundary element elastic stress analyzer to form an optimum shape design algorithm in two dimensions and also allows high-accuracy computation. The boundary element method needs much fewer data related only to the considered boundary of the structure, so it is very suitable for shape optimization in comparison with the finite element method.Because of the non-linear behavior of weight and stresses, the numerical optimization method used in the program is the feasible direction approach, together with the one-dimensional golden-section search technique. The two-dimensional electric fillet knife used as the numerical example in order to verify the formulation and the implementation of the proposed method.

OPTIMAL DESIGN OF HOUSING ATTICS WITH INTEGRATED SOLAR COLLECTORS

In order to reduce the increasing energy consumption for the domestic demands of existing single-family housing and take advantage of frequent building enlargements, this paper presents a methodology and supporting software tool for determining the optimal design configuration of an attic with integrated solar collectors. The analysis procedure is based on parametric modeling, energy simulation and the use of evolutionary algorithms for finding optimal designs. It has been implemented as a Web-platform for public use that provides users with a proposal of an attic shape with maximum solar energy collection, maximum living space and minimum construction envelope for each house according its size and orientation. The attic integrates PV, thermal and hybrid solar panels on one side of the roof. This paper describes the methodology and software design, assessment of the Web-platform usage and case-studies to verify its behavior. In a matter of minutes, the Web-platform enables users to select a specific attic design for each house that has integrated solar collectors that can produce energy to cover almost 100% of domestic energy consumption. The attics designed provide a nearly 30% increase in living space through the extension of one to four rooms, and the construction cost of the envelope is similar to that of a standard housing extension.

Random search algorithm for optimal mixture experimental design

ABSTRACTIt is well known that it is difficult to obtain an accurate optimal design for a mixture experimental design with complex constraints. In this article, we construct a random search algorithm which can be used to find the optimal design for mixture model with complex constraints. First, we generate an initial set by the Monte-Carlo method, and then run the random search algorithm to get the optimal set of points. After that, we explain the effectiveness of this method by using two examples.

Application of Single- and Multi-Objective Evolutionary Algorithms for Optimal Nonlinear Controller Design in Boiler–Turbine System

This paper presents an application of evolutionary algorithm techniques for optimal nonlinear controller design in drum-type boiler–turbine system. Designing of controller for third-order boiler–turbine system is always a complicated task due to the presence of highly interactive nonlinearities. The present work is the first one which attempts to design and implement recently developed finite time convergent controller in third-order boiler–turbine dynamics, and it is described as multi-input–multi-output (MIMO) nonlinear system. The present work explores the possibility of application of single-objective and multi-objective evolutionary algorithm techniques toward optimal tuning of finite time convergent controller to achieve the desired performance for third-order boiler–turbine system. The single-objective evolutionary algorithm techniques such as real-coded genetic algorithm (RGA), modified particle swarm optimization (MPSO), differential evolution (DE), and self-adaptive differential evolution (SADE) are implemented with the minimization of integral square error (ISE) as an objective to obtain optimal tuning parameters. Also, the present paper explores the possibility of simultaneous minimization of conflicting objectives such as ISE and computational cost of the proposed controller using multi-objective evolutionary algorithms such as non-dominated sorting genetic algorithm (NSGA) and modified non-dominated sorting genetic algorithm-II (MNSGA-II). The performance of the proposed optimal finite time convergent controller is validated by simulating different kinds of set point changes, and the obtained results are presented as various case studies. The adaptability of the proposed controller during parameter variations is also examined. The performance of the single-objective and multi-objective evolutionary algorithms has been statistically analyzed, and the results are reported. The results reveal that among the four single-objective EA techniques, SADE offers better performance due to its inherit self-adaptive capability. Also, during multi-objective optimization, MNSGA-II has provided better solution due the presence of dynamic crowding distance (DCD) and control elitism (CE) strategies.

Artificial bee colony algorithm for thermohydraulic optimization of flat plate solar air heaters

Determination of thermal efficiency is not a sufficient tool for solar air heaters because of disregarded energy consumption by fans. Most of the solar air heaters are equipped with fans in order to overcome pressure drops. Thermohydraulic efficiency is more favorable than thermal efficiency in terms of the design of solar energy conversion systems. In this present study, Artificial bee colony (ABC) based optimum design and operation algorithm is developed for Single pass solar air heater (SPSAH) and for Double parallel pass solar air heater (DPPSAH) which maximizes thermohydraulic efficiency. In order to solve the algorithm, channel depth is treated as a design variable, and air mass flow rate is defined as an operation parameter. Furthermore, energy balance equations are written for solar air heaters and solved for steady-state condition. As a result, optimum design and operation values are obtained from the algorithm for different collector lengths and solar insolation levels.