Algorithm For Optimal Design Research Articles

Symbiotic organisms search algorithm for optimal design of CMOS two‐stage op‐amp with nulling resistor and robust bias circuit

This study suggests an evolutionary technique namely symbiotic organisms search (SOS) algorithm based optimal designs of two different analogue very-large-scale integration circuits. The configurations considered here are nulling resistor compensation based complementary metal–oxide–semiconductor (CMOS) two-stage op-amp and two-stage CMOS op-amp with robust bias circuit. The prime goal of this work is the sizing of metal–oxide–semiconductor (MOS) transistors employing the SOS algorithm to optimise the area occupied by the individual circuit. Design results based on the SOS algorithm are authenticated with SPICE simulation. SPICE simulation results reveal that all the design specifications are firmly satisfied for both the circuits. Moreover, SPICE based results show that the SOS algorithm provides much better results compared to the earlier reported techniques regarding the gain, MOS area and power dissipation for the abovementioned op-amp circuits.

Hybrid Invasive Weed Optimization-Shuffled Frog-Leaping Algorithm for Optimal Design of Truss Structures

In this paper, an efficient hybrid optimization algorithm based on invasive weed optimization algorithm and shuffled frog-leaping algorithm is presented for optimum design of trusses. The shuffled frog-leaping algorithm is a population-based cooperative search metaphor inspired by natural memetic, and the invasive weed optimization algorithm is an optimization method based on dynamic growth of weeds colony. The proposed algorithm utilizes the shuffled frog-leaping algorithm for finding optimal solution region rapidly and the invasive weed optimization is used to exploit global solutions. Different benchmark truss structures are optimized using the new hybrid algorithm. This algorithm converges to better or at least the same solutions compared to some other methods, while the number of structural analyses is reduced. The outcomes are compared to those obtained previously using other recently developed metaheuristic optimization methods.

Optimum design of tied-arch bridges under code requirements using enhanced artificial bee colony algorithm

In this study, optimum design algorithm is presented for tied-arch bridges under AASHTO-LRFD Bridge Design Specifications provisions. It is decided that in tied-arch bridges ties, arch ribs, and bottom and top bracings are made of built-up box sections, whereas built-up I sections are utilized for floor beams and stringers. Bars are adopted for hangers. In the formulation of the optimization problem, design variables are selected as the cross sectional dimensions of steel plates not that of I and box sections. Design pools are prepared for steel plate sections in addition to the hanger bars so that the optimization algorithm can select appropriate steel plates, construct I and box built-up sections for members of 3-D tied-arch such that the weight of the bridge is minimized. In addition to design code requirements, geometrical constraints among its elements that are required for manufacturability of the bridge are also considered. The design process of tied-arch bridges differs from that of steel framed structures. It necessitates consideration of moving vehicle loads. Design optimization algorithms require the response of bridges under several design load arrangements and in the construction of influence lines. This is achieved by using open application programming interface (OAPI) facility of SAP2000. The solution of discrete nonlinear programming problem is obtained by using the proposed Enhanced Artificial Bee Colony algorithm (eABC). The proposed algorithm is compared with Standard Artificial Bee Colony (ABC) and Exponential Big Bang-Big Crunch (eBB-BC) algorithms to evaluate its performance.

TO QUESTION OF COMBINED SYSTEMS RATIONALIZATION

Purpose. Developing a new approach to the rationalization of the combined structure, taking into account constructive, technological, operational and economic requirements. Methodology. The bending moment adjustment method representations to achieve the goal, based on the properties of the thrust-systems, namely: in the combination beam construction cost material defining elements are experiencing stress state of compression with a bend; in cells operating at the bending compression, reducing the size of the cross section largely determined by the decrease of the bending moment; bending moments increase from the support to the middle of the span. Originality. On the basis of calculations using the methodology set forth above, the algorithm of optimal design of truss beams under the influence of permanent and temporary load. The present solution allows to obtain an effective design combined with a variable number of racks, depending on the required span length, corresponding to the received optimality criterion. Practical value. The use of this approach and the optimization algorithm based on the use of the design features of truss beams and requires minimization of bending moments, will significantly reduce the complexity of the calculation, as well as to find the optimal values of the cross sections for each case.

Coal or electricity? An evolutionary game approach to investigate fuel choices of urban heat supply systems

There is a growing tendency to replace coal heating with electric heating in urban heat supply systems to reduce the air pollution and achieve defossilisation. To promote the use of electric heating, the economic incentive from governments will play a decisive role. This paper investigates the impact of taxation and subsidy policies on the long-term evolution of urban heat supply systems based on the evolutionary game theory. The evolutionary stable strategy is calculated which yields the fraction of each kind of heating strategy as well as the regulatory policy in a steady state. Four scenarios including both static and dynamic policy design mechanisms are simulated. The dynamic policy design is formulated as an optimal control problem, and the optimal evolutionary trajectory is obtained from the proposed optimal parameter design algorithm. Simulation results indicate that the amount of electric heating can be flexibly and accurately controlled by the government through the design of taxation and subsidy policies. It is shown in the case study that taxations could be more efficient than subsidies to encourage using electric heating, and a combination of dynamic taxation and dynamic subsidy policies can expedite the heating system electrification process.

Dynamic optimization of a dual-axis solar tracker for PV modules

This paper deals with the dynamic optimization of the dual-axis tracking mechanism, which is used for increasing the energetic efficiency of a photovoltaic module, by maximizing the rate of incident solar radiation. The study is based on the optimal design algorithm included in the commercial MBS (Multi-Body Systems) software solution ADAMS, through the parameterization of the virtual model, which offers the possibility to perform a series of studies aiming at identifying the design parameters that decisively influence the dynamic behavior of the tracking systems. The global coordinates of the design points (i.e. the mounting points of the actuating sources) are defined as design variables for the optimization process, while the design objectives refer to the power consumptions of the actuating sources.

Coupling of inverse method and cuckoo search algorithm for multiobjective optimization design of an axial flow pump

This work describes the application of a multiobjective cuckoo search method for turbomachinery design optimization of an axial pump. Maximization of the total efficiency and minimization of the required net positive suction head of the pump are the two objective functions considered for the optimization problem. The optimization process is carried out on a range of imposed volumetric flow rates, with taking into account at each discretized radius between the hub and tip of the rotor: the profile camber, rotor wall thickness, angular deviation, and the solidity, regarded as geometrical constraints and nominal flow rate as mechanical constraint. Two strategies are proposed in order to solve the problem. In the first one, three forms of mono-objective model with two variables, total efficiency and net positive suction head, are considered. In the second one, a multiobjective model with nondominated sorting scheme is adopted. A comparative evaluation of results obtained from the proposed approach with those of a reference machine and genetic algorithm allowed us to validate the present work.

Adaptive optimal waveform design algorithm based on frequency‐stepped chirp signal

In this study, the authors propose an adaptive optimal waveform design algorithm, based on a frequency-stepped chirp signal with arbitrary sub-pulse bandwidths and chirp slopes. The waveform is designed to maximise the signal-to-interference-plus-noise ratio at the receiver. The integral sidelobe level of an autocorrelation function is constrained at a low level, thereby increasing the detection probability of the weak echoes reflected from dim targets. An energy constraint and a low peak-to-average power ratio constraint are also taken into account owing to the flexible power allocation for interference suppression. By doing so, the radar system improves the efficiency of the transmitter power resources and avoids the non-linear effects of the power amplifier. What is more, a cyclic iterative algorithm is employed to solve the optimisation problem. At last, numerical simulation results evaluate the effectiveness of the designed waveform compared with a linear frequency modulation signal, resulting in a great improvement in the target detection performance.

Optimal designs with axial values

We introduce a modification to the coordinate-exchange algorithm for generating optimal experimental designs that incorporates off-face axial value runs similar to a central composite design (CCD). This improvement addresses a weakness of current optimal designs relative to classical designs: the superior properties of CCDs with off-face axial values relative to equal-sized optimal designs that are constrained to the cuboidal experimental design region. CCDs tend to have significantly less collinearity among quadratic effects, which results in higher power for quadratic terms, better D-efficiency, and lower average prediction variance (i.e., I-optimality). Optimal designs offer greater flexibility in design size, model specification, and the ability to incorporate categorical factors. By incorporating axial values into an optimal design algorithm, the strengths of both approaches can be combined in a single design that generally outperforms both CCDs and current optimal designs.

Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass

The factor of stochastic vehicle mass can greatly affect the optimality of energy management for plug-in hybrid electric buses. However, current studies usually take the vehicle mass as constant, which is inevitably far away from reality. This paper responds to this problem by investigating a receding horizon control (RHC)-based energy management together with a predictive model of terminal state of charge (SOC) constraint. The predictive model can reinforce the local feature of the terminal SOC constraint, by a piecewise nonlinear regression model considering the stochastic vehicle mass. Especially, the stochastic vehicle mass is designed as stochastic variable at each road segment (the route between neighbored bus stops), and Optimal Latin Hypercube Design algorithm is deployed to sample and probe the design space constituted by the stochastic variables. Besides, the predictive model is constructed by partial least squares method, based on the sampled design space and corresponding optimal SOCs from dynamic programming. Simulation results show that the proposed predictive model is reasonable and can optimally predict the terminal SOC constraint at every receding horizon. Furthermore, although the fuel economy of the RHC strategy is worse than the DP strategy, it can be improved by 5.29% at least, compared to the rule-based strategy.

Efficient Hybrid Method of FEA-Based RSM and PSO Algorithm for Multi-Objective Optimization Design for a Compliant Rotary Joint for Upper Limb Assistive Device

This paper proposes an efficient hybrid methodology for multi-objective optimization design of a compliant rotary joint (CRJ). A combination of the Taguchi method (TM), finite element analysis (FEA), the response surface method (RSM), and particle swarm optimization (PSO) algorithm is developed to solving the optimization problem. Firstly, the TM is applied to determine the number of numerical experiments. And then, 3D models of the CRJ is built for FEA simulation, and mathematical models are formed using the RSM. Subsequently, the suitability of the regression equation is assessed. At the same time, the calculation of weight factors is identified based on the series of statistical equations. Based on the well-established equations, a minimum mass and a maximum rotational angle are simultaneously optimized through the PSO algorithm. Analysis of variance is used to analyze the contribution of design variables. The behavior of the proposed method is compared to the adaptive elitist differential evolution and cuckoo search algorithm through the Wilcoxon signed rank test and Friedman test. The results determined the weight factors of the mass and rotational angle are about 0.4983 and 0.5017, respectively. The results found that the optimum the mass and rotational angle are 0.0368 grams and 59.1928 degrees, respectively. It revealed that the maximum stress of 335 MPa can guarantee a long working time. The results showed that the proposed hybrid method outperforms compared to other evolutionary algorithms. The predicted results are close to the validation results. The proposed method is useful for related engineering fields.

Optimal Design for Distributed Secondary Voltage Control in Islanded Microgrids: Communication Topology and Controller

This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains. First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix. A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration. After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements. Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller. Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters. Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures. Therefore, it promises to improve the performance of distributed secondary control. The effectiveness of the proposed methodology is verified by a simulation study.

Enhanced crow search algorithm for optimum design of structures

In this study, the capability of recently introduced crow search algorithm (CSA) was evaluated for structural optimization problems. It is observed that the standard CSA was led to undesirable performance for solving structural optimization problems. Accordingly, three modifications were made on the standard CSA to obtain the enhanced crow search algorithm (ECSA) while no parameter was added to the ECSA. First, each violated decision variable was replaced by corresponding decision variable of the global best solution. Second, a free-fly mechanism was suggested for constraint handling. Third, the personal upper bound strategy (PUBS) was proposed for elimination of inessential structural analyses. To assess the efficacy of the proposed modifications, four popular benchmark structures were employed and each modification was added to the CSA in a separate stage and then its effects were illustrated. The results of benchmark structures were examined in terms of minimum weight, convergence rate, and reliability. The results confirmed that the ECSA was significantly better than the standard CSA. Moreover, the ECSA obtained better or very competitive results in comparison with well-known and other newly developed metaheuristic methods.

Effective Hybrid Algorithm of Taguchi Method, FEM, RSM, and Teaching Learning-Based Optimization for Multiobjective Optimization Design of a Compliant Rotary Positioning Stage for Nanoindentation Tester

This paper proposes an effective hybrid optimization algorithm for multiobjective optimization design of a compliant rotary positioning stage for indentation tester. The stage is created with respect to the Beetle’s profile. To meet practical demands of the stage, the geometric parameters are optimized so as to find the best performances. In the present work, the Taguchi method is employed to lay out the number of numerical experiments. Subsequently, the finite element method is built to retrieve the numerical data. The mathematical models are then established based on the response surface method. Before conducting the optimization implementation, the weight factor of each response is calculated exactly. Based on the well-established models, the multiple performances are simultaneously optimized utilizing the teaching learning-based optimization. The results found that the weight factors of safety factor and displacement are 0.5995 (59.95%) and 0.4005 (40.05%), respectively. The results revealed that the optimal safety factor is about 1.558 and the optimal displacement is 2.096 mm. The validations are in good agreement with the predicted results. Sensitivity analysis is carried out to identify the effects of variables on the responses. Using the Wilcoxon’s rank signed test and Friedman test, the effectiveness of the proposed hybrid approach is better than that of other evolutionary algorithms. It ensures a good effectiveness to solve a complex multiobjective optimization problem.

Vibration absorber optimization for boom-sprayer

The main task of this work is to analyze optimal design-system of the booms of boom-sprayers. The discrete-continue models of machines dynamics of such wheeled machines as boom-sprayer with elongated boom element with the attachment of dynamic vibration absorbers are offered. The algorithms for vibration decreasing of boom are received. The new vibroabsorbing elements are proposed. The paper contemplates the provision of dynamic vibration absorbers (DVA) of buffered impact masses and particle type. Such originally designed absorbers reduce vibration selectively in maximum vibration mode, without introducing vibration in other modes. The damping results from the exchange of momentum during impacts among the masses and masses and stops as the structure vibrates. A technique is developed to give the optimal DVA’s as single degree of freedom (SDOF) buffered system. The one-digit values are established not only for the dynamic vibration absorber parameters, but also for mechanical parameter of base structure – boom in connection points of the dynamic vibration absorbers. Finally, present research develops the genetic algorithms for optimal design searching by discrete-continuum DVA’s system – base system modeling.

Optimal Trajectory Design of Spacecraft Using Multi-flyby

This paper proposes an optimal design method of a trajectory of spacecraft using multi-flyby. In recent years, many deep space explorations have been conducted, and it is necessary to minimize the fuel consumption for these missions. Therefore, a trajectory of spacecraft that minimizes the fuel consumption with the mission constraints satisfied is determined by numerical optimization. In order to cope with complicated orbits with many flybys and severe constraints, this paper extends the multiple gravity assist (MGA) problem, and proposes a new optimal trajectory design algorithm. We newly add two discrete optimization variables to the MGA problem: the number of revolutions before flyby, and selection of multiple solutions to the Lambert problem. In the proposed algorithm, trajectory optimization is recursively executed by adding a flyby target one by one, instead of optimizing the entire trajectory with multi-flybys at once. The validity of the proposed algorithm is demonstrated by numerical experiments with two scenarios of Parker Solar Probe and Solar Orbiter. In addition, the relation between the number of flybys and fuel consumption is also examined, which shows the trade-off relationship between the two.

Hybrid Structure Design of Lightweight Robotic Arms Based on Carbon Fiber Reinforced Plastic and Aluminum Alloy

Some light materials, such as hollow sphere composite (HSC), carbon fiber reinforced plastic (CFRP), and aluminum alloy (AA), are currently used in the design of lightweight robotic arms. However, the high cost limit the use of HSC, the CFRP has a relatively low cost but poor processing property, and the AA has good processing property but relatively high density. To make up respective shortcomings, this study focuses on the design issues of lightweight robotic arms by using two kinds of materials. Based on the CFRP and AA, a hybrid structure design approach is proposed to minimize the total mass of the lightweight robotic arms with CFRP/AA hybrid structure. To accomplish the objective, structural dimensions and layer parameters in hybrid structures are parameterized as design variables subject to the strength, stiffness, and dynamic constraints. Moreover, the bonding strength of the interaction surfaces between CFRP and AA parts is also considered on the basis of the cohesive zone model (CZM). In optimization design, the ABAQUS and elitist non-dominated sorting genetic algorithm (NSGA-II) in modeFRONTIER utilizing the Python script are respectively employed for the structural analyses and iteration calculation. Finally, a design example and an experimental prototype are provided to validate the proposed method and compare with the previous AA prototype in mass with a reduction of 24.32%.

Modified Farmland Fertility Optimization Algorithm for Optimal Design of a Grid-connected Hybrid Renewable Energy System with Fuel Cell Storage: Case Study of Ataka, Egypt

In this paper, a Modified Farmland Fertility Optimization algorithm (MFFA) has been presented for optimal sizing of a grid connected hybrid system including photovoltaic (PV), wind turbines and fuel cell (FC). The system is optimal designed for providing a clean, reliable and affordable energy by adopting hybrid power systems. This system is very important for countries looking to achieve their sustainable development goals. MFFA is proposed in order to reduce the processing implementation time. The optimization method depends on the high reliability of the hybrid power supply, small fluctuation in the injected energy to the grid and high utilization of the wind and solar complementary characteristics. Moreover, MFFA is applied to minimize the cost of energy while satisfying the operational constraints. A real case study of a hybrid power system for Ataka region in Egypt is introduced to evaluate the performance of the proposed optimization method. Moreover, a comprehensive comparison between the proposed MFFA optimization technique and the conventional Farmland Fertility Algorithm (FFA) has been presented to validate the proposed MFFA.

Optimization design of reconfiguration algorithm for high voltage power distribution network based on ant colony algorithm

Abstract Distribution network reconfiguration is a very complex and large-scale combinatorial optimization problem. In network reconfiguration, whether an effective solution can be obtained is a key issue. Aiming at the problems in network reconstruction by traditional algorithm, such as long time required, more times of power flow calculation and high network loss, a network optimization design algorithm based on improved ant colony algorithm for high voltage power distribution network is proposed. After analyzing the operating characteristics of the high voltage power distribution network, the network topology of the high voltage power distribution network is described by constructing a hierarchical variable-structure distribution network model. A mathematical model of distribution network reconstruction considering the opportunity constraint with the minimum network loss as the objective function is established. The power flow distribution is calculated by using the pre-push back-generation method combined with the hierarchical structure of the distribution network. The maximum and minimum ant colony algorithm is introduced to improve the pheromone updating method of the traditional ant colony algorithm, and the search range is expanded, so that the algorithm can jump out of the local optimization trap to realize the accurate solution of the power distribution network reconstruction model. The experimental results show that compared with the current network reconstruction algorithm, the proposed algorithm requires less time for convergence, less power flow calculation, and lower network loss.

Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.