Algorithm For Optimal Design Research Articles

Discrete optimization algorithm for optimal design of a solar/wind/battery hybrid energy conversion scheme

Abstract Renewable energy technologies have been developed in recent years due to the limited sources of fossil fuels, the possibility of depletion of fossil fuels and the related environmental issues. In these types of systems, it is crucial to reach optimum sizing in order to have an affordable system based on solar and wind energy and energy storage. In this study, a powerful optimization scheme based on tabu search, called discrete tabu search, has been proposed for sizing three stand-alone solar/wind/energy storage (battery) hybrid systems. For validating the applied algorithm effectiveness, the results are compared with the results found by the discrete harmony search. The obtained outcomes are compared on the basis of total annual cost. The components of the scheme are analyzed in different operating conditions by applying meteorological data in addition to real time information from three typical regions of Iran. According to the obtained data, applying ‘discrete tabu search’ leads to better outputs on the basis of mean, standard deviation and worst indexes.

Performance evaluation of artificial bee colony algorithm and its variants in the optimum design of steel skeletal structures

Artificial bee colony algorithm (ABC) has been efficiently used to obtain the optimum design of structures under design code limitations. There are several enhancements suggested to improve the performance of ABC even further in the literature. In this chapter, the performance of enhanced ABC variants in the optimum design of steel skeletal frames is studied. The mathematical model of such design problem when based on the provisions of design code requirements results in a discrete nonlinear programming problem. Seven variants of ABC is selected from the literature. These are standard artificial bee colony algorithm (sABC), artificial bee colony algorithm with orthogonal learning (OCABC), quick artificial bee colony algorithm (qABC), noel chaotic artificial bee colony algorithm (STOC-BC), directed artificial bee colony algorithm (dABC), improved artificial bee colony algorithm (NSABC) and artificial bee colony algorithm with Levy flight distribution (ABC_Levy). Seven optimum design algorithms are developed to obtain the solution of the design problem formulated considering design code provisions. Two steel space frames, one ten story and the other fifteen story are optimized using each of the seven optimum design techniques. The performance of each variant is observed and the optimum designs are compared. It is noticed after carrying out the statistical analysis that ABC_Levy and qABC algorithms outperform the other algorithms.

Application of artificial intelligence and evolutionary algorithms in simulation-based optimal design of a piezoelectric energy harvester

This paper tackles the problem of finding the optimal design parameters for a piezoelectric energy harvester. A new simulation-based optimization procedure is proposed with the goal of acquiring the optimal geometric and circuit design parameters that leads to higher energy harvesting efficiency and also enhances the obtained electrical power. The basis of the optimization platform is a numerical model of the energy harvesting system operating during electrical transient of charging an external storage capacitor. The model consists of a cantilever beam partially coated with piezoelectric patches, a non-linear interfacing and conditioning circuit, and a storage device. The numerical model simulates a complete energy harvesting scenario from piezoelectric transduction, to power enhancement and conditioning through interfacing circuit and energy storage. Two different case studies are considered for beams under harmonic tip-force, and harmonic base-excitation. Since performing multiple simulations in order to evaluate the objective function is computationally expensive and imposes time and space (memory) complexities, a more efficient Neural Network (NN) model is first trained based on a set of training data obtained from the numerical model. Performance and accuracy of the NN training is studied using available statistical methods. Second, a Genetic Algorithm (GA) optimization performs a block-box optimization procedure, using the trained Neural Network model for objective function evaluation. Finally, a thorough analysis of the optimal design parameters obtained from the optimization process is provided.

Compensation Network Optimal Design Based on Evolutionary Algorithm for Inductive Power Transfer System

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation.

Improved Wolf Pack Algorithm for Optimum Design of Truss Structures

In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems.

Self-Adaptive Cuckoo Search Algorithm for Optimal Design of Water Distribution Systems

Self-adaptive cuckoo search algorithm is used to optimize the design of water distribution system problems. It is proposed to dynamically adjust the two sensitive parameters of the algorithm, (i) step size control parameter ‘α’ and (ii) discovering probability parameter ‘Pa’ which largely govern the exploration and exploitation search strategies of the algorithm. These parameters are essential for enhancing the performance of the algorithm and normally the values of these parameters needs careful selection according to the type of problem. Single objective self-adaptive cuckoo search algorithm (SACSA) and multi-objective self-adaptive cuckoo search algorithm (SAMOSCA) are proposed in this study. Robustness and efficiency of these algorithms in single (minimization of cost) and multi-objective scenarios (minimization of cost and maximization of resilience) is validated using standard water distribution benchmark problems i.e. Two loop and Hanoi network. These are later applied to solve a medium size real-life water distribution system located at Pamapur, Telangana, India. A simulation-optimization based program combining the water distribution network simulation software EPANET 2.2 and MATLAB is used for computation. The proposed methodology has provided better results in terms of computational efficiency as well as found better solutions when compared to the previously reported results in both single and multi-objective scenarios. In the case of multi-objective problems, it has been observed that SAMOCSA has been able to find new points in pareto front when compared to the best-known pareto front reported in the literature. Self-adaptive cuckoo search algorithm has been found to be an attractive alternative in both exploration and exploitation of larger search spaces for finding better optimal solutions.

Set theoretical variants of the teaching–learning-based optimization algorithm for optimal design of truss structures with multiple frequency constraints

In this paper, a set theoretical framework is proposed for the population-based metaheuristic algorithms. Using the proposed framework, two new set theoretical variants of the teaching–learning-based optimization (TLBO) algorithm are developed. These algorithms are named as OST-TLBO and STMP-TLBO, which are acronyms for the “ordered set theoretical teaching–learning-based optimization” and “set theoretical multi-phase teaching–learning-based optimization”, respectively. The present framework can be applied to other population-based metaheuristic algorithms. In order to verify the stability and robustness of the presented algorithms, some optimization problems are examined. These problems include four truss optimization problems with multiple natural frequency constraints. Comparing the results obtained from the proposed algorithms with those of the standard version of the TLBO algorithm shows that the proposed set theoretical framework improves the performance of the standard TLBO in terms of robustness and convergence characteristics.

Optimal sensor network design for multi-scale, time-varying differential algebraic equation systems: Application to an entrained-flow gasifier refractory brick

An algorithm for optimal sensor network design for multi-scale, time-varying differential algebraic equation systems with non-separable dynamics is presented. As the process is time-varying, an integral normalized posterior error covariance of a multi-scale filter is minimized to obtain the optimal sensor locations. For reducing the computational cost, an adaptive sampling rate approach is considered for the slowly-varying variables. The algorithm is applied to a smart refractory brick with embedded sensors as part of an entrained-flow gasifier. Thermistors and interdigital capacitors are considered as candidate measurement technologies for estimating temperature and slag penetration profile along the gasifier wall. When the optimal set of sensors obtained from the algorithm is used for estimating temperature and slag penetration profiles in a multi-scale Kalman filter framework, satisfactory estimates are obtained despite high measurement noise and model mismatch.

Prof. Comparing H2 and H∞ Algorithms for Optimum Design of Tuned Mass Dampers under Near-Fault and Far-Fault Earthquake Motions

In this study, the robust optimum design of Tuned Mass Damper (TMD) is established. The H2 and H∞ norm of roof displacement transfer function are implemented and compared as the objective functions under Near-Fault (NF) and Far-Fault (FF) earthquake motions. Additionally, the consequences of different characteristics of NF ground motions such as forward-directivity and fling-step are investigated on the behavior of a benchmark 10-story controlled structure. The Colliding Bodies Optimization (CBO) is employed as an optimization technique to calculate the optimum parameters of the TMDs. The resulting statistical assessment shows that the H∞ objective function is rather superior to H2 objective function for optimum design of TMDs under NF and FF earthquake excitations. Finally, the robustness of the designed TMDs is evaluated under a large set of natural ground motions.

Design of Patient Specific Spinal Implant (Pedicle Screw Fixation) using FE Analysis and Soft Computing Techniques

This work uses genetic algorithm (GA) for optimum design of patient specific spinal implants (pedicle screw) with varying implant diameter and bone condition. The optimum pedicle screw fixation in terms of implant diameter is on the basis of minimum strain difference from intact (natural) to implantation at peri-prosthetic bone for the considered six different peri-implant positions. This design problem is expressed as an optimization problem using the desirability function, where the data generated by finite element analysis is converted into an artificial neural network (ANN) model. The finite element model is generated from CT scan data. Thereafter all the ANN predictions of the microstrain in six positions are converted to unitless desirability value varying between 0 and 1, which is then combined to form the composite desirability. Maximization of the composite desirability is done using GA where composite desirability should be made to go up as close as possible to 1. If the composite desirability is 1, then all 'strain difference values in 6 positions' are 0. The optimum solutions obtained can easily be used for making patient-specific spinal implants.

Synthesis and methodology for optimal design of a parallel remote center of motion mechanism: Application to robotic eye surgery

This work reports on the synthesis of a parallel Remote Center of Motion (RCM) mechanism, and an optimized design for the use-case of robot-assisted vitreoretinal surgery. A 2-DoF planar RCM mechanism is proposed and synthesised as part of a 4-DoF RCM mechanism. The proposed design substantially reduces the occupied volume at the end-effector. This solves a major problem present in related state-of-the-art, which poses limitations on sterile end-effector design and surgical instrument compatibility. Subsequently, an optimal design algorithm is proposed and implemented for the given use-case. The workspace is determined within mechanism constraints, after which performance parameters such as workspace coverage, potential energy, manipulability, reflected stiffness are determined for specific areas of interest within a desired workspace. Subsequently, these parameters are combined in a score function identifying an optimal kinematic design. When compared with the closely-related prior art, the resulting design shows improvements in relevant workspace coverage, reduced gravity compensation effort, and more isotropic manipulability. Overall reflected stiffness is reduced and should be taken into account in future design phases. Future work includes the integration of the kinematic design into a detailed conceptual design and a first prototype development.

Selection of Control Vector Switching Points for Circuit Optimization

The process of optimizing the circuit is formulated as a controlled dynamic system. A special control vector has been defined to redistribute the computational cost between circuit analysis and parametric optimization. This redistribution will help solve the task of optimizing the circuit for minimum CPU time. In this case, the task of optimizing the circuit with minimal CPU time can be formulated as the classical optimal control problem for minimizing some functional. The concept of the Lyapunov function of a controlled dynamic system is used to analyze the main characteristics of the design process. An analysis of the Lyapunov function and its time derivative makes it possible to predict the optimal structure of the control vector for constructing an optimal or quasi-optimal circuit design algorithm. The results are based on the previously discovered effect of accelerating the design process. In this case, the optimal structure of the control vector is determined, which minimizes processor time.

A Study on Stacking Sequence Design of Composite Fan Blades Using Multi-level Optimization

In this paper, the multi-level optimization method is proposed to design the optimal stacking sequence of composite fan blade more efficiently. The fan blade model used is a laminate with inconsistent stiffness because the thickness is not uniform throughout the model. This feature complicates the optimization problem and makes it difficult to apply optimal design algorithms that use only continuous or discrete variables. For this reason, we conducted the literature survey about combining the optimal design algorithm with continuous and discontinuous variables and developed optimization algorithm for the composite fan blade. Moreover, the design space of discrete variables is reduced through the optimal design of continuous variables. In the first-level, response surface method is used, with the design variables being the volume fraction of plies of the predefined angles of [0°/±45°/90°]. The optimization problem in the first-level aims at minimizing the tip displacement of the fan blade and constraints on other structural responses are imposed. At the second-level, the permutation genetic algorithm is used with the fiber orientation of each ply as design variables. In this level, we perform the optimal design of the stacking sequence using the results determined by prior level, while the composite design rule and compliance constraints are applied.

Butterfly optimization algorithm for optimum shape design of automobile suspension components

Abstract This article presents an implementation of one of the latest optimization methods of obtaining light vehicle designs. First, the problem of coupling with a bolted rim is optimized using the butterfly optimization algorithm (BOA). Finally, the BOA is used to solve the shape optimization of a vehicle suspension arm. It is utilized from the Kriging metamodeling method to obtain equations of objective and constraint functions in shape optimization. At the end of the research effort in this paper, the weight reduction of the suspension arm by using the BOA is 32.9 %. The results show the BOA’s ability to design better optimum components in the automotive industry.

A General Algorithm for Rectangular Parking Optimization Design

The optimization design of parking is a comprehensive programming solution with a relatively large practical value. Determining the optimal design of the parking lot is the main goal of this paper. Before the algorithm design, this paper first deduces the calculation formula of the width of the lane in the parking lot; then refines the parking lot design problem into the choice of the layout of the double-side parking channel and the layout of the single-side parking channel, and the choice scheme is based on the greedy algorithm. On this basis, we establish a general optimization design algorithm for the rectangular parking lot, which is essentially a programming algorithm. Finally, this paper selects an example to solve the general algorithm by SA algorithm. It proves that the general algorithm meets the requirements and has practical value.

A set theoretical shuffled shepherd optimization algorithm for optimal design of cantilever retaining wall structures

In this paper, a recently developed meta-heuristic algorithm, shuffled shepherd optimization algorithm (SSOA), is employed for optimal design of reinforced concrete cantilever retaining wall structures under static and seismic loading conditions. The concepts of set theory are employed to express the SSOA in a set theoretical term. The Rankine and Coulomb theories are utilized in order to estimate the lateral earth pressures under the static loading condition, whereas the Mononobe–Okabe method is employed for the seismic one. Optimization aims to minimize the cost of cantilever retaining wall while satisfying some constraints on stability and strength limits. The design is based on the requirements of ACI 318-05. In order to investigate the efficiency of the SSOA, one benchmark cantilever retaining wall problem is considered from the literature. Comparing the optimization results obtained by the SSOA with those of other meta-heuristics shows the efficient performance of the SSOA in both aspects of accuracy and convergence rate.

Sensitivity Assessment using Genetic Algorithm for Optimal Design of RC Ring Wall Foundation of Liquid Storage Tanks

Hydrocarbons and chemical industries extensively use storage tanks made of steel for storing large quantities of liquids. These tanks are typically supported on a RC ring wall foundation. This paper presents a method to minimize the cost of RC Ring Wall Foundations and study the sensitivity of this cost towards the different design parameters. The optimization process is developed through the use of genetic algorithm which simulates the biological evolution for the fittest (optimized) organism Previous studies on use of genetic algorithm in structural engineering has been applied to different structures like frames beams, columns etc. This paper extends the use of genetic algorithm to ring wall foundations of liquid storage tanks. The objective function for optimization includes the costs of concrete, steel, formwork and excavation whose sensitivity is analysed for parameters like grade of steel, concrete, seismic and wind loading for different tank sizes. All the constraints functions are set to meet the design requirements as per Indian Standard Codes and construction industry practices. Eight cases of parametric study are considered in order to illustrate the applicability of the genetic algorithm design model. It is concluded that this approach is economically more effective compared to conventional methods for design and sensitivities of different design parameters can be quickly assessed. Additionally this design methodology can be extended to deal with other types of structures as well.

An Optimization Design of Contact Interface Material Stiffness for Improving the Uniformity of the Contact Pressure

Abstract Material stiffness, a significant parameter of a contact interface, is investigated to improve the uniformity of the contact pressure. A contact interface material stiffness optimization design algorithm is developed based on the modified solid isotropic material with the penalization (SIMP) method. The uniformity of the contact pressure field is represented by its variance and is defined as the optimization objective. A node-to-node frictionless elastic contact theory is adopted to perform the contact analysis. The effectiveness of the interface material stiffness design for improving the uniformity of the contact surface is verified based on two contact cases. Because the relationship between the material stiffness and the hard-and-soft degree of a contact interface is always a positive correlation, the results in this paper could be extended so that the design of the contact interfaces’ hard-and-soft degree will improve the distributing uniformity of the contact surface.

The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components

Abstract As a result of the requirements imposed by international organizations and governments on fuel emissions, there is a growing interest in the design of lightweight vehicles with low-fuel emissions. Metaheuristic methods have been widely used for the optimum design of vehicle components in recent years for which successful results have been reported. Encouraged by such results obtained from the methods mentioned, the Henry gas solubility optimization algorithm (HGSO), a recently developed optimization method, is used to solve the shape optimization of a vehicle brake pedal to prove how HGSO can be used for solving shape optimization problems. This paper is the first application of the HGSO in connection with real-world optimization problems in the literature. The results show HGSO's ability to design better optimal components in the automotive industry.

Aggregate Fluctuations in Networks With Drift-Diffusion Models Driven by Stable Non-Gaussian Disturbances

The focus of this article is to quantify measures of aggregate fluctuations for consensus-seeking networks subject to exogenous noise with $\alpha$ -stable distributions. This type of noise is generated by a class of random measures with heavy-tailed probability distributions. We define a cumulative-scale parameter over the output variables, as a measure of aggregate fluctuation. We show that this class of measures can be characterized implicitly, because finding its explicit form in terms of network parameters is, in general, almost impossible. To this end, we offer several tractable upper bounds in terms of Laplacian spectrum and statistics of the input noise. Our results suggest that relying on Gaussian-based optimal design algorithms will result in nonoptimal solutions for dynamical networks that operate in the face of non-Gaussian noise.