磁悬浮带式输送机电磁结构优化

常规磁悬浮带式输送机采用永磁体和电磁铁组合的电磁结构,在磁悬浮支承力需求较高的工况条件下具有易发热、电流损耗大等问题。为解决该问题,提出了一种基于Halbach阵列的电磁结构。以电磁结构磁感应强度最大为目标函数,以电磁结构尺寸和磁感应强度分布范围为约束条件,建立了电磁结构优化数学模型。针对教与学优化(TLBO)算法用于求解电磁结构优化数学模型时容易陷入局部最优的问题,提出了一种改进的TLBO算法,该算法通过筛选引入新种群及改进教学阶段和互学阶段的学习方式,增强种群的多样性和搜索能力。测试结果表明,改进的TLBO算法的准确性和稳定性均优于标准TLBO算法。采用改进的TLBO算法对磁悬浮带式输送机电磁结构优化数学模型进行求解,得到最优电磁结构参数:Halbach阵列中单个永磁体高7 mm、宽9 mm,永磁体块数为7。实验结果表明,相同尺寸条件下,基于Halbach阵列的电磁结构最大磁感应强度相对基于永磁体的电磁结构提高了47.69%。     

Optimal design of truss structures for size and shape with frequency constraints using a collaborative optimization strategy

A new metaheuristic strategy is proposed for size and shape optimization problems with frequency constraints. These optimization problems are considered to be highly non-linear and non-convex. The proposed strategy extends the idea of using a single optimization process to a series of collaborative optimization processes. In this study, a modified teaching-learning-based optimization (TLBO), which is a relatively simple algorithm with no intrinsic parameters controlling its performance, is utilized in a collaborative framework and introduced as a higher-level TLBO algorithm called school-based optimization (SBO). SBO considers a school with multiple independent classrooms and multiple teachers with inter-classroom collaboration where teachers are reassigned to classrooms based on their fitness. SBO significantly improves the both exploration and exploitation capabilities of TLBO without increasing the algorithm's complexity. In addition, since the SBO algorithm uses multiple independent classrooms with interchanging teachers, the algorithm is less likely to be influenced by local optima. A parametric study is conducted to investigate the effects of the number of classes and the class size, which are the only parameters of SBO. The SBO algorithm is applied to five benchmark truss optimization problems with frequency constraints and the statistical results are compared to other optimization techniques in the literature. The quality and robustness of the results indicate the efficiency of the proposed SBO algorithm.     

Synthesis of thinned concentric circular antenna arrays using teaching‐learning‐based optimization

In this article, the design of thinned concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the teaching‐learning‐based optimization (TLBO) is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:443–450, 2014.     

An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

Teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self-feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability.     

基于讨论组和自主学习的教与学优化算法*

教与学优化算法(teaching-learning-based optimization, TLBO) 是一种模仿教学过程的新型启发式优化算法。针对TLBO 算法寻优精度低、稳定性差的特点, 提出了基于讨论组和自主学习的教学优化算法DSTLBO(discussion group and self-learning TLBO)。在原TLBO算法的“教”阶段当中加入了小组讨论,随机将全体同学分成若干组,通过组内学生向本组中学习最好的组长学习,提高了算法的局部开发和寻优能力;组长受老师和组内同学影响进行变异,提高了算法的探索能力;在“教”、“学”阶段后,每个学生进入“自我学习”阶段,从而提高了算法的全局搜索能力。通过对8个复杂的Benchmark函数的测试表明：DSTLBO 算法与基本TLBO算法和其经典改进算法ETLBO算法相比,在寻优精度、稳定性和收敛速度方面更具优势。     

一种优化神经网络的教与学优化算法

为了提高BP神经网络的输出精度,提出一种改进的教与学优化算法进行神经网络中的权值和阈值的优化调整.算法对基本的教与学优化算法的“教”阶段和“学”阶段分别进行改进,并提出一种“自学”机制来增强算法的学习能力.通过函数拟合实验和拖拉机齿轮箱故障诊断实验进行算法性能测试,结果表明,与遗传算法和基本的教与学优化算法相比,该算法具有收敛速度快、求解精度高等优势.     

基于K-均值的“教”与“学”优化算法

在解决复杂多峰优化问题时,传统的"教"与"学"优化算法易于陷入局部搜索且优化效率较低.针对此问题,提出了一种基于K-均值的"教"与"学"优化改进算法,算法采用K-均值来降低种群规模,又针对"教"和"学"两个阶段进行相应改进,提高全局收敛速度;还加入了"变异"操作来避免算法陷入局部最优.实验对7个单峰值优化问题和2个有代表性的多峰值优化问题进行优化,并与手榴弹爆破算法和传统"教"与"学"优化算法进行比较,实验结果表明,该改进算法在单峰和多峰测试函数中,均能快速高效地寻得全局最优解,优于原始"教"与"学"优化算法.     

Fuzzy adaptive teaching–learning-based optimization for global numerical optimization

Teaching–learning-based optimization (TLBO) is one of the latest metaheuristic algorithms being used to solve global optimization problems over continuous search space. Researchers have proposed few variants of TLBO to improve the performance of the basic TLBO algorithm. This paper presents a new variant of TLBO called fuzzy adaptive teaching–learning-based optimization (FATLBO) for numerical global optimization. We propose three new modifications to the basic scheme of TLBO in order to improve its searching capability. These modifications consist, namely of a status monitor, fuzzy adaptive teaching–learning strategies, and a remedial operator. The performance of FATLBO is investigated on four experimental sets comprising complex benchmark functions in various dimensions and compared with well-known optimization methods. Based on the results, we conclude that FATLBO is able to deliver excellence and competitive performance for global optimization.

     

融合头脑风暴思想的教与学优化算法

针对教与学优化（TLBO）算法在求解高维问题时表现出的收敛速度慢、解精度低、易陷入于局部最优的问题,提出了一种融合头脑风暴思想的改进教与学优化算法（ITLBOBSO）。在该算法中设计了一种新的“学”算子,并以其替换TLBO算法中的“学”。该算法在种群的迭代过程中,当前个体首先执行“教”算子。随后,在种群中随机选择两个个体,令其中优秀的个体与当前个体执行头脑风暴式学习,提升当前个体的状态。为了赋予算法早期良好的探索能力和后期对新解的开发能力,在该算子的公式中引入柯西变异和一个与迭代次数关联的随机参数。进行的一系列的仿真实验表明,与TLBO算法相比,所提算法在11个Benchmark函数上的解精度、鲁棒性和收敛速度都有大幅度提升。在2个约束工程优化问题上,ITLBOBSO所求得的耗费成本比TLBO算法降低了4个百分点。由此验证了所提出的机制对克服TLBO弱点的有效性,所提算法适合用来求解较高维度的连续优化问题。     

Topology optimization of an offshore jacket structure considering aerodynamic,hydrodynamic and structural forces

The present work focused on the optimization of offshore wind turbine structure which can sustain different environmental conditions and is of the least cost. Size and topology optimization is carried out for the jacket structure from the National Renewable Energy Laboratory (NREL) [used in the Offshore Code Comparison Collaboration Continuation (OC4) project] by using teaching learning-based optimization (TLBO) algorithm and genetic algorithm (GA). The optimization process is carried out in Matlab along with the time-dependent dynamic wind turbine simulation with the aerodynamic, hydrodynamic and structural forces in the fatigue, aerodynamics, structures, and turbulence code (FAST) from NREL. This is an innovative process which can be used to substitute the time-consuming construction of a wind turbine for its analysis. In this work, both static and dynamic analyses are carried out for simultaneous size and topology optimization. The forces applied to the structure are realistic in nature and fatigue analysis is carried out to ensure that the structure does not fail during its design life. This ensures that the simulation is more accurate and realistic as compared with other analysis. The results showed that the TLBO algorithm is effective compared to GA in terms of size and topology optimization. Further, the other state-of-the art algorithms from the Congress on Evolutionary Computation (CEC) such as differential evolution, LSHADE, multi-operator EA-II, effective butterfly optimizer, and unified differential evolution are also implemented and the comparative results of all the algorithms are presented.

     

A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching–learning-based optimization (TLBO)–differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO–DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.     

具有自学机制和退火选择的教学优化算法

为了克服教学优化(TLBO)算法容易早熟,解精度低的弱点,提出一种具有教师自学和学生选择学习的改进教学优化算法。在每次迭代过程中教师个体首先通过反向学习(OBL),实现教师的自我提高,加强优秀个体周围邻域的搜索,引导算法向包含全局最优的解空间逼近,保证算法具有较好的平衡和探索能力。学生个体通过随机执行反向学习进行自学习,同时亦向教师个体进行学习,计算两种学习方法后的状态相对教师个体的突跳概率,并以此概率为基础进行轮盘赌产生子个体。通过在多个标准测试函数上的实验仿真并与相关的算法对比,结果表明所提出的改进算法具有更高的收敛速度和收敛精度。     

Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems

A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.     

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

This paper deals with the design of a novel fuzzy proportional–integral–derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching–learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.     

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

融合头脑风暴思想的教与学优化算法

针对教与学优化（TLBO）算法在求解高维问题时表现出的收敛速度慢、解精度低、易陷入于局部最优的问题，提出了一种融合头脑风暴思想的改进教与学优化算法（ITLBOBSO）。在该算法中设计了一种新的“学”算子，并以其替换TLBO算法中的“学”。该算法在种群的迭代过程中，当前个体首先执行“教”算子。随后，在种群中随机选择两个个体，令其中优秀的个体与当前个体执行头脑风暴式学习，提升当前个体的状态。为了赋予算法早期良好的探索能力和后期对新解的开发能力，在该算子的公式中引入柯西变异和一个与迭代次数关联的随机参数。进行的一系列的仿真实验表明，与TLBO算法相比，所提算法在11个Benchmark函数上的解精度、鲁棒性和收敛速度都有大幅度提升。在2个约束工程优化问题上，ITLBOBSO所求得的耗费成本比TLBO算法降低了4个百分点。由此验证了所提出的机制对克服TLBO弱点的有效性，所提算法适合用来求解较高维度的连续优化问题。     

Shape structural optimization with an interior point nonlinear programming algorithm

The aim of this paper is to study the implementation of an efficient and reliable technique for shape optimization of solids, based on general nonlinear programming algorithms. We also study the practical behaviour for this kind of applications of a quasi-Newton algorithm, based on the Feasible Direction Interior Point Method for nonlinear constrained optimization. The optimal shape of the solid is obtained iteratively. At each iteration, a new shape is generated by B-spline curves and a new mesh is automatically generated. The control point coordinates are given by the design variables. Several illustrative two-dimensional examples are solved in a very efficient way. We conclude that the present approach is simple to formulate and to code and that our optimization algorithm is appropriate for this problem. Received May 12, 1999     

A teaching–learning based optimization approach for economic design of X-bar control chart

Being simple to use X-bar control chart has been most widely used in industry for monitoring and controlling manufacturing processes. Measurements of a quality characteristic in terms of samples are taken from the production process at regular interval and the sample means are plotted on this chart. Design of a control chart involves the selection of three parameters, namely the sample size (n), the sampling interval (h) and the width of control limits (k). In case of economic design, these three control chart parameters are selected in such a manner that the total cost of controlling the process is the least. The effectiveness of this design depends on the accuracy of determination of these three parameters. In this paper, a new efficient and effective optimization technique named as teaching–learning based optimization (TLBO) has been used for the global minimization of a loss cost function expressed as a function of three variables n, h and k in an economic model of X-bar chart based on unified approach. In this work, the TLBO algorithm has been modified to simplify the tuning of teaching factor. A MATLAB computer program has been developed for this purpose. A numerical example has been solved and the results are found to be better than the earlier published results. Further, the sensitivity analysis using fractional factorial design and analysis of variance have been carried out to identify the critical process and cost parameters affecting the economic design.     

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.     

一种基于反思机制的教与学优化算法

针对教与学优化(Teaching-learning-based optimization,TLBO)算法中存在的易于陷入局部最优以及收敛速度较慢等问题,提出了基于反思机制的TLBO算法。为提高算法的全局搜索和局部收敛能力,在教学过程中利用教师反思来增强教师教学水平,进一步提高学生知识技能,同时学生实现自我反思,达到同步提高的目的。利用基准测试函数对算法进行了性能测试,实验结果表明,改进后的TLBO算法具有更好的寻优性能。