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.     

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。     

差分进化粒子群算法求解发电资源调度问题

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于差分进化粒子群的调度算法。设计了水火电力系统资源调度问题的数学模型,给出了差分进化粒子群优化算法的框架,通过PSO种群和DE种群之间的信息交流机制以寻求全局最优位置,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,该算法能有效解决水火发电资源调度问题,具有较好的应用价值。     

融合差分变异的教-学优化算法

教-学优化算法（TLBO）是模拟班级中的教学过程和学习过程而提出的一种新型智能优化算法。为了改善教-学优化算法的性能,结合差分进化算法,提出一种融合差分变异的教-学优化算法（DMTLBO）。该算法提出自适应的教学因子,根据差分进化算法中变异策略,对学习阶段迭代方程进行改进,使得学习者的学习能力不仅受到学习者之间的相互影响,而且还受到当前最好学习者的影响,提高了算法的收敛速度。仿真实验表明,该算法的收敛速度和寻优精度均优于TLBO算法、PSO算法以及DE算法。     

基于改进教学算法的车间作业调度问题

为求解车间作业调度问题,提出一种基于个体差异化自学习的改进教学算法.针对教学算法局部搜索能力不高的缺陷, 提出学生不仅应向能力好的学习者学习,亦应进行有差异的自我学习.通过学习者的完工时间评估学生的学习能力,提出学习次数概念,并设计自学习算子,完善学生阶段的更新,提高算法的局部搜索能力.最后,对OR-Library中的标准仿真实例进行实验,结果表明改进教学算法是有效的,其在收敛精度和鲁棒性能上均有较好的提高.     

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.     

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.     

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%。     

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.     

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.     

Teaching and peer-learning particle swarm optimization

Most of the recent proposed particle swarm optimization (PSO) algorithms do not offer the alternative learning strategies when the particles fail to improve their fitness during the searching process. Motivated by this fact, we improve the cutting edge teaching–learning-based optimization (TLBO) algorithm and adapt the enhanced framework into the PSO, thereby develop a teaching and peer-learning PSO (TPLPSO) algorithm. To be specific, the TPLPSO adopts two learning phases, namely the teaching and peer-learning phases. The particle firstly enters into the teaching phase and updates its velocity based on its historical best and the global best information. Particle that fails to improve its fitness in the teaching phase then enters into the peer-learning phase, where an exemplar is selected as the guidance particle. Additionally, a stagnation prevention strategy (SPS) is employed to alleviate the premature convergence issue. The proposed TPLPSO is extensively evaluated on 20 benchmark problems with different features, as well as one real-world problem. Experimental results reveal that the TPLPSO exhibits competitive performances when compared with ten other PSO variants and seven state-of-the-art metaheuristic search algorithms.     

Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm

Teaching–learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching–learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of a two stage thermoelectric cooler (TEC). Two different arrangements of the thermoelectric cooler are considered for the optimization. Maximization of cooling capacity and coefficient of performance of the thermoelectric cooler are considered as the objective functions. An example is presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization obtained by using the modified TLBO are validated by comparing with those obtained by using the basic TLBO, genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms.     

新型教与同伴学习粒子群算法求解作业车间调度问题

针对最小化最大完工时间的单目标作业车间调度问题,提出了新型教与同伴学习粒子群算法。通过教学阶段融合多邻域搜索,采用多样性变异策略以及同伴学习阶段采用混合学习策略三个方面的改进操作,扩大了种群的多样性,避免算法陷入局部最优,算法收敛速度和寻优性能有了显著提高。通过作业车间调度问题FT、LA系列测试实例的对比实验,验证了新型教与同伴学习粒子群算法是解决单目标作业车间调度问题的有效方法。     

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。     

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.     

改进的双种群竞争教与学优化算法

为了克服教与学优化算法在求解高维函数问题时,容易早熟,收敛速度慢,解精度低的弱点,提出一种引入竞争机制的双种群教与学优化算法。在该算法中设置两个教师,并基于帝国竞争优化机制将种群初始化成为两个学生种群,每一个教师带领自己的种群独立进化。在进化过程中,教师可以利用自己的影响力将外种群内的成员吸收进入自己的种群。为了提高教师个体的学习能力,引入反向学习机制。在多个Benchmark函数的测试表明,改进算法解精度较高,全局收敛能力强,适合求解较高维度的函数优化问题。     

基于自主学习行为的教与学优化算法

针对教与学优化（TLBO）算法收敛精度较低、易于早熟收敛等问题,提出一种基于自主学习行为的教与学优化算法（SLTLBO）。SLTLBO算法为学生构建了更加完善的学习框架,学生在完成常规"教"阶段与"学"阶段的学习行为之外,将进一步对比自己与教师、最差学生的差异,自主完成多样化的学习操作,以提高自己的知识水平,提高算法的收敛精度;同时学生通过高斯搜索的自主学习反思行为跳出局部区域,实现更好的全局搜索。利用10个基准测试函数对SLTLBO算法进行了性能测试,并将SLTLBO算法与粒子群优化（PSO）算法、智能蜂群（ABC）算法以及TLBO算法进行结果比对,实验结果验证了SLTLBO算法的有效性。     

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。     

A hybrid differential evolution approach based on surrogate modelling for scheduling bottleneck stages

Surrogate modelling based optimization has attracted much attention due to its ability of solving expensive-to-evaluate optimization problems, and a large majority of successful applications from various fields have been reported in literature. However, little effort has been devoted to solve scheduling problems through surrogate modelling, since evaluation for a given complete schedule of these complex problems is computationally cheap in most cases. In this paper, we develop a hybrid approach for solving the bottleneck stage scheduling problem (BSP) using the surrogate modelling technique. In our approach, we firstly transform the original problem into an expensive-to-evaluate optimization problem by cutting the original schedule into two partial schedules using decomposition, then a surrogate model is introduced to, quickly but crudely, evaluate a given partial schedule. Based on the surrogate model, we propose a differential evolution (DE) algorithm for solving BSPs in which a novel mechanism is developed to efficiently utilize the advantage of the surrogate model to enhance the performance of DE. Also, an improved adaptive proximity-based method is introduced to balance the exploration and exploitation during the evolutionary process of DE. Considering that data for training the surrogate model is generated at different iteration of DE, we adopt an incremental extreme learning machine as the surrogate model to reduce the computational cost while preserving good generalization performance. Extensive computational experiments demonstrate that significant improvements have been obtained by the proposed surrogate-modelling based approach.     

差分进化混合粒子群算法求解项目调度问题*

针对求解资源受限项目调度问题（RCPSP）,提出了基于差分进化（DE）的混合粒子群算法（PSODE）。通过在PSO种群和DE种群之间建立一种信息交流机制,使信息能够在两个种群中传递,以避免个体因错误的信息判断而陷入局部最优点。采用标准测试函数和具体算例进行检验,结果表明PSODE算法可以较好地解决RCPS问题。