基于改进教学算法的无人机航路规划

针对传统教-学优化（TLBO）算法进行航路规划时收敛速度慢、容易陷入局部最优的问题,提出一种自适应交叉教-学优化（AC-TLBO）算法。首先,该算法令传统教-学优化（TLBO）算法的教学因子随着迭代次数而发生变化,提高算法的学习速度;其次,当算法可能要陷入局部最优时,加入一定的扰动,使算法尽可能地跳出局部最优;最后,为了进一步提升算法的收敛效果,在算法中引入遗传算法的交叉环节。利用传统教-学优化（TLBO）算法、自适应交叉教-学优化（AC-TLBO）算法和量子粒子群优化（QPSO）算法进行无人机航路规划,仿真结果表明,在10次规划中,自适应交叉教-学优化（AC-TLBO）算法有8次找到了全局最优路径,而传统教-学优化（TLBO）算法和量子粒子群优化（QPSO）算法分别只找到了2次和1次;而且自适应交叉教-学优化（AC-TLBO）算法的收敛速度高于另外两种算法。     

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。     

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。     

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。     

采用差分进化策略的入侵杂草改进算法及函数优化应用

针对入侵杂草优化算法易出现早熟且收敛速度较慢的问题,提出一种具有差分进化策略的入侵杂草算法。利用差分进化策略较强的开发能力,对种子进行交叉变异选择操作以帮助算法跳出局部最优;同时,为了提高算法的收敛速度和种群多样性,提出对杂草进行初始化并采用基于混沌反向学习的初始化方法。对8个标准测试函数进行的仿真实验表明:与标准杂草优化、差分进化及混合杂草优化算法相比,提出的改进算法具有较快的收敛速度、较高的收敛精度及较强的搜索全局最优解的能力。     

混沌时间序列的自适应变异差分进化ANFIS预测

提出了一种基于自适应变异差分进化（AMDE）算法的ANFIS模型对混沌时间序列进行预测的方法,该方法采用自适应变异差分进化算法和最小二乘法相结合的混合学习算法对ANFIS网络结构参数进行优化设计,利用差分进化算法的全局寻优能力对ANFIS网络前件参数进行优化,而网络的结论参数采用最小二乘法优化,混合学习算法提高了网络参数辨识的收敛速度和系统的全局收敛性,仿真实验结果表明了该方法的有效性。     

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。     

差分进化粒子群混合优化算法的研究与应用

对基本粒子群算法（PSO）和差分进化算法（DE）进行了分析,有机结合两种进化算法提出了一种新型差分进化粒子群混合优化算法,该算法将优化过程分成两阶段,两分群分别采用PSO算法和DE算法同时进行。迭代过程中引入进化速度因子并通过群体间的信息交流阻止算法陷入局部最优。对4个高维复杂函数寻优测试表明算法的鲁棒性、收敛速度和精度,全局搜索能力均优于常规PSO和DE。将提出的改进算法用于乙烯收率软测量建模,应用结果表明模型精度较高、泛化性能较好。     

改进蚁狮优化算法及其工程应用

针对蚁狮优化算法（ALO）在求解工程优化问题时易陷入局部最优及收敛速度慢等缺陷,提出一种基于Levy飞行和差分进化的改进蚁狮优化算法（LDALO）。改进算法对ALO中的蚂蚁进行差分进化操作,从而改善种群多样性,避免算法陷入局部最优并提高算法全局搜索能力。精英引导的Levy飞行被用于蚂蚁位置更新,以加快算法收敛速度。改进算法还在蚁狮捕食蚂蚁后对蚁狮进行差分变异,以提高算法的寻优精度。仿真实验基于10个基准函数进行,其结果显示LDALO较其他算法收敛速度更快,寻优精度更高。在无线传感器网络覆盖优化、压力容器设计、拉压弹簧设计等工程优化问题中的应用,验证了LDALO 的适用性和有效性。     

动态自适应的教学优化算法

针对教学优化算法（teaching-learning-based optimization algorithm,TLBO）容易陷入局部最优和寻优速度慢等缺陷,提出一种动态自适应的教学优化算法（ITLBO）。首先,提出一种与sigmoid函数有关的惯性权重,并将其引入到教师阶段和学生阶段,以动态调整种群的移动步长,从而抑制算法前期种群多样性的衰减,扩大寻优区域,增强算法后期局部精细化搜索能力,提高求解精度;其次,在教师阶段,利用一种动态更新教师的机制来增强教师的教学水平,保证学生及时地向全局最优解学习,提高算法收敛速度;再次,在学生阶段,提出一种自学与向最好学生学习相结合的学习方式,让学生全面发展,从而提高算法的搜索能力和收敛速度。为验证ITLBO的有效性,采用16个标准测试函数,对惯性权重的有效性和各算子对算法整体的贡献度进行测试,对TLBO、RTLBO、EPGTOA和ITLBO进行仿真对比实验。一系列测试结果表明,ITLBO具有更好的收敛能力和稳定性。     

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.     

An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art 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.     

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.     

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。     

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.