An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase

An improved teaching–learning-based optimization with neighborhood search for applications of ANN

An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Teaching Learning Based Optimization (TLBO) algorithm simulate the teaching learning peculiarity of a classroom to solve multidimensional, linear and nonlinear problems with appreciable efficiency. In order to accelerate the execution time of software implementation, the TLBO algorithm is implemented on hardware. Then the TLBO hardware is developed as TLBO Intellectual Property and it is interfaced as a peripheral to the System on Chip platform. We compared the performance of the floating point TLBO IP is realized by solving the benchmark functions, results 183–224X times faster than the software implementation of the same algorithm. As a case study, the same TLBO IP is used to solve the spectrum allocation problem by optimizing Max-Sum-Reward (MSR) function and it results 69–78X times faster than the software implementation of the same algorithm.

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.

The paper presents an application of Teaching Learning-Based Optimization (TLBO) algorithm to improve the performance of self-excited induction generators (SEIG).Two control methods of SEIG have been studied.The first method, the TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with constant prime mover speed.The drawback of this method is the generator frequency decreases with load and to overcome this disadvantage, the other control method is proposed.In the proposed method, the TLBO is used to obtain optimal capacitance and prime mover speed to have rated load voltage and frequency.The Static VAR Compensator (SVC) of fixed capacitor and controlled reactor is used to control the reactive power.The parameters of SVC are obtained by using TLBO algorithm.The performance of the SEIG at different loads and prime mover speeds using TLBO algorithm is realized.A whole system of three phase induction generator and SVC is established under MatLab/Simulink environment.The performance of the SEIG is demonstrated on two different ratings (i.e. 10 hp and 2hp).An experimental setup is built-up using a 2 hp induction motor to confirm the theoretical analysis.Good agreement between results confirms and signifies the viability of the proposed TLBO-based methodology. ‫الع‬ ‫الولخص‬ ‫شبى‬‫ٔانرعهى‬ ‫انرعهيى‬ ‫خٕاسصييّ‬ ‫ذطثيق‬ ‫انثحث‬ ‫يقذو‬ ( ‫اساط‬ ‫عهى‬ ‫االيثم‬ TLBO ) ‫اداء‬ ‫نرحسيٍ‬ ‫انًٕنذ‬ ‫انحثى‬ ‫راذى‬ ‫ٔقذ‬ ‫انرغزيح.‬‫ان‬ ‫انحثى‬ ‫انًٕنذ‬ ‫اداء‬ ‫خٕاص‬ ‫فى‬ ‫نهرحكى‬ ‫طشيقريٍ‬ ‫دساسح‬ ‫ذًد‬ ً ‫عضٔل.‬‫تاسرخذاو‬ ‫انًثهى‬ ‫انًكثف‬ ‫قيى‬ ‫ذحذيذ‬ ‫فيٓا‬ ‫يرى‬ ‫االٔنى‬ ‫انطشيقح‬ ‫خٕاسصييّ‬ ( TLBO ) ‫خٓذ‬ ‫نرثثيد‬ ‫االحًال‬ ‫كم‬ ‫عُذ‬ ‫انًٕنذ‬ ‫عُذ‬ ‫انًٕنذ‬ ‫ذشدد‬ ‫ذغيش‬ ‫فى‬ ‫انطشيقّ‬ ‫ْزِ‬ ‫عية‬ ‫ٔيرًثم‬ ‫انًٕنذ.‬‫سشعّ‬ ‫ثثاخ‬ ‫تاسرخذاو‬ ‫انًٕنذ‬ ‫ٔسشعّ‬ ‫انًكثف‬ ‫يٍ‬ ‫نكال‬ ‫انًثهى‬ ‫انقيى‬ ‫ذعييٍ‬ ‫ذى‬ ‫حيث‬ ‫انثاَيّ‬ ‫انطشيقّ‬ ‫اقرشاح‬ ‫ذى‬ ‫انعية‬ ‫ْزا‬ ‫ٔنرالفى‬ ‫االحًال.‬‫ذغيش‬ ‫يع‬ ‫خٕاسصييّ‬ ( TLBO ) ‫عُذ‬ ‫انًٕنذ‬ ‫ٔذشدد‬ ‫خٓذ‬ ‫نرثثيد‬ ‫انساكُّ‬ ‫انفعانح‬ ‫انغيش‬ ‫انقذسج‬ ‫يعٕضاخ‬ ‫اسرخذاو‬ ‫ذى‬ ‫انثحث‬ ‫فى‬ ‫االحًال.

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.

Teaching-Learning-Based Optimization (TLBO) algorithm is an evolutionary powerful algorithm that has better global searching capability. However, in the later period of evolution of the TLBO algorithm, the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions, which lead to a trap in local optima and premature convergence. This paper presents an improved version of the TLBO algorithm based on Laplace distribution and Experience exchange strategy (LETLBO). It uses Laplace distribution to expand exploration space. A new experience exchange strategy is applied to make good use of experience information to identify more promising solutions to make the algorithm converge faster. The experimental performances verify that the LETLBO algorithm enhances the solution accuracy and quality compared to original TLBO and various versions of TLBO and is very competitive with respect to other very popular and powerful evolutionary algorithms. Finally, the LETLBO algorithm is also applied to parameter estimation of chaotic systems, and the promising results show the applicability of the LETLBO algorithm for problem-solving.

Teaching---learning-based optimisation (TLBO) is an emerging gradient-free optimisation algorithm inspired by interactions between students and teacher in classrooms. TLBO has no control parameter to be tuned by user. This property makes it popular in research community. It has been successfully applied to challenging optimisation problems in different areas. In this study, TLBO is assisted to find optimal setting of thyristor-controlled series compensators in electric power systems. The experiments have been done for both N-1 and N-2 line outage contingencies. The results show that TLBO performs well in solving this problem.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules

An improved teaching–learning-based optimization algorithm for solving global optimization problem

Image segmentation has been used widely for detection and extraction of objects in digital images. Thresholding is one of the effective image segmentation techniques. Fuzzy 2-partition entropy is used effectively for the selection of threshold value. Fuzzy 2-partition entropy required the optimization of some parameters for the selection of threshold value. Teaching-Learning-Based Optimization (TLBO) algorithm has been applied on fuzzy 2-partition entropy to find these parameters and subsequent threshold value. Normally, fuzzy 2-partition Shannon entropy is used for thresholding. In the proposed research work, fuzzy 2-partition Kapur entropy is explored for the evaluation of its potential for selecting optimal threshold value using TLBO algorithm. The present work measures the performance comparison of fuzzy 2-partition Kapur entropy using TLBO with fuzzy 2-partition Shannon entropy using TLBO. The experiments are carried out on standard test images from benchmark dataset. Peak signal to noise ratio, uniformity and structural similarity index are the three different measures which have been used to compare the performance. Results demonstrate that the performance of fuzzy 2-partition Kapur entropy using TLBO is quite promising.