Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Tolerance design has become a very key issue in product and process development because of an informal compromise between functionality, quality, and manufacturing cost. The problem formulation becomes complex with simultaneous selection of design and manufacturing tolerances and the optimization problem is difficult to solve with the traditional optimization techniques. In this paper, a recently developed optimization algorithm called teaching-learning-based optimization (TLBO) is used for optimal selection of design and manufacturing tolerances with an alternative manufacturing process to obtain the optimal solution which is nearer to global optimal solution. Three problems are considered and these are: overrunning clutch assembly, knuckle joint assembly with three arms, and a helical spring. Out of these three problems, the problems of overrunning clutch assembly and knuckle joint assembly with three arms are multi-objective optimization problems and the helical spring problem is a single-objective problem. The comparison of the proposed algorithm is made with the genetic algorithm (GA), Non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective particle swarm optimization algorithm (MOPSO). It is found that the TLBO algorithm has produced better results when compared to those obtained by using GA, NSGA-II, and MOPSO algorithms.

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.

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

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

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.

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.

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.

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.

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

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.

A global optimization method for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the global topology optimization method in flow problems, this research works on two single-objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and the multi-objective optimization problem, which combines these two problems. The shape of flow channels is represented by the level set function, and the pressure loss and the temperature of the channels are evaluated by the Building-Cube Method (BCM), which is a Cartesian-mesh CFD approach. The proposed method resulted in an agreement with previous study in the single-objective problems in its topology, and achieved global exploration of non-dominated solutions in the multi-objective problem.

After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.

The optimal power flow (OPF) problem is one of the most popular and important issues that researchers need to solve in power systems. In this study, a modified weighted teaching-learning-based optimization (WTLBO) algorithm was designed and implemented for the OPF problem, regarding the teaching-learning-based optimization (TLBO) algorithm, which is one of the meta-heuristic algorithms. To demonstrate the effectiveness of TLBO and developed WTLBO algorithms in OPF solutions, six different single/multi-objective functions consisting of targets, such as fuel cost, total active power losses, and voltage deviation were tested on standard IEEE 30 and 57 bus systems. Multi-objective functions were transformed into a single objective function using the weighted sum method. Analysis results were compared with different optimization algorithms used in the literature. When the developed WTLBO algorithm is compared according to the single/multi-objective functions in OPF solutions, it has been proven that it performs better than the original TLBO algorithm and other algorithms in the literature. As a result of the analysis, according to the TLBO algorithm, in the IEEE 30 bus power system, in the proposed WTLBO algorithm, a decrease of 0.05% in the total fuel cost, 2.20% in active power losses, and 13.37% in voltage deviation are observed.

A high bypass turbofan aero-engine delivers compressed air from the low-pressure to the high-pressure compressor through a compressor transition duct. Weight and design space limitations impel to its S-shaped design. Despite that, the compressor transition duct has to guide the flow very carefully to the high-pressure compressor without disturbances and flow separations. Hence, the present paper is devoted to elaborate on the application of the proposed heuristic optimization technique known as multi-objective teaching–learning-based optimization (MO-TLBO) algorithm in the area of S-shaped transition compressor duct. The present algorithm is applied for obtaining the optimum design of the S-shaped duct while minimizing the total pressure loss coefficient and non-uniformity index at the S-shaped duct outlet. These objectives are also optimized independently and as a multi-objective consideration as well. Because of the conflicting nature of the objective functions, a Pareto-optimal curve is also presented for a successful trade-off between the objectives. Pareto-optimal curve gives flexibility to the designer for selecting the best set of the parameters based on the objectives. Successful execution of the proposed MO-TLBO algorithm is examined by comparing the optimum results acquired by the multi-objective genetic algorithm and comparisons divulge that the TLBO algorithm can be successfully practiced to obtain the optimized design of compressor transition S-shaped duct. Optimized duct shows the reduction in total pressure loss and non-uniformity by 28.80% and 36.67%, respectively, despite the reduction of 14.74% in overall length.