Modified teaching-learning-based optimization algorithm for multi-objective optimization problems

When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils.

Teaching Learning Based Optimization (TLBO) for energy efficiency in Fog Computing

Fog Computing is eminent to ensure quality of service in handling huge volume and variety of data and to display output, or for closed loop process control. It comprises of fog devices to manage huge data transmission but results in high energy consumption, end-to end-delay, latency. In this paper, an energy model for fog computing environment has been proposed and implemented based on teacher student learning model called Teaching Learning Based Optimization (TLBO) to improve the responsiveness of the fog network in terms of energy optimization. The results show the effectiveness of TLBO in choosing the shortest path with least energy consumption.

Aircraft Control Parameter Estimation Using Self-Adaptive Teaching-Learning-Based Optimization with an Acceptance Probability

This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.

Optimization of cylindrical wall domes via metaheuristic algorithms

Optimization is a widely used phenomenon in various problems and fields. Because time and resources are very limited in today's world, it can be said that the usage area of the optimization process will be expanded and spread in all areas of life. Although different methods are used in the realization of the optimization process, the performance of metaheuristic algorithms in solving problems has led to an increase in research on these methods. As in other fields, the application examples of these algorithms are diversifying and increasing in the field of structural engineering. In this study, the performance comparison of five different algorithms for the optimum design of an axisymmetric cylindrical wall with a dome is investigated. These algorithms are Jaya (JA), Flower pollination (FPA), teaching-learning-based optimization (TLBO) algorithms and two hybrid versions of these algorithms. ACI 318 regulation was used in reinforced concrete design with a flexibility method-based approach in the analyses. In the analyzes with five different situations of the wall height, some statistical values , and data of analysis numbers were obtained by running the algorithms a large number of times. According to the analysis results, Jaya algorithm is slightly better in terms of the speed of reaching the optimum result, but also all algorithms are quite effective and reliable in solving the problem.

Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction

The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

Demand Side Management for Commercial Area Using Teaching Learning Based Optimization

In Smart Grid Demand side management (DSM) plays a crucial role which permits customers to form educated selections concerning their energy consumption. It allows the strength to companies lessen the height load call for and reshape the burden profile. Most of the present demand aspect management ways utilized in ancient energy management system is with specific techniques and algorithms. In addition, the present ways handle solely a restricted range of governable a lot of restricted varieties of loads. This paper covers a requirement aspect management strategy supported load shifting technique for demand aspect management of future sensible grids with an outsized range of devices of many sorts. The day-in advance load shifting technique is proposed and mathematically formulated as a minimization problem. Teaching Learning Based Optimization (TLBO) is an efficient optimization is proposed. Considering Smart Grid with commercial customer, Simulations has been carried out. The respective results emphasis that the considered demand side management strategy attains substantial savings, whereas suppresses the mark of load demand of the smart grid. The outcome is by improve in sustainability of the smart grid, in addition to reduced standard operational value and carbon emission levels. The proposed algorithms can be easily applied to various optimization problems.

Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.

Study of optimum welding performance in friction stir welding of dissimilar Mg alloys using integrated RSM-TLBO algorithm

Lightweight with excellent strength of magnesium alloys has attracted its use in transportation industries but difficulty in fusion welding of magnesium alloys restricts its application. The present research investigates solid state friction stir welding of dissimilar AZ31-AZ91 magnesium alloys with aim to achieve optimum quality welds. Surface roughness, microstructure and mechanical properties of these joints have been investigated at different tool rotational speed, welding speed and tool shoulder diameter. Maximum joint strength obtained is 89.71% (as compare to AZ31) which is more than the previously reported joint strengths of dissimilar magnesium alloys. Further, mathematical relations for responses have been developed and utilised for multi-objective optimization using teaching-learning-based optimization algorithm. Eventually, teaching-learning-based optimization algorithm results suggest that the optimum value of surface roughness (3.3925 µm), grain size (12.6869 µm), tensile strength (237.9621 MPa), microhardness (69.3652 Hv) and flexural strength (333.2285 MPa) can be achieved at 921 rpm rotational speed, 30 mm/min welding speed and 15 mm shoulder diameter with overall improvement in multiple responses.