TLBO Algorithm Research Articles

Hybrid Optimization Algorithm to Solve the Nonconvex Multiarea Economic Dispatch Problem

In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and nonconvex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA–TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA–TLBO based approach to provide a better solution.

Determination of Lemaitre damage parameters for DP590 steel using Teacher-Learner based optimization

Dual phase (DP) steel is widely used in high strength applications. The components or parts some-times damage under the applied load during the production stage or the service stage. The damage behaviour of material under such loading conditions need to be analysed. The finite element analysis of the component and/or material incorporating the damage model is a useful step. The determination of parameters of the damage model envisages the application of such models into the numerical analysis. The present work proposes a model to determine the parameters of the Lemaitre damage model using the “Teacher-Learner based optimization” (TLBO) algorithm and the finite element method. The model simulates the tensile test and predicts the flow curve till fracture to determine the optimum set of parameters for the Lemaitre damage model. The proposed model incorporating uncoupled TLBO algorithm and finite element method is a general model and can be effectively employed to determine the values of the unknown parameters for the model.

Dimensional accuracy improvement by parametric optimization in pulsed Nd:YAG laser cutting of Kevlar-29/basalt fiber-reinforced hybrid composites

Laser cutting proves its suitability for cutting of single fiber-reinforced polymer (FRP) composites compared with conventional cutting techniques due to reduced fiber delamination, matrix cracking and fiber pullout. However, the performance of the laser cutting process of hybrid FRP composites is yet to be revealed, which paves the way of the present study. In this article, an experimental investigation of the laser cutting process is carried out on 1.35-mm-thick Kevlar-29 and basalt fiber-reinforced polymer (KBFRP) hybrid composite laminates using 250 W pulsed Nd:YAG laser system. The performance of laser cutting was evaluated by quantifying different kerf quality characteristics such as top and bottom kerf width, top and bottom kerf deviation and kerf taper. These kerf quality characteristics define the geometrical accuracy of the laser cut. Response surface methodology-based Box–Behnken design was adopted for conducting the experiments with varied settings of laser cutting parameters, viz. lamp current, pulse width, pulse frequency, compressed air pressure and cutting speed. Second-order regression models of each response were developed and validated by using standard error plots. A parametric effect analysis was carried out by using the variation of performance measures predicted through developed mathematical models. In order to achieve the optimal levels of the process parameters for all kerf quality characteristics, a self-developed python language-coded TLBO algorithm was used. Finally, the confirmation experiments were performed at obtained optimal levels of laser cutting parameters. An overall improvement of 22.23% in multiple kerf quality characteristics was achieved through optimal settings of laser cutting parameters. The individual improvement of 11.44%, 8.47%, 17.65%, 15.22% and 58.87% was recorded in top kerf width, bottom kerf width, top kerf deviation, bottom kerf deviation and kerf taper, respectively. The developed mathematical models and suggested optimal conditions are able to provide direction to the researchers for obtaining higher-dimensional accuracy in Nd:YAG laser cutting of KBFRP hybrid composites.

Network design for resilience in supply chains using novel crazy elitist TLBO

A resilient plant location model is proposed in this research and has been evaluated for a case problem. The model considers three major indicators of network resilience, viz. node density, node complexity and node criticality. A resilient design could ensure for cost efficiency, apart from that the likelihood of potential disruptions due to bottlenecks could be minimized. The results were optimized using a novel crazy elitist TLBO algorithm. The algorithm has been presented to solve the case problem and has been pretested for a constrained and unconstrained test function. A multi-objective decision-making model has been constructed with the flow of products as variables and was effectively solved using the meta-heuristic. The solution to the case brings insights into the design of supply network for resilience, and the managers are recommended to incorporate the concepts of resilience from the design phase itself.

Application of Efficient SFHA and TLBO Algorithms for Cell Formation Problems in Cellular Manufacturing Environment

Application of Efficient SFHA and TLBO Algorithms for Cell Formation Problems in Cellular Manufacturing Environment

Comparable investigation on TLBO algorithm for power system optimization

Comparable investigation on TLBO algorithm for power system optimization

Arrival Aircraft Optimal Sequencing based on Teaching-Learning-based Optimization Algorithm with Immunity

Sequencing and scheduling of arrival aircrafts is of great significance for ensuring flight safety and reducing flight costs. A multi-constrained approach aircraft optimization ranking model with minimum delay time as an objective function was established. Based on the “Teaching-Learning Based Optimization” (TLBO), the algorithm was discretized, combined with the “antibody injection” and “immune modification” of the Immune Algorithm (IA). The improved algorithm was used to simulate the aircraft sequencing optimization model and compared with the traditional FCFS method. The simulation results show that compared with the FCFS method, the improved TLBO algorithm can significantly reduce the total aircraft delay, effectively mitigate the delay of aircraft arrival, and can be used to solve the problem of aircraft optimization.

Solving Complex Cardinality Constrained Mean-Variance Portfolio Optimization Problems Using Hybrid HS and TLBO Algorithm

Solving Complex Cardinality Constrained Mean-Variance Portfolio Optimization Problems Using Hybrid HS and TLBO Algorithm

A Non-Dominated Sorting TLBO Algorithm for Multi-Objective Short-Term Hydrothermal Self Scheduling of GENCOs in a Competitive Electricity Market

A Non-Dominated Sorting TLBO Algorithm for Multi-Objective Short-Term Hydrothermal Self Scheduling of GENCOs in a Competitive Electricity Market

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

DECISION-MAKING PROCESS TECHNIQUES USED IN THE OPTIMIZATION OF CONSTRUCTION PROJECTS

It is desirable that the construction projects can be completed in the most appropriate manner at the desired time, cost or other purposes. If any of these requests are solely aimed, the appropriate project parameters can be determined and the results such as the optimal time or cost can be determined after an optimization process. If more than one goal is to be achieved at the same time, the decision making process becomes more difficult. In this case, rather than a single final result, multiple results can be obtained. A number of techniques are used to optimize both time and cost. In this study, the techniques proposed for this purpose in the literature have been examined and some of these techniques have been applied to the construction projects considered as time-cost problem. Also, the TLBO algorithm, popular in recent years, has been the preferred in the solution of the multi-objective optimization problem. Construction project business activities which are taken into account as a time-cost problem in the literature are examined as numerical examples. A computer program is developed to realize the time-cost trade-off problem by using MATLAB.

Optimizing chaos based image encryption

Due to its inherent properties such as sensitivity to initial condition, ergodicity and systematic parametrization chaos systems have been extensively used in image encryption. However, one needs to optimize the chaos for each image being encrypted to provide high quality encryption. Features such as high entropy or low pixel correlation are measures of interest in image encryption. In this paper, we propose a novel technique to optimize a chaos based image encryption algorithm. The image is confused using logistic map as chaos function and diffused by modified Knuth shuffling algorithm. We use the logistic map parameters as encryption key. We then harness TLBO and GSA evolutionary algorithms to speed up the optimization process of these parameters. The optimization method yields parameters that lead to lowest correlation among adjacent pixels or highest entropy. We compare the quality and security of our cryptosystem with some famous image encryption methods in the literature.

Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking

A novel optimization algorithm called hybrid sine–cosine algorithm with teaching–learning-based optimization algorithm (SCA–TLBO) is proposed in this paper, for solving optimization problems and visual tracking. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence than the standard SCA and TLBO. The effectiveness of this algorithm is evaluated using 23 benchmark functions. Statistical parameters are employed to observe the efficiency of the hybrid SCA–TLBO qualitatively, and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The hybrid SCA–TLBO algorithm is applied for visual tracking as a real thought-provoking case study. The hybrid SCA–TLBO-based tracking framework is used to experimentally measure object tracking error, absolute error, tracking detection rate, root mean square error and time cost as parameters. To reveal the capability of the proposed algorithm, a comparison of hybrid SCA–TLBO-based tracking framework and other trackers, viz. alpha–beta filter, linear Kalman filter and extended Kalman filter, particle filter, scale-invariant feature transform, particle swarm optimization and bat algorithm, is presented.

Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

PurposeThis study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.Design/methodology/approachThe optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.FindingsTo improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.Originality/valueThe proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.

Comparison of optimum tapered cascade and optimal square cascade for separation of xenon isotopes using enhanced TLBO algorithm

ABSTRACTThe present study is conducted to propose a new code for optimizing a multicomponent isotope separation cascade. An efficient code is developed using an enhanced TLBO algorithm with a novel mutation for calculating the optimal parameters. The results indicate that by implementing a new objective function derived from the combination of D function with total interstage flow rates, the optimum tapered cascade is more efficient than the optimal square one. Also, the best feed locations for the separation of the middle components in the square cascade are the middle stages.

Parametric Optimization of Surface Roughness of Deburring Process on Aluminum Alloy Using SCARA Manipulator

This paper presents an attempt to analyze deburring operation using a SCARA (Selective Compliance Articulated Robot Arm) manipulator. There are various deburring techniques available in the literature but this work aims at deburring of small sized components. In this paper parametric optimization of surface roughness using SCARA Manipulator using various deburring parameters has been performed with Genetic and TLBO optimization algorithms. A rectangular work piece of Aluminum is considered and the end effector used in this case is a grinding wheel of aluminum oxide. The machining parameters considered are Spindle speed, Feed rate and Depth of cut. An orthogonal array L9 was modeled using Taguchi Method to conduct the experiments. The surface roughness of the workpiece is measured using an instrument called Talysurf. Later the end effector of SCARA is used for deburring on the same workpiece to check the efficiency of SCARA for removal of burrs. Regression analysis is employed to analyze the effect of the deburring parameters on material. Optimization of parameters was done using Genetic and TLBO algorithms in MATLAB environment. An optimum deburring parameters for minimizing the surface roughness was obtained at the Spindle speed 5250 (rpm), feed rate 0.859 (mm/sec) and depth of cut 0.3 (mm) and Spindle speed was found to be the most significant factor followed by feed rate for Surface roughness.

Hybrid DE-TLBO Based Robotic Arm Controller

A New effective optimization Technique called ‘Hybrid DE-TLBO’ is proposed in this paper by clubbing of Differential Evolution10 and Teacher learning Based optimization algorithms9 for optimization of motion control of one degree of freedom of Robotic arm consisting of a DC Motor. This algorithm is used to control the PID gains namely Kp, Ki, Kd to get the optimized values of desired specifications that is to optimize the Rise time, settling time, steady state error, Maximum Peak Overshoot. This can be achieved through developing the model of DC Motor of Robotic Arm in Simulink and simulate with the algorithm and comparing with DE and TLBO algorithms.

A novel multicast traffic engineering technique in SDN using TLBO algorithm

In recent years, multicast communication is widely used by network providers to deliver multimedia contents. Quality of service (QoS) provisioning is one of the most important issues while transmitting multimedia contents using multicast. Traditional IP multicasting techniques suffer from reliability, scalability and have limitations to provide appropriate QoS for multimedia applications based on service level agreement (SLA). Nowadays, the advent of software defined networking (SDN), enables network providers to manage their networks dynamically and guarantee QoS parameters for customers based on SLA. SDN provides capabilities to monitor network resources and allows to dynamically configure desired multicasting policies. In this paper, we proposed a novel multicasting technique to guarantee QoS for multimedia applications over SDN. To deliver multimedia contents in an efficient manner, our proposed method models multicast routing as a delay constraint least cost (DCLC) problem. As DCLC problem is NP-Complete, we proposed an approximation algorithm using teaching–learning-based optimization to solve this problem. We evaluated our proposed method under different topologies. Experimental results confirmed that our proposed method outperforms IP multicast routing protocol, and it achieves a gain of about 25% for peak signal-to-noise ratio.

A modified teaching–learning-based optimization for optimal control of Volterra integral systems

This study aimed to utilize a novel modified approach based on teaching–learning-based optimization (MTLBO), to achieve an approximate solution of optimal control problem governed by nonlinear Volterra integro-differential systems. The scheme was based upon Chebyshev wavelet and its derivative operational matrix, which eventually led to a nonlinear programming problem (NLP). The resulted NLP was solved by the MTLBO. The novel algorithm used a heuristic mechanism to intensify learning on the best students in learner phase. The new strategy was applied to improve learners’ knowledge and to structure the MTLBO. The applicability and efficiency of the MTLBO were shown for three numerical examples. The proposed algorithm was compared with the traditional TLBO algorithm and the Legendre wavelets and collocation method in the literature. The experimental results showed that the proposed MTLBO not only obtained the high-quality solutions with respect to the absolute errors but also provided results with the high speed of convergence.

Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

Abstract The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.