teaching–learning based optimization

This paper proposes a robust state feedback controller for Electric Vehicle aggregators to solve the challenging problem caused by the participation of Electric Vehicles in the load frequency control of the power system.  The Lyapunov-Krasovskii functional method is used to achieve two objectives of the robust performance and stability.  Then, by using teaching learning based optimization algorithm, both primary and secondary participation gains of EV aggregators in LFC are optimally determined. The Generation Rate Constraint and time delay, as nonlinear elements, are also taken into account.  Simulations are carried out on two nonlinear power systems by using the power system simulation software.  The results show that the designed controller gives a desirable robust performance for frequency regulation at the presence of uncertainties.

Today, due to the complexity of engineering problems and at the same time the advancement of computer science, the use of machine learning (ML) methods and soft computing methods in solving engineering problems has been considered by many researchers. These methods can be used to find accurate estimates for problems in various scientific fields. This paper investigates the effectiveness of the Adaptive Network-Based Fuzzy Inference System (ANFIS) hybridized with Teaching Learning Based Optimization Algorithm (TLBO), to predict the ultimate strength of columns with square and rectangular cross-sections, confide with various fiber-reinforced polymer (FRP) sheets. In previous studies by many researchers, several experiments have been conducted on concrete columns confined by FRP sheets. The results indicate that FRP sheets effectively increase the compressive strength of concrete columns. Comparing the results of ANFIS-TLBO with the experimental findings, which were agreeably consistent, demonstrated the ability of ANFIS-TLBO to estimate the compressive strength of concrete confined by FRP. Also, the comparison of RMSE, SD, and R2 for ANFIS-TLBO and the studies of different researchers show that the ANFIS-TLBO approach has a good performance in estimating compressive strength. For example, the value of R2 in the proposed method was 0.92, while this parameter was 0.87 at best among the previous studies. Also, the obtained error in the prediction of the proposed model is much lower than the obtained error in the previous studies. Hence, the proposed model is more efficient and works better than other techniques.

As a novel strategy, Quantum-behaved particles use uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates’ positions. In this case, the local attractors as potential solutions between the best solution and the others are introduced to explore the solution space. Also,  the difference between the average and another solution is established as a new step size. In the present paper, the quantum teacher phase is introduced to improve the performance of the current version of the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO) by using the formulation obtained from solving the time-independent Schrodinger equation predicting the probable positions of optimal solutions. The results show that QTLBO, an acronym for the Quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two circulant space trusses with multiple frequency constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.

To achieve high-quality software, different tasks such as testing should be performed. Testing is known as a complex and time-consuming task. Efficient test suite generation (TSG) methods are required to suggest the best data for test designers to obtain better coverage in terms of testing criteria. In recent years, researchers to generate test data in time-efficient ways have presented different types of methods. Evolutionary and swarm-based methods are among them. This work is aimed to study the applicability of swarm-based methods for efficient test data generation in EvoSuite. The Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) are used here. These methods are added to the EvoSuite. The methods are adapted to work in a discrete search space of test data generation problem. Also, a movement pattern is presented for generating new solutions. The performances of the presented methods are compared over 103 java classes with two built-in genetic-based methods in EvoSuite. The results show that swarm-based methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.

The optimal design of dome structures is a challenging task and therefore the computational performance of the currently available techniques needs improvement. This paper presents a combined algorithm, that is supported by the mixture of Charged System Search (CSS) and Teaching-Learning-based optimization (TLBO). Since the CSS algorithm features a strong exploration and may explore all unknown locations within the search space, it is an appropriate complement to enhance the optimization process by solving the weaknesses with using another optimization algorithm’s strong points. To enhance the exploitation ability of this algorithm, by adding two parts of Teachers phase and Student phase of TLBO algorithm to CSS, a method is obtained that is more efficient and faster than standard versions of these algorithms. In this paper, standard optimization methods and new hybrid method are tested on three kinds of dome structures, and the results show that the new algorithm is more efficient in comparison to their standard versions.

The last decade has seen an important role of container terminals in the global trade centers. By another point of view, the high cost of quay cranes on the other hand is a motivation for a set of real-world problems including of Quay Crane Assignment Problem (QCAP) and the Quay Crane Scheduling Problem (QCSP) in the hotspot of research. The main innovation of this proposal is to integrate both QCAP and QCSP to improve Quay Crane (QC) performance by an optimization goal, i.e., QCASP. A real case study in Iran has been applied to validate the proposed problem which has been formulated by a mixed integer linear programming (MILP). Due to inherent complexity of problem proposed in the real-world cases, the Teaching-Learning-Based-Optimization (TLBO) algorithm has been used to find an optimal/global solution in a reasonable time. The applied TLBO has been tuned by Taguchi method and validated in small instances in comparison with an exact method. The computational results show that our proposed TLBO algorithm can solve QCASP, especially in large size instances, successfully. Finally, a set of managerial implications has been recommended to consider the benefits of proposed methodology and algorithm regarding the real case study presented

In this study, to enhance the optimization process, especially in the structural engineering field two well-known algorithms are merged together in order to achieve an improved hybrid algorithm. These two algorithms are Teaching-Learning Based Optimization (TLBO) and Harmony Search (HS) which have been used by most researchers in varied fields of science. The hybridized algorithm is called A Discrete Hybrid Teaching-Learning Based Optimization (DHTLBO) that is applied to optimization of truss structures with discrete variables. This new method is consisted of two parts: in the first part the TLBO algorithm applied as conventional TLBO for local optimization, in the second stage the HS algorithm is applied to global optimization and exploring all the unknown places in the search space. The new hybrid algorithm is employed to minimize the total weight of structures. Therefore, the objective function consists of member’s weight, which is depends on the form of stress and deflection limits. To demonstrate the efficiency and robustness of this new algorithm several truss structures which are optimized by most researchers are presented and then their results are compared to other meta-heuristic algorithm and TLBO and HS standard algorithms.

This study deals with inverse approach for damage detection in a double-beam system. A double-beam system made of two parallel beams connected through an elastic layer. Degradation in stiffness of beams element, crack occurrence and partly destruction of inner layer has been considered as different types of damage. The time domain acceleration response of the system measured and proper orthogonal decomposition has been applied to the collected data in order to derive the proper orthogonal values (POV) and proper orthogonal modes (POM) of the system. Effect of single damage in different locations on the POV has been analyzed and an objective function has been defined using the dominant POV and POM of each beam separately. In order to increase robustness of the method against noise, the objective function enriched by adding statistical property of time domain response. The teaching-learning based optimization algorithm has been employed to solve optimization problem. Efficiency of the proposed method for detecting single and multiple damages in the system demonstrated with and without noise. Simulation results show good accuracy of the proposed method for detection single and multiple damages of different types in the system.

The Teaching-Learning-Based Optimization (TLBO) algorithm is a new meta-heuristic algorithm which recently received more attention in various fields of science. The TLBO algorithm divided into two phases: Teacher phase and student phase; In the first phase a teacher tries to teach the student to improve the class level, then in the second phase, students increase their level by interacting among themselves. But, due to the lack of additional parameter to calculate the distance between the teacher and the mean of students, it is easily trapped at the local optimum and make it unable to reach the best global for some difficult problems. Since the Harmony Search (HS) algorithm has a strong exploration and it can explore all unknown places in the search space, it is an appropriate complement to improve the optimization process. Thus, based on these algorithms, they are merged to improve TLBO disadvantages for solving the structural problems. The objective function of the problems is the total weight of whole members which depends on the strength and displacement limits. Indeed, to avoid violating the limits, the penalty function applied in the form of stress and displacement limits. To show the superiority of the new hybrid algorithm to previous well-known methods, several benchmark truss structures are presented. The results of the hybrid algorithm indicate that the new algorithm has shown good performance.

Computer-aided process planning (CAPP) is an essential component in linking computer-aided design (CAD) and computer-aided manufacturing (CAM). Operation sequencing in CAPP is an essential activity. Each sequence of production operations which is produced in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number  of feasible sequences increase exponentially, consequently the best sequence is to be chosen. This paper aims at  presenting the application of a newly developed meta-heuristic called the hybrid teaching–learning-based optimization (HTLBO) as a global search technique for the quick identification of the optimal sequence of operations with consideration of various feasibility constraints. To do so, three case studies have been conducted to evaluate the performance of the proposed algorithm and a comparison between the proposed algorithm and the previous searches from the literature has been made. The results show that HTLBO performs well in operation sequencing problem.

In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. Teaching–learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem’s instances and Brandimarte's data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP.

Imperialist Competitive Algorithm, ICA is a meta-heuristic which simulates collapse of weak empires by more powerful ones that take possession of their colonies. In order to enhance performance, ICA is hybridized with proper features of Teaching-Learning-Based Optimization, TLBO. In addition, ICA walks are modified with an extra term to intensify looking for the global best solution. The number of control parameters and consequent tuning effort has been reduced in the proposed Imperialist Competitive Learner-Based Optimization, ICLBO with respect to ICA and several other methods. Efficiency and effectiveness of ICLBO is further evaluated treating a number of test functions in addition to continuous and discrete engineering problems. It is discussed and traced that balancing between exploration and exploitation is enhanced due to the proposed hybridization. Numerical results exhibit superior performance of ICLBO vs. ICA and a variety of other well-known meta-heuristics.

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching–learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

Power systems are subjected to small–signal oscillations that can be caused by sudden change in the value of large loads. To avoid the dangers of these oscillations, the Power System Stabilizers (PSSs) are used. When the PSSs can not be effective enough, installation of the Thyristor–based compensators to increase the oscillations damping is a suitable method. In this paper, a Static Synchronous Series Compensator (SSSC) is used in Single–Machine Infinite–Bus (SMIB). To control the signal of the output voltage of SSSC, a robust controller is used. Also, we proposed a hybrid control method to adjust the PSS voltage using Teaching–Learning Based Optimization (TLBO) algorithm and Fuzzy Inference System (FIS). Objective functions of designing parameters are based on Integral of Time multiplied by Absolute value of the Error (ITAE). The time–variations of angular speed deviations are investigated in different modes, including: with SSSC/PSS, without SSSC/PSS, different input mechanical power, and different system parameters.

A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.