Learner Phase Research Articles

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.

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

This paper proposes a novel hybrid teaching–learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teaching–learning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.

Improved teaching–learning-based optimization algorithm-based maximum power point trackers for photovoltaic system

This paper presents an improved teaching–learning-based optimization algorithm (ITLBO) for designing and implementing the maximum power point trackers (MPPT) incorporated with a photovoltaic (PV) module to extract the maximum power from the PV generating unit. The proposed algorithm is based on a population-based optimization algorithm to design a practical circuit of MPPT controller based on Arduino microcontroller. A modification in the teaching–learning-based optimization algorithm is considered where the best learner is considered as a teacher in the learner phase. The ITLBO-MPPT is physically implemented with Arduino Uno microcontroller which is presented as an open source. An experimental circuit comprising a PV module, a converter controlled via Arduino which is programmed by the proposed algorithm, ITLBO, is implemented, and the results are tabulated via three days of different operating conditions. A significant improvement in the global maximum power point is obtained via the proposed approach compared with that obtained via PSO and TLBO.

Modified Teaching-Learning-Based Optimization for Combined Heat and Power Economic Dispatch

AbstractThis paper develops and suggests modified teaching-learning-based optimization to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. The valve-point loading and forbidden working regions of conventional thermal generator are taken into account. Modified teaching–learning-based optimization (MTLBO) introduces Gaussian random variables in the ‘Teacher phase’ and ‘Learner phase’ which perks up search efficiency and assurances a high probability of acquiring the global optimum without considerably impairing the convergence speed and the simplicity of the structure of teaching-learning-based optimization (TLBO). The efficacy of the suggested MTLBO technique has been confirmed on three test systems and 15 benchmark functions. Test results of the suggested technique have been matched up to those achieved by other evolutionary techniques. It has been observed from the comparison that the suggested MTLBO technique has the ability to endow with superior solution.

Performance enhancement of magnetic levitation system using teaching learning based optimization

This paper demonstrates the potency of evolution based optimization techniques in the sense of enhancing the system’s performance. Teaching Learning Based Optimization (TLBO) is a well-known evolutionary algorithm used to optimize the parameters of the PID controller so as to improve the performance of the magnetic levitation system. The TLBO search algorithm is split into two phases, the teacher phase and the learner phase. The teacher phase is comprised of having minimum performance index as compared to learner phase. The learners improve their knowledge on the basis of teacher’s performance. The parameters are tuned while minimizing the performance index of the system. The performance index incorporated in this paper is the integral time weighted square error (ITSE). The corroboration of the above technique is ended by comparing it with the conventional control techniques.

Teaching–learning-based optimization with differential and repulsion learning for global optimization and nonlinear modeling

Teaching–learning-based optimization (TLBO) algorithm is one of the recently proposed optimization algorithms. It has been successfully used for solving optimization problems in continuous spaces. To improve the optimization performance of the TLBO algorithm, a modified TLBO algorithm with differential and repulsion learning (DRLTLBO) is presented in this paper. In the proposed algorithm, the differential evolution (DE) operators are introduced into the teacher phase of DRLTLBO to increase the diversity of the new population. In the learner phase of DRLLBO, local learning method or repulsion learning method are adopted according to a certain probability to make learners search knowledge from different directions. In the local learning method, learners learn knowledge not only from the best learner but also from another random learner of their neighbors. In the repulsion learning method, learners learn knowledge from the best learner and keep away from the worst learner of their neighbors. Moreover, self-learning method is adopted to improve the exploitation ability of learners when they are not changed in some continuous generations. To decrease the blindness of random self-learning method, the history information of the corresponding learners in some continuous generations is used in self-learning phase. Furthermore, all learners are regrouped after a certain iterations to improve the local diversity of the learners. In the end, DRLTLBO is tested on 32 benchmark functions with different characteristics and two typical nonlinear modeling problems, and the comparison results show that the proposed DRLTLBO algorithm has shown interesting outcomes in some aspects.

Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.

An improved teaching-learning-based optimization with differential evolution algorithm for optimal power flow considering HVDC system

In this paper, a modified teaching–learning based optimization (TLBO) algorithm is introduced in order to solve the optimal power flow (OPF) considering the high voltage direct current (HVDC) link in power systems. In TLBO, there is an improper diversity among search learners; therefore, its convergence speed is lower in comparison with some other evolutionary algorithms. Hence, in order to improve the quality of the solutions and speed up the velocity convergence, the teacher phase is modified. Moreover, to balance the global and local search capability of TLBO, one of the most common mutation operations of the differential evolution algorithm is incorporated into learner phases. With these modifications, the trapping to the local minima of traditional TLBO is vastly improved and the trade-off between the global searching ability and the convergence rate is retained. In order to demonstrate the efficiency of the proposed optimization method, it is applied to the OPF problem of two different two-terminal HVDC systems, including the modified 5-bus system and the modified WSCC 9-bus system. The behavior of some optimization methods such as the Genetic Algorithm, Backtracking Search Algorithm, and Artificial Bee Colony algorithm as well as the CPU running time for the objective function is presented. Comparison results indicate that the proposed optimization method is reliable with higher quality solutions among other applied evolutionary algorithms.

Economic load dispatch problem: quasi-oppositional self-learning TLBO algorithm

This paper proposes a meta-heuristic algorithm named as quasi-oppositional self-learning teacher-learner-based-optimization (QOSLTLBO) for solving non-convex economic load dispatch (ELD) problem. The ELD problem is an essential concern of power system and its main objective is to allocate optimal power generation to each generating unit so as to minimize the total cost of generation while satisfying all constraints available in the system. The problem considered in this paper is a non-convex quadratic generation cost of the units (with or without valve-point loading effects) with power balance and generation limits as the system constraints. This model of generation cost is a continuous model of the ELD problem. The proposed algorithm includes a quasi-oppositional approach for better initialization of population. A self-learning phase is added after teacher phase and learner phase of basic teacher-learner-based-optimization (TLBO) algorithm to improve the convergence rate. To prove the efficacy and robustness of proposed algorithm, it is applied to solve ELD problem on different standard IEEE generator systems and the results, thus obtained are compared with other state-of-art algorithms. The minimum total cost of generation in all the cases are obtained from the proposed algorithm which proves its effectiveness over others. The additional advantage of the proposed QOSLTLBO algorithm is that it is kept free from algorithm-specific parameters like basic TLBO.

A Fast Converging and Consistent Teaching-Learning-Self-Study Algorithm for Optimization: A Case Study of Tuning of LSSVM Parameters for the Prediction of NOx Emissions from a Tangentially Fired Pulverized Coal Boiler

This paper presents a novel Teaching-Learning-Self-Study-Optimization (TLSO) algorithm which is not only fast converging according to the number of iterations, but also relatively consistent in converging with high accuracy to the global minimum in comparison with some other algorithms. The original Teaching-Learning-Based Optimization (TLBO) gives uniformly distributed and randomly selected weight to the amount of knowledge to a learner at each phase, i.e., teacher phase and learner phase. This uniformly distributed and randomly selected weight causes the algorithm to converge the average cost of learners in a moderate number of iterations. Li and his coworkers intensified the teacher and learner phases by introducing weight-parameters in order to improve the convergence speed in terms of iterations in 2013 and called it Ameliorated Teaching-Learning-Based Optimization (ATLBO). The criterion of a good evolutionary optimization algorithm is to be consistent in converging the cost of the objective function. For this, it should include intensification for local search as well as diversification for global search in order to reduce the chances of trapping in a local minimum. Some students naturally tend to study by themselves by the means of a library and internet academic resources in order to enhance their knowledge. This phenomenon is termed as self-study and is introduced in the proposed TLSO’s learner phase as a diversification factor (DF). Various other evolutionary algorithms such as ACO, PSO, TLBO, ATLBO and two variants of TLSO are also developed and compared with TLSO in terms of consistency to converge to the global minimum. Results reveal that the TLSO was found to be consistent not only for a higher number of functions among 20 benchmark functions, but also for NOx prediction application. Results also show that the predicted NOx emissions through LSSVM tuned with TLSO are comparable with the other algorithms considered in this work.

Observer-Teacher-Learner-Based Optimization: An enhanced meta-heuristic for structural sizing design

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

Obstacles to engaging in young driver licensing: Perspectives of parents

IntroductionYoung novice drivers remain at greater risk of injury and death despite a wealth of interventions including graduated driver licensing (GDL) programs. The key to implementing safer practices inherent in GDL appears to lie with optimising the role of parents. This qualitative research explored the parent’s perspectives of obstacles to engaging in the driver licensing process within a GDL program. Parents also shared advice on what they found helpful, and where relevant, recommended changes in the process to enable safer practices for young drivers. MethodTwenty-three parents (aged 35–60 years, M=49.52, SD=8.01, 11 males) participated in semi-structured interviews regarding licensing experiences with their young driver children. The young drivers included learner (n=11), provisional (restricted/intermediate) (n=9) and open (unrestricted/full) licence drivers (n=3), ranging from 16 to 24 years (M=18.04, SD=2.21, 13 males). Findings and conclusionsContent analysis revealed that most obstacles were encountered at the learner licensing phase, with the parent-reported difficult temperament of the learner driver the most prominent. Unsurprisingly, advice to other parents to be patient and remain calm featured heavily during the same phase. Anxiety from not having control of the vehicle was another obstacle at the learner phase, translating to anxieties for child safety in the early stages of provisional driving. Recommendations for the current GDL included more rigorous road rule testing, with general support for the program, professional driver training at learner and provisional stages facilitated parental engagement through the licensing phases. Practical applicationsThe findings overwhelmingly suggest a need for parents to be educated regarding their importance in, and of, the driver licensing process, and the efficacy of their instruction, content and practices.

A geometric image segmentation method based on a bi-convex, fuzzy, variational principle with teaching-learning optimization

This paper proposes an efficient, bi-convex, fuzzy, variational (BFV) method with teaching and learning based optimization (TLBO) for geometric image segmentation. Firstly, we adopt a bi-convex, object function to process a geometric image. Then, we introduce TLBO to maximally optimize the length-p enalty item, which will be changed under the teaching phase and the learner phase of the TLBO. This makes the length penalty item closer to the target boundary. Therefore, the length-penalty item can be automatically adjusted according to the fitness function, namely the evaluation standards of the image quality. At last, we combine the length-penalty item with the numerical remedy mechanism to achieve better results. Compared with existing methods, simulations show that our method is more effective.

Constrained optimization based on improved teaching–learning-based optimization algorithm

This paper proposes an improved constrained teaching–learning-based optimization (ICTLBO) method to efficiently solve constrained optimization problems (COPs). In the teacher phase of ICTLBO, the population is partitioned into several subpopulations, and the direction information between the mean position of each subpopulation and the best position of population guide the corresponding subpopulation to the promising region promptly. Information exchange between different subpopulations is used to discourage premature convergence of each subpopulation. Furthermore, in the learner phase, a new learning strategy is introduced to improve the population diversity and enhance the global search ability. Three different constraint handling methods are adopted for three situations, which are infeasible, semi-feasible, and feasible situations, during the evolution process. To evaluate the performance of ICTLBO, 22 benchmark functions presented in CEC2006 and 18 benchmark functions introduced in CEC2010 are chosen as the test suite. Moreover, four widely used engineering design problems are selected to test the performance of ICTLBO for real-world problems. Experimental results indicate that ICTLBO can obtain a highly competitive performance compared with other state-of-the-art algorithms.

Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm

Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III.

An Image Segmentation by BFV and TLBO

This paper presents the establishing of a biconvex fuzzy variational (BFV) method with teaching learning based optimization (TLBO) for geometric image segmentation (GIS). Firstly, a biconvex object function is adopted to process GIS. Then, TLBO is introduced to maximally optimize the length penalty item (LPI), which will be changed under teaching and learner phase of TLBO, making the LPI closer to the target boundary. Afterward, the LPI can be adjusted based on fitness function, namely, the evaluation standards of image quality. Finally, the LP is combined item with the numerical order to get better results. Different GIS strategies are compared with various fitness functions in terms of accuracy. Simulations show that the presented method is more effective in this area.

Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

A novel chaotic teaching–learning-based optimization algorithm for multi-machine power system stabilizers design problem

This paper proposes an efficient optimization algorithm named chaotic teaching–learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: ‘teacher phase’ and ‘learner phase’. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named “chaotic phase” is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices.

Teaching–learning-based optimization with learning experience of other learners and its application

To improve the global performance of the standard teaching–learning-based optimization (TLBO) algorithm, an improved TLBO algorithm (LETLBO) with learning experience of other learners is proposed in the paper. In LETLBO, two random possibilities are used to determine the learning methods of learners in different phases. In the Teacher Phase, the learners improve their grades by utilizing the mean information of the class and the learning experience of other learners according to a random probability. In Learner Phase, the learner learns knowledge from another learner which is randomly selected from the whole class or the mutual learning experience of two randomly selected learners according to a random probability. Moreover, area copying operator which is used in Producer–Scrounger model is used for parts of learners to increase its learning speed. The feasibility and effectiveness of the proposed algorithm are tested on 18 benchmark functions and two practical optimization problems. The merits of the improved method are compared with those of some other evolutionary algorithms (EAs), the results show that the proposed algorithm is an effective method for global optimization problems.

Effectiveness of Parent-Focused Interventions to Increase Teen Driver Safety: A Critical Review

PurposeWe critically reviewed recent parent-directed teen driving interventions to summarize their success in meeting stated goals; identify promising intervention components and knowledge gaps; aid in the selection, adaptation, and dissemination of effective interventions; and guide future research efforts. MethodsWe focused on interventions that included a direct parent component, explicitly stated outcomes related to the teen and/or their parents, were evaluated for parent or teen outcomes, targeted drivers younger than the age of 21 years, and had at least one evaluation study published since 1990 and in English. We conducted a comprehensive systematic search of 26 online databases between November 2013 and January 2014 and identified 34 articles representing 18 interventions. ResultsSeveral interventions—in particular, those that had an active engagement component, incorporated an in-vehicle data recorder system, and had a strong conceptual approach—show promise in improving parental supervisory behaviors during the learner and early independent phases, increasing teen driver skill acquisition, and reducing teens' risky driving behaviors. ConclusionsWe identify essential characteristics of effective parent-involved teen driving interventions and their evaluation studies, propose a comprehensive and multitiered approach to intervention, and discuss several research areas and overarching issues for consideration.