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Teaching-learning-based optimization (TLBO) is one of the latest metaheuristic algorithms being used to solve global optimization problems over continuous search space. Researchers have proposed few variants of TLBO to improve the performance of the basic TLBO algorithm. This paper presents a new variant of TLBO called fuzzy adaptive teaching-learning-based optimization (FATLBO) for numerical global optimization. We propose three new modifications to the basic scheme of TLBO in order to improve its searching capability. These modifications consist, namely of a status monitor, fuzzy adaptive teaching-learning strategies, and a remedial operator. The performance of FATLBO is investigated on four experimental sets comprising complex benchmark functions in various dimensions and compared with well-known optimization methods. Based on the results, we conclude that FATLBO is able to deliver excellence and competitive performance for global optimization. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient Modified Distance Vector Hop algorithm using Teaching-Learning-Based Optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms. [ABSTRACT FROM AUTHOR]

Traditional multi-objective evolutionary algorithms (MOEAs) adopt selection and reproduction operators to find approximate solutions for multi-objective optimization problems (MOPs). The Pareto-dominance-based method is an important branch of MOEA research which exploits dominance relations information. To use dominance relations information more efficiently, this paper proposes an individualized instruction mechanism combined with the non-dominated sorting concept and the teaching-learning process of teaching-learning-based optimization (TLBO). This algorithm, with its individualized instruction mechanism (INM-TLBO), places greater emphasis on the guiding role of the non-dominated solution. INM-TLBO designates specific teachers or interactive objects to help learners improve in the individualized teaching-learning process and adopts an external archive to preserve the best solution found. In addition, the INM-TLBO needs only generic control parameters as input, such as population size, an epsilon value for the external archive, and a stop criterion (maximal generation or function evaluation). The performance of INM-TLBO was evaluated on three test problem sets, including twelve extensively used unconstrained test problems, six truly disconnected test problems, and ten complex continuous unconstrained optimization test problems originally proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The numerical results are compared with those of other state-of-the-art algorithms and show that INM-TLBO has good convergence and high robustness on these test problems. [ABSTRACT FROM AUTHOR]

Routing in Wireless Sensor Networks (WSN) differs from routing in classical networks, as in WSN links are unreliable. Indeed, there is no infrastructure and the sensors have limited energy resources and low processing capacity. Routing problems in many cases are considered NP-hard problems. In these cases, metaheuristics can be the best solution for this kind of problems. Thus, many metaheuristics are used for routing in WSNs. Ant colony optimization (ACO) is among the most used metaheuristics, as it showed an excellent performance for routing. Due to the adaptation difficulties related to the ACO parameters, in this paper we propose a new optimization approach for routing problem using the Teaching Learning Based Optimization (TLBO). TLBO is a recent nature inspired metaheuristic based on the influence of the teacher in the class and learners interactions. In this work, TLBO algorithm is used for routing as it was proposed for continuous optimization problems, despite the fact that the routing in WSNs is a discrete optimization problem. Our proposed approach is shown to be competitive with ACObased approaches in terms of energy consumption and WSN lifetime. The experiments performed in varied scenarios and the results found, illustrate the efficiency of the TLBO approach against the well-known ACO approaches: Energy Efficient Ant Based Routing (EEABR) Algorithm and the Improved Ant Colony Optimization Routing Protocol (IACO). [ABSTRACT FROM AUTHOR]

Summary: In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer‐based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve the quality of learning process and thus enhance the exploitation ability. In addition, a well‐designed learning phase procedure is developed to enhance the learning process between one another in the population. A novel exploration and self‐learning procedures are embedded in the proposed TLBO algorithm, which can enhance the exploitation and exploration capabilities. The proposed algorithm is tested on several well‐known case studies and compared with several efficient algorithms. From the experimental comparisons, the efficient performance of the proposed TLBO is verified. [ABSTRACT FROM AUTHOR]

Nonnegative linear least square (NLLS) is an important optimization problem, which often appears in processing the physical data. In this paper, we use a recently proposed algorithm named harmony search with teaching-learning (HSTL) strategy which derived from Teaching-Learning-Based Optimization (TLBO) for solving NLLS problems. Numerical results are reported which demonstrate very good computational performance. [ABSTRACT FROM PUBLISHER]

This paper studies the performance of an artificial neural network (ANN) with teaching-learning-based optimization (TLBO) for modeling electric energy demand (EED) in Turkey. The ANN with TLBO (ANN-TLBO) was compared to the ANN with backpropagation (ANN-BP) and the ANN with artificial bee colony algorithm (ANN-ABC) models. Gross domestic product, population, import, and export were selected as independent variables in the models. The results reveal that the ANN-TLBO models perform better than the ANN-BP and ANN-ABC models in EED estimation. The average root-mean-square error of the ANN-BP and ANN-ABC models was decreased by 42.3 and 39.3 % using the ANN-TLBO model, respectively. Different scenarios have been studied over a projected 6-year period, from 2013 to 2018, to forecast Turkey's EED. The results of the proposed model give excellent clues with regards to its use in future energy studies. [ABSTRACT FROM AUTHOR]

Hierarchical cooperation mechanism, which is inspired by the features of specialization and cooperation in the social organizations, has been successfully used to increase the diversity of the population and avoid premature convergence for solving complex optimization problems. In this paper, a new two-level hierarchical multi-swarm cooperative TLBO variant called HMCTLBO is presented to solve global optimization problems. In the proposed HMCTLBO algorithm, all learners are randomly divided into several sub-swarms with equal amounts of learners at the bottom level of the hierarchy. The learners of each swarm evolve only in their corresponding swarm in parallel independently to maintain the diversity and improve the exploration capability of the population. Moreover, all the best learners from each swarm compose the new swarm at the top level of the hierarchy, and each learner of the swarm evolves according to Gaussian sampling learning. Furthermore, a randomized regrouping strategy is performed, and a subspace searching strategy based on Latin hypercube sampling is introduced to maintain the diversity of the population. To verify the performance of the proposed approaches, 48 benchmark test functions are evaluated. Conducted experiments indicate that the proposed HMCTLBO algorithm is competitive to some existing TLBO variants and other optimization algorithms. [ABSTRACT FROM AUTHOR]

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth (fz) = 0.06 mm, axial depth of cut (Ap) = 0.74 mm, cutting speed (Vc) = 145.8 m/min, and radial depth of cut (Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11mm and has been validated by confirmation experiments. [ABSTRACT FROM AUTHOR]

Published relationships typically consider liquid critical-flow rate through wellhead chokes of producing oil wells as functions of wellhead pressure, choke size and gas-liquid ratio. Such correlations can be improved by taking into account three additional input variables: gas specific gravity, oil specific gravity and temperature. Novel liquid critical-flow rate models, hybridizing an artificial neural network (ANN) with a teaching-learning-based optimization (TLBO) algorithms, involving 3 and 6 input variables, demonstrate improved accuracy compared to nonlinear regression models, traditional ANN models and published correlations. The improved accuracy of the developed models is assessed statistically using a data set of 113 wellhead flow tests from oil wells in South Iran (with a full data listing included). The ANN-TLBO (6 parameters) developed model is the most accurate, yielding the best liquid critical-flow rate predictions for that data set: coefficient of determination of 0.981; root mean square error of 714; average relative error of 2.09%; and, average absolute relative error of 6.5%. The 6-parameters models outperform the 3-parameters models without over complicating model functionality. This justifies the consideration of all six input variables to deliver improved predictions of wellhead choke liquid critical-flow rates. Calculation of relevancy factors for the 6-parameters ANN-TLBO model to the data set for all six input variables reveals choke size and gas-liquid ratio have maximum and minimum influence in determining the liquid critical-flow rate, respectively. [ABSTRACT FROM AUTHOR]

Most recent studies on machining parameter optimization in machining operations focused on reducing machining cost and energy consumption. However, environmental impacts caused by manufacturing activities were not involved in those studies, which can be quantified by equivalent carbon dioxide emissions. In this study, a direct method was proposed to quantify the carbon emissions generated during multi-pass turning operations. Moreover, machining parameter optimization models of multi-pass turning operations in dry and wet cut environments were established using an experimental design method. Three objectives were considered in both models: carbon emissions, operation time, and machining cost. Furthermore, a multi-objective teaching-learning-based optimization algorithm was used to deal with the models. The optimization results indicated that the use of cutting fluids could significantly reduce carbon emissions and machining cost and improve production efficiency in multi-pass turning operations. [ABSTRACT FROM AUTHOR]

In this paper, the multi-objective optimization of R-245fa vapour condensation inside horizontal tube has been carried out using teaching-learning-based optimization algorithm. The teaching-learning-based optimization algorithm is teaching-learning procedure motivated and works on the impact of a teacher on the outcome of students in a class. Heat transfer coefficient and pressure drop with two parameters have been considered to evaluate the performance of the tube. The mass flux and vapour quality of refrigerant are taken as the parameters. The limit of mass flux and vapour quality are from 100 to 300 kg/m² s and 0.1 to 0.8, respectively. The optimum values of heat transfer coefficient 2820.5W/m² K and pressure drop 1360.2 Pa are obtained with mass flux 137.65 kg/m² s and vapour quality 0.77 using teaching-learning-based optimization algorithm. [ABSTRACT FROM AUTHOR]

This paper develops and suggests modified teaching-learning-based optimization to solve non-smooth nonconvex 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. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) provides high learning speed, but generalization performance needs to be further improved. Therefore, we propose an adaptive ELM with a relaxation factor $$\lambda $$ (A-ELM). In A-ELM, according to the nonlinear degree of actual data, the output layer obtains adaptively $$1-\lambda $$ rate information through the hidden layer and $$\lambda $$ rate information through the input layer. Since the relaxation factor $$\lambda $$ is bound up with the input weights and hidden biases of A-ELM, in order to obtain the optimal $$\lambda $$ , $$\lambda $$ , input weights and hidden biases are obtained together by teaching-learning-based optimization (A-ELM-TLBO). Then, 15 benchmark regression data sets verify the performance of A-ELM-TLBO. Finally, A-ELM-TLBO is applied to set up the mapping relation between NOx emission and operational conditions of a 300 MW circulating fluidized bed (CFB) boiler. Compared with six other models, experimental results show that A-ELM-TLBO has good approximation ability and generalization performance. So, A-ELM-TLBO provides a good basis for tuning CFB boiler operating parameters to reduce NOx emission. [ABSTRACT FROM AUTHOR]

The power against voltage curve for PV power system during shadow condition contains number of local maximum power points (MPPs) and only one global. The classical maximum power point tracking (MPPT) algorithms are designed to follow the global MPP, but they stuck around local MPPs such as fuzzy logic controller (FLC). Therefore, A global MPPT based on teaching-learning-based optimization (TLBO) algorithm has been presented in this paper. The performance of PV system under abnormal conditions such as partial shading has been improved. TLBO algorithm is simple computational steps and faster convergence to optimal solution. A comprehensive assessment of TLBO-based tracker is carried out against FLC and particle swarm optimization (PSO) techniques for same conditions. Six different partial shading patterns have been employed to investigate TLBO performance using MATLAB/Simulink. The parameters of comparison include the tracking speed and overall tracking efficiency. The results confirm that TLBO-based tracker exactly convergence to global MPP under different studied cases. TLBO has best performance compared to the other studied techniques. The tracking speed is increased using TLBO-based tracker; the average tracking time of global MPP is reduced by more than 23.8 % compared with PSO in all studied different partial shading patterns. [ABSTRACT FROM AUTHOR]

This paper proposed a technique based on Teaching-learning-based Optimization (TLBO) for fundamental frequency optimization of simply supported antisymmetric laminated composite plates. The fibre orientations of the layers are selected as the optimization design variables with the aim to find the optimal laminated plates. The first order shear deformation theory is used to calculate the natural frequencies of laminates. In order to perform the optimization based on the TLBO, a special code is written in MATLAB software environment. Several numerical examples are presented to show the efficiency of the proposed algorithm. Also, the optimization problems are solved using other optimization algorithm such as Artifical Bee Colony (ABC) algorithm and the results are compared. It is concluded that TLBO algorithm can effectively be used for fundamental frequency optimization of antisymmetric laminated plates. The CPU time and the number of function evaluations required by TLBO are much smaller than those of the other algorithms tested. This indicates that TLBO is a competitive optimization algorithm for this class of problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self-feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The contemporary Teaching-learning based optimization method (TLBO), is used for solving machine design optimization problems. The method consists of two phases, teaching phase and learning phase. This method requires a small number of known facts which describe the problem in order to be able to find a solution. This research is oriented on software implementation of TLBO methods and problems in real world application for solving optimization problems. An original software has been developed which uses TLBO method. Testing the method is conducted on machine design examples from literature, and the results are compared to optimal solutions from literature. TLBO is a modern and very significant method, and this paper shows its convergence characteristics and practical implementation for engineering problems. [ABSTRACT FROM AUTHOR]

This paper addresses an application of Teaching-Learning-Based Optimization method for the optimal allocation of Distributed Generations (DGs) in radial distribution systems. The problem is formulated to maximize annual energy loss reduction while maintaining better node voltage profiles using penalty function approach. A piecewise linear multi-level load pattern is considered, and the distribution network is reconfigured after optimal placement of DGs in the distribution network. A probability-based heuristic intelligent search (IS) is suggested to enhance the accuracy and convergence of the optimization techniques. IS directs optimization techniques to efficiently scan the problem search space in such a way that a fair candidature is available to all decision variables of the problem. It virtually squeezes the search space while maintaining adequate diversity in population. The proposed method is investigated on the benchmark IEEE 33-bus, 69-bus test distribution systems, and 83-bus real distribution system. The application results show that the proposed optimization methodology provides substantial improvement in convergence characteristics and quality of solutions. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In this paper, a teaching-learning-based optimization algorithm (TLBO) is proposed to solve the multi-skill resource constrained project scheduling problem (MS-RCPSP) with makespan minimization criterion. A task-resource list-based encoding scheme is presented by combining the task list and the resource list, and a left-shift decoding scheme is developed to generate feasible schedules. To achieve satisfactory performances, the balance between global exploration and local exploitation is stressed in designing the TLBO algorithm. At the initialization stage, a balanced resource rule is proposed to generate the initial resource lists, and multiple task list rules are adopted in a hybrid way to initialize the task lists. At the teacher phase and the student phase, the two-point crossover and the resource-based local search are utilized to generate the promising task-resource lists. Moreover, a reinforcement phase is incorporated into the original TLBO with both the permutation-based and the resource-based local search strategies as an additional phase to enhance the local intensification capability. To investigate the influence of parameter setting on the TLBO, numerical tests based on Taguchi method of design of experiment are carried out. In addition, the effectiveness of the proposed balanced resource rule is shown by statistical comparisons with the random resource rule. Computational comparisons between TLBO and the existing algorithm also demonstrate the effectiveness and efficiency of the proposed TLBO in solving the MS-RCPSP. [ABSTRACT FROM AUTHOR]

In typical optimization problems, the number of design variables may be large and their influence on the specific objective function can be complicated; the objective function may have some local optima while most chemical engineers are interested only in the global optimum. For any new optimization algorithms, it is essential to validate their performance, compare with other existing algorithms and check whether they provide the global optimum solutions, which can be done effectively by solving benchmark problems. In this work, seven typical optimization algorithms including the newly proposed TLBO (Teaching-learning-based optimization) based algorithms such as the TLSO (Teaching-learning-self-study optimization) algorithm have been reviewed and tested by using a set of 20 benchmark functions for unconstrained optimization problems to validate the performance and to assess these optimization algorithms. It was found that the TLSO algorithm shows the fastest convergence speed to the optimum and outperforms other algorithms for most test functions. [ABSTRACT FROM AUTHOR]

Nowadays, different types of non-linear and multi-modal hybridized alternating current (AC) power grids with multi-terminal high voltage direct current (HVDC) are of great importance. Energy management aims to reduce costs, network power losses, and environmental pollutants in AC-HVDC electricity grids. This paper describes an improved variant of TLBO (teaching–learning-based optimization) entitled teaching–learning studying-based optimizer (TLSBO) that improves TLBO's global optimization performance. Nevertheless, an advanced model of preventive strategy action is presented that offers an appropriate margin in power output and power transfer through transmission lines to plan for possible contingencies. Following that, a contingency study is conducted to guarantee the system's operation. Also, a corrective strategy action via Re-performing the energy management in the high voltage AC–DC network considering the most critical lines that prevents the system from converging. The suggested upgrade is formed by the inclusion of a change initiative to TLBO known as the studying approach, wherein one individual borrows additional data from some other randomized member in improving its situation. The suggested TLSBO is assessed and contrasted to TLBO and various existing approaches on modified IEEE 30-bus, 57-bus, and large-scale 118-bus AC-HVDC systems. Also, the efficiency of the proposed TLSBO is declared versus the top three techniques in CEC2020 Competition. Compared to the standard TLBO, the proposed TLSBO achieves improvements of 0.04, 4.25, 15.65 and 65.84% for the best, mean, worst and standard deviation metrics, respectively. The simulation outcomes show that the developed TLSBO outperforms the others in terms of efficacy and resilience. TLSBO, on the other hand, has convergence speed and superior quality for the ultimate optimal solution, as well as greater power for escape from converging to local optima than basic TLBO. • Optimal energy management in hybrid AC-HVDC systems is proposed based on TLSBO. • TLSBO has high stochastic change and dynamic exploration/exploitation properties. • The TLSBO is tested on 21 real-world CEC2020 Competition test functions. • The capability and scalability are applied on IEEE 30-, 57-, 118-bus systems. [ABSTRACT FROM AUTHOR]

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner's search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners' improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching–learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP. • MDHFSP with SDST is considered. • A TLBO with reward and punishment mechanism is proposed. • Multiple classes are formed and each of them evolves independently. • Different search times and learning phase are implemented on different classes. • Two search periods are used and teacher's learning phase is added in each period. [ABSTRACT FROM AUTHOR]

Assemble-to-order (ATO) systems refer to a manufacturing process in which a customer must first place an order before the ordered item is manufactured. An ATO system that operates under a continuous-review base-stock policy can be formulated as a stochastic simulation optimization problem (SSOP) with a huge search space, which is known as NP-hard. This work develops an ordinal optimization (OO) based metaheuristic algorithm, abbreviated to OOMH, to determine a near-optimal design (target inventory level) in ATO systems. The proposed approach covers three main modules, which are meta-modeling, exploration, and exploitation. In the meta-modeling module, the extreme learning machine (ELM) is used as a meta-model to estimate the approximate objective value of a design. In the exploration module, the elite teaching-learning-based optimization (TLBO) approach is utilized to select N candidate designs from the entire search space, where the fitness of a design is evaluated using the ELM. In the exploitation module, the sequential ranking-and-selection (R&S) scheme is used to optimally allocate the computing resource and budget for effective selecting the critical designs from the N candidate designs. Finally, the proposed algorithm is applied to two general ATO systems. The large ATO system comprises 12 items on eight products and the moderately sized ATO system is composed of eight items on five products. Test results that are obtained using the OOMH approach are compared with those obtained using three heuristic methods and a discrete optimization-via-simulation (DOvS) algorithm. Analytical results reveal that the proposed method yields solutions of much higher quality with a much higher computational efficiency than the three heuristic methods and the DOvS algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization algorithm called fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching-learning-based optimization namely status monitor, fuzzy adaptive teaching-learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems. [ABSTRACT FROM AUTHOR]

Estimation of parameters of chaotic systems is a subject of substantial and well-developed research issue in nonlinear science. From the viewpoint of optimization, parameter estimation can be formulated as a multi-modal constrained optimization problem with multiple decision variables. This investigation makes a systematic examination of the feasibility of applying a newly proposed population-based optimization method labeled here as teaching-learning-based optimization (TLBO) to identify the unknown parameters for a class of chaotic system. The preliminary test demonstrates that despite its global fast coarse search capability, teaching-learning-based optimization often risks getting prematurely stuck in local optima. To enhance its fine (local) searching performance of TLBO, Nelder-Mead simplex algorithm-based local improvement is incorporated into TLBO so as to continually search for the global optima through the reflection, expansion, contraction, and shrink operators. Working with the well-established Lorenz system, we assess the effectiveness and efficiency of the proposed improved TLBO strategy. The empirical results indicate the success of the proposed hybrid approach in which the global exploration and the local exploitation are well balanced, providing the best solutions for all instances used over other state-of-the-art metaheuristics for chaotic identification in literature, including particle swarm optimization, genetic algorithm, and quantum-inspired evolutionary algorithm. [ABSTRACT FROM AUTHOR]

The present study proposes a maximum power point tracking (MPPT) method in which improved teaching-learning-based optimization (I-TLBO) is applied to perform global MPPT of photovoltaic (PV) module arrays under dissimilar shading situations to ensure the maximum power output of the module arrays. The proposed I-TLBO enables the automatic adjustment of teaching factors according to the self-learning ability of students. Incorporating smart-tracking and self-study strategies can effectively improve the tracking response speed and steady-state tracking performance. To evaluate the feasibility of the proposed I-TLBO, a HIP-2717 PV module array from Sanyo Electric was employed to compose various arrays with different serial and parallel configurations. The arrays were operated under different shading conditions to test the MPPT with double, triple, or quadruple peaks of power-voltage characteristic curves. Boost converters were employed with TMS320F2808 digital signal processors to test the proposed MPPT method. Empirical results confirm that the proposed method exhibits more favorable dynamic and static-state response tracking performance compared with that of conventional TLBO. [ABSTRACT FROM AUTHOR]

This paper presents a modified TLBO (teaching-learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data. [ABSTRACT FROM AUTHOR]

Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labeled here as Teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the Teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Hénon as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the Teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed. Furthermore, possible engineering applications of directing chaotic orbits are discussed. [ABSTRACT FROM AUTHOR]

In this paper, a population-based robust enhanced teaching-learning-based optimization (ETLBO) algorithm with reduced computational effort and high consistency is applied to design stable digital infinite-impulse response (IIR) filters in a multiobjective framework. Furthermore, a decision-making methodology based on fuzzy set theory is applied to handle nonlinear and multimodal design problems of the IIR digital filter. The original teaching-learning-based optimization (TLBO) algorithm has been remodeled by merging the concepts of opposition-based learning and migration for the selection of good candidates and to maintain diversity, respectively. A multiobjective IIR digital filter design problem takes into consideration magnitude and phase response of the filter simultaneously, while satisfying stability constraints on the coefficients of the filter. The order of the filter is controlled by a control gene whose value is also along with filter coefficients, to obtain the optimum order of the designed IIR filter. Results illustrate that ETLBO is more capable and efficient in comparison to other optimization methods for the design of all types of filter, i.e. high-pass, low-pass, band-stop, and band-pass IIR digital filters. [ABSTRACT FROM AUTHOR]

A variant of teaching-learning-based optimization algorithm (TLBO) with multi-classes cooperation and simulated annealing operator (SAMCCTLBO) is proposed in paper. To take full advantage of microteaching, the population is divided into several sub-classes, the mean of all learners in teacher phase of original TLBO is replaced by the mean solutions of different sub-classes, the modification might make the mean solutions improved quickly for the effect of microteaching is often better than teaching in big classes. With considering the limitation of learning ability of learner, the learners in different sub-classes only learn new knowledge from others in their sub-classes in learner phase of SAMCCTLBO, and all learners are regrouped randomly after some generations to improve the diversity of the sub-classes. The diversity of the whole class is improved by simulated annealing operator. The effectiveness of the proposed algorithm is tested on several benchmark functions, the results demonstrate that SAMCCTLBO has some good performances when compared with some other EAs. [ABSTRACT FROM AUTHOR]