Parameter estimation of nonlinear chaotic system by improved TLBO strategy

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

An integer encoding grey wolf optimizer for virtual network function placement

Teaching-Learning-Based Optimization (TLBO) algorithm is an evolutionary powerful algorithm that has better global searching capability. However, in the later period of evolution of the TLBO algorithm, the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions, which lead to a trap in local optima and premature convergence. This paper presents an improved version of the TLBO algorithm based on Laplace distribution and Experience exchange strategy (LETLBO). It uses Laplace distribution to expand exploration space. A new experience exchange strategy is applied to make good use of experience information to identify more promising solutions to make the algorithm converge faster. The experimental performances verify that the LETLBO algorithm enhances the solution accuracy and quality compared to original TLBO and various versions of TLBO and is very competitive with respect to other very popular and powerful evolutionary algorithms. Finally, the LETLBO algorithm is also applied to parameter estimation of chaotic systems, and the promising results show the applicability of the LETLBO algorithm for problem-solving.

Teaching---learning-based optimisation (TLBO) is an emerging gradient-free optimisation algorithm inspired by interactions between students and teacher in classrooms. TLBO has no control parameter to be tuned by user. This property makes it popular in research community. It has been successfully applied to challenging optimisation problems in different areas. In this study, TLBO is assisted to find optimal setting of thyristor-controlled series compensators in electric power systems. The experiments have been done for both N-1 and N-2 line outage contingencies. The results show that TLBO performs well in solving this problem.

An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase

Identification of nonlinear systems is one of the important problems in engineering. Chaotic systems are among those nonlinear systems that have been in the center of attention of many researchers because of their complex and unpredictable behaviors. This paper proposes an algorithm for optimally estimating the parameters of system by minimizing the mean of squared errors (MSE) index. In this paper, Teaching Learning Based Optimization (TLBO) algorithm is used for solving both offline and online parameter estimation problems for chaotic systems. The validity of this algorithm in terms of convergence speed and parameter accuracy in comparison to other popular optimization algorithms such as Standard PSO (PSO), Differential Evolution (DE), Adaptive Particle Swarm Optimization (APSO) and Genetic Algorithm (GA) is shown through an illustrative example for the modeling of chaotic systems. Furthermore, in order to demonstrate the feasibility of this algorithm, it is applied to the problem of parameter identification of a well-known nonlinear Lorenz chaotic system. According to simulation results, the proposed algorithm is a very suitable algorithm for online parameter identification for a class of nonlinear chaotic systems.

Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid

An important problem in the study of evolutionary algorithms is how to continuously predict promising solutions while simultaneously escaping from local optima. In this paper, we propose an elitist probability schema (EPS) for the first time, to the best of our knowledge. Our schema is an index of binary strings that expresses the similarity of an elitist population at every string position. EPS expresses the accumulative effect of fitness selection with respect to the coding similarity of the population. For each generation, EPS can quantify the coding similarity of the population objectively and quickly. One of our key innovations is that EPS can continuously predict promising solutions while simultaneously escaping from local optima in most cases. To demonstrate the abilities of the EPS, we designed an elitist probability schema genetic algorithm and an elitist probability schema compact genetic algorithm. These algorithms are estimations of distribution algorithms (EDAs). We provided a fair comparison with the persistent elitist compact genetic algorithm (PeCGA), quantum-inspired evolutionary algorithm (QEA), and particle swarm optimization (PSO) for the 0---1 knapsack problem. The proposed algorithms converged quicker than PeCGA, QEA, and PSO, especially for the large knapsack problem. Furthermore, the computation time of the proposed algorithms was less than some EDAs that are based on building explicit probability models, and was approximately the same as QEA and PSO. This is acceptable for evolutionary algorithms, and satisfactory for EDAs. The proposed algorithms are successful with respect to convergence performance and computation time, which implies that EPS is satisfactory.

Optimization of Surface Roughness in Plasma Arc Cutting of AISID2 Steel Using TLBO

The present work focuses on the performance modeling of surface grinding to attain an optimum parameter setting for the minimum coefficient of friction and surface roughness. The experimental data were collected during the surface grinding process using dry conditions and minimum quantity lubrication (MQL) as a clean technology lubricant. The usage material was SKD 61 tool steel. The varied surface grinding parameters were the depth of cut and table speed, wherein each had three levels. The surface grinding operation was performed by using a full factorial design 2 × 3 × 3. Backpropagation neural network (BPNN) was first applied to obtain the modeling of the surface grinding experiment, the objective function, the predictions of coefficient of friction, and surface roughness. The objective function is then modified into a fitness function. Finally, this fitness function is utilized in multi-objective optimization using the teaching–learning-based optimization (TLBO) method to attain the surface grinding parameters’ levels that simultaneously produce a minimum coefficient of friction and surface roughness. Based on our experimental results, the combination of BPNN-TLBO can be applied to simultaneously minimize the coefficient of friction and surface roughness in the grinding of SKD 61 by implementing MQL and setting the feeding speed at 150 mm/s and the depth of cut at 0.01 mm. As the result, the minimum surface roughness is 0.376 μm, and the coefficient of friction is 0.333.KeywordsBackpropagation neural networkClean technologyMinimum quantity lubricationSurface grindingTeaching–learning-based optimization

An improved teaching–learning-based optimization with neighborhood search for applications of ANN

One of the most critical tasks during the application of photovoltaic (PV) systems is to harvest the optimal output power at various environmental scenarios, which is called maximum power point tracking (MPPT). Though plenty of advanced techniques are developed to achieve this purpose, most of them have corresponding prominent disadvantages, such as inefficient tracking ability, high computation burden, and complex convergence mechanism. Therefore, this work aims to propose a novel and powerful bio-inspired meta-heuristic optimization algorithm called peafowl optimization algorithm (POA), which is inspired by the group food searching behaviors of peafowl swarm. It can effectively achieve a suitable balance between local exploitation and global exploration thanks to its efficient exploratory and exploitative searching operators. Thus, a satisfactory MPPT performance for PV systems under partial shading condition (PSC) can be obtained based on POA. Moreover, two case studies, e.g., start-up test and step change in solar irradiation with constant temperature, are adopted to fairly and comprehensively validate the superiority and effectiveness of POA in contrast with particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), respectively.

After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.

A new meta-heuristic method, comprehensive teaching learning harmony search optimization algorithm (CTLHSO), is developed in this paper for the simultaneous reconfiguration and optimal allocation of distributed generation resources in radial distribution systems. The proposed method is a hybridization of the teaching–learning-based optimization (TLBO) and the harmony search (HS) algorithms. Primarily, eleven mathematical benchmark functions are used to test the performance of the CTLHSO algorithm. The results are then compared with that of global best artificial bee colony (G-ABC), particle swarm artificial bee colony (PS-ABC), TLBO and improved TLBO (I-TLBO) with identical parameters and initial conditions. The results show that the CTLHSO performance is better than the G-ABC, PS-ABC, TLBO and I-TLBO. Subsequently, CTLHSO is implemented on the IEEE 33-bus and 69-bus radial distribution systems for network reconfiguration and optimal placement of distributed generation resources to minimize the power losses and improve the voltage profiles. Five case studies at three different load levels are carried out. The results obtained are found to be better than those obtained with HS algorithm, genetic algorithm, refined genetic algorithm and fireworks algorithm.

Shape memory alloys (SMAs) are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft components, military instruments, fabricating elements, and bio-medical devices, among others. Despite their remarkable characteristics, effective production of these alloys continues to be a problem for researchers worldwide. This paper has been aimed to examine the considered machining responses that is, tool wear rate (TWR) and diametral deviation (DD) in electrical discharge machining (EDM) of Fe-based shape memory alloy (SMA) using a Cu-electrode under the varying settings of input factors namely as; pulse on time (Ton), pulse off time (Toff), peak current (Ip), and gap voltage (GV). The central composite design matrix has been employed for planning the main runs. The experimental results reflect the lowest and highest for TWR as; 0.0694 and 0.1357 g/min; and for DD as; 0.02 and 0.19 mm, respectively. The microstructure analysis of the EDMed work samples and electrode surfaces have also been conducted using scanning electron microscopy (SEM). The micrographs exposed the development of debris, craters, micro-cracks, and recast layer creation on the workpiece surface and electrode tool as well. The creation of debris develops as a result of large spark energy at the work piece-tool contact as a result of a high peak current and long pulse on time. Furthermore, single and multi-objective optimization of investigated responses (i.e. TWR and DD) were tried using the desirability approach, Genetic Algorithm (GA), and teacher learning based optimization (TLBO), techniques. The obtained multi-response optimized values for TWR and DD by using desirability approach, Genetic algorithm (GA), and Teacher Learning Based Optimization (TLBO) are as; 0.0815 g/min and 0.0527 mm; 0.1529 g/min and 0.2547 mm; 0.1675 g/min and 0.2533 mm., correspondingly.