Teaching Learning Based Optimization Approach Research Articles

Portfolio selection model using teaching learning-based optimization approach

<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization problems. This article presents a portfolio selection model using the TLBO approach to maximize the portfolio's Sharpe Ratio. The Sharpe ratio combines both expected return and risk. This algorithm model the natural teaching process of the classroom with two main phases, viz., teaching and learning. Performance analysis has been undertaken to investigate the suitability of TLBO based solution approach by comparing it with genetic algorithm (GA) and particle swarm optimization (PSO) on the real datasets, Deutscher Aktienindex (DAX) 100, Hang Seng 31, Standard and Poor’s (S&P) 100, financial times stock exchange (FTSE) 100, and Nikkei 225. The empirical results verify the superiority of the TLBO over GA and PSO.</p>

An approach for extractive text summarization using fuzzy evolutionary and clustering algorithms

Automatic text summarization schemes are indeed helpful for glancing briefly at the text document. With this motivation, we introduce here a two-stage hybrid model for text summarization task by utilizing the strength of various approaches. In the first step, we cluster the sentences of a document according to their similarity using a partitional clustering algorithm. We then use a linear combination of the normalized Google distance and word mover’s distance to differentiate two sentences. The notion of gap statistics is exploited to approximate the number of partitions for the given document needed in the partitional clustering algorithm. We extract the significant sentences from each cluster (partition), which are recognized by their adjusted text feature scores, in the second step. The teaching–learning based optimization approach is used to find the optimal weights for the text features whereas a fuzzy inference system with a full-fledged knowledge base generated by humans is employed to determine the final score of the sentences. Moreover, we have also proposed an exact method to give a solution for the summarization problem by modeling it as an Integer Linear Programming (ILP) problem. We evaluate the proposed methods on three different datasets: DUC 2001, DUC 2002, and CNN. The observed results on these standard datasets manifest the efficacy of the proposed methods. We further show that partitioning a document in an optimal number of clusters plays a major role in content coverage in summaries. The performance of the proposed hybrid method shows that the combination of fuzzy, evolutionary, and clustering algorithms produces good summaries of the documents.

A Novel Software Package Selection Method Using Teaching–Learning Based Optimization and Multiple Criteria Decision Making

Software packages that meets the requirements of an organization should be appropriately investigated and evaluated. Picking up a wrong software package may adversely influence the business process and working function of an organization. Inappropriate software selection can turn out to be costly and it is a time-consuming decision-making process. This paper aims to provide a base for selecting the open source software packages based on analytic hierarchy process and technique for order preference similarity to ideal solution methodologies. In addition, the priority weights are generated and optimized by using teaching-learning based optimization approach. A well-organized algorithmic procedure is given in detail and a numerical example is examined to illustrate the validity and practicability of our proposed methodologies.

Fuzzy teaching learning based optimization approach for solving the QoS-aware web service selection problem in uncertain environments

This paper proposes a new fuzzy optimization approach for the quality of service (QoS)-aware web service selection (QWSS) problem under uncertain QoS parameters. To deal with the ambiguity phenomena of some web services’ QoS characteristics like the execution time attribute, which strongly fluctuates depending on the rates of network traffic and systems congestion; hence, the generalized trapezoidal fuzzy number (GTrFN) is used to represent the uncertainty of the QoS values. Subsequently, the QWSS problem with GTrFN-based QoS proprieties is modeled as a fuzzy optimization (FQWSS) problem. To solve the FQWSS model, we design a new fuzzy teaching learning based optimization (FTLBO) algorithm, for which a local search method and an elitism operator are embedded into it to enhance the exploitation ability and speed up the convergence of FTLBO. The experiment results of comparing the FTLBO algorithm to some recent fuzzy optimization approaches demonstrate its effectiveness and performance in solving the FQWSS problem with different scales.

An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

Minimization of total completion time on a batch processing machine with arbitrary release dates: an effectual teaching–learning based optimization approach

In this research study, a single machine batch-processing problem with release dates to minimize the total completion times of jobs is considered. The machine is able to process at most a certain number of jobs at the same time and the total size of the jobs allocated to a batch cannot exceed the machine capacity. Since the research problem has been shown to be NP-hard, an effective Teaching–Learning Based Optimization (TLBO) is proposed. A constructive heuristic approach is developed to generate initial feasible solutions for the TLBO. In order to enhance the efficiency of the proposed TLBO, a Tabu Search (TS) with three different neighborhood generation mechanisms is incorporated into the teaching phase and learner phase separately. To validate the outcomes of the proposed TLBO, we carry out an experimental study and compare its outcomes with the best-known results obtained by several meta-heuristic methods on a set of benchmark instances derived from the literature. The computational results show that the proposed TLBO with the incorporation of TS in its learning phase is able to come up with very good quality solutions.

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing.

Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Though the complex programming, parameters selection, and ability to obtain the global optimal solution are major concerns of evolutionary based algorithms, in the present paper, the optimization was performed by using efficient algorithms i.e., teaching–learning-based optimization and bacterial foraging optimization. Furthermore, the weighted sum method was used to transform the diverse responses into a single response, and then multi-objective optimization was performed using the teaching–learning-based optimization method and the standard bacterial foraging optimization method. Finally, the optimum results reported by these methods are compared to choose the best method. In fact, owing to better convergence within shortest time, the teaching–learning-based optimization approach is recommended. It is expected that the outcome of this research would help to efficiently and intelligently perform the hard-turning process under automatic and optimized environment.

An energy efficient teaching learning based optimization approach for common content distribution in mobile ad hoc networks

An energy efficient content distribution technique in mobile ad hoc networks is proposed in this paper. Based on bit rate, remaining energy and maximum allowable delay, a fitness value is evaluated for each node in the network. A set of nodes having maximum fitness values are elected as cluster heads. It is determined by applying teaching learning based optimization technique, only after the nodes satisfy an energy constraint. The clusters of nodes are formed by assigning the remaining nodes to their corresponding cluster heads. The cluster heads can receive the required packets from the base station via long range communication and forward the same to the cluster members. Efficient energy expenditure by this approach leads to an increased network lifetime. The experimental results show effectiveness of the proposed approach over K-means Clustering and U+M techniques in terms of reduced energy consumption and improved network lifetime.

Optimal planning of distributed energy resources in harmonics polluted distribution system

Abstract In this study, harmonics related to power quality issue and energy demand growth are considered simultaneously towards the realistic planning of medium voltage radial distribution system. Mostly, harmonics of distribution networks are produced in the presence of non-linear loads. Therefore, Distributed Energy Resource (DERs) can be placed to mitigate the harmonic distortions and to supply the required system energy demand. This paper presents a Modified Group experience of Teaching Learning Based Optimization approach, which can deal with allocation of DERs efficaciously in distorted and non-distorted radial distribution networks. The effectiveness of the proposed approach is validated on standards 33-bus and 69-bus test systems along with 83-bus (Taiwan Power Company) practical radial non-distorted distribution system. The results are compared with already well-established existing methods as suggested in the literature. Further, the proposed algorithm is applied to DER planning considering harmonics generating loads in above-mentioned test systems. The results with linear as well as non-linear loads on all three test systems prove that the proposed strategy can be a robust approach to enhance the system performance towards mitigating increased load demand within the constraints of the distribution system.

Teaching Learning Based Optimization Approach for the Development of an Effective Master Production Schedule

Teaching Learning Based Optimization Approach for the Development of an Effective Master Production Schedule

A novel teaching-learning based optimization approach for design of broad-band anti-reflection coatings

Abstract The design of thin-film multilayered anti-reflection (AR) coating is quite an intricate task due to highly nonlinear and complex dimensional search space, which includes many local minima. In this paper, a novel teaching-learning based optimization (TLBO) approach is employed to design ultra-low reflective coating over a broad wavelength-band using multilayer thin-film structures for optoelectronic devices. The algorithm is implemented using LabVIEW as a programming tool. Various design specific input parameters such as scanning range of wavelengths, step-size, angle of incidence, number of layers, the name and sequence of coating materials etc. are required to be fed by the user on the graphical user interface. The algorithm minimizes the average reflectivity computed over given wavelength range by tuning the thickness of layers in the multilayer stack. The reliability and evolution of design solution with iterations have been systematically investigated for different learner-sizes. Finally, using the optimized learner size and desired number of iterations, the optimum AR design is obtained in terms of the thickness of each layer for the multilayer AR coating. The effectiveness of the TLBO approach has been compared with that of an established algorithm, i.e. genetic algorithm (GA), by means of Wilcoxon singed ranked test. It is concluded that the TLBO can be a very efficient, simpler and relatively faster approach to address complex optimization problems such as broad-band AR coating designs.

Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

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.

Maximum power point computation using current–voltage data from open and short circuit regions of photovoltaic module: A teaching learning based optimization approach

Solar photovoltaic module operating with constant solar irradiance needs entire power (P)-voltage (V) curve tracing to find the maximum power point (MPP), if perturb and observe or incremental conductance algorithms are used. Therefore, there is a need of an algorithm which tracks the MPP without tracing entire P-V curve. This paper focuses on an MPP prediction method which is only based on a few data collected in the open-circuit and short-circuit regions of current (I)–voltage (V) curves of a cell or a module. The work presented here assumes I-V curve of a module to follow I-V power relation, I = aVb + c. The power model coefficients a, b, and c are determined by teaching-learning based optimization. The voltage at the MPP (Vmp) is computed by correlating the incremental conductance algorithm with the I-V power model and thereafter power at the MPP (Pmax) is obtained. The effectiveness of the algorithm is confirmed by applying it to the reported I-V curve of different types of solar cells. The obtained values of Vmp and Pmax are found to be in excellent agreement with their reported values. The algorithm is successfully applied to experimentally measured I-V curves of a silicon cell and a module.

Transient Stability Constrained Optimal Power Flow Using Teaching Learning Based Optimization

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

Transient Stability Constrained Optimal Power Flow Using Teaching Learning-Based Optimization

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

A teaching–learning based optimization approach for economic design of X-bar control chart

Being simple to use X-bar control chart has been most widely used in industry for monitoring and controlling manufacturing processes. Measurements of a quality characteristic in terms of samples are taken from the production process at regular interval and the sample means are plotted on this chart. Design of a control chart involves the selection of three parameters, namely the sample size (n), the sampling interval (h) and the width of control limits (k). In case of economic design, these three control chart parameters are selected in such a manner that the total cost of controlling the process is the least. The effectiveness of this design depends on the accuracy of determination of these three parameters. In this paper, a new efficient and effective optimization technique named as teaching–learning based optimization (TLBO) has been used for the global minimization of a loss cost function expressed as a function of three variables n, h and k in an economic model of X-bar chart based on unified approach. In this work, the TLBO algorithm has been modified to simplify the tuning of teaching factor. A MATLAB computer program has been developed for this purpose. A numerical example has been solved and the results are found to be better than the earlier published results. Further, the sensitivity analysis using fractional factorial design and analysis of variance have been carried out to identify the critical process and cost parameters affecting the economic design.

Oppositional teaching learning based optimization approach for combined heat and power dispatch

This paper presents a new optimization technique i.e. teaching learning based optimization (TLBO) to solve combined heat and power dispatch (CHPD) problem with bounded feasible operating region. To accelerate the convergence speed and improve the simulation results, opposition based learning (OBL) is incorporated in basic TLBO algorithm. The potential of the proposed TLBO and oppositional TLBO (OTLBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for three different standard combined heat and power dispatch problems of power systems. The results of the proposed methods are compared with other popular optimization techniques like evolutionary programming (EP), three variants of particle swarm optimization (PSO), real coded genetic algorithm (RCGA), differential evolution (DE) and bee colony optimization (BCO). Through the simulation of MATLAB programming it is seen that OTLBO provides better results than all other optimization techniques at less computational time.

Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint

This article presents a novel teaching learning based optimization (TLBO) to solve short-term hydrothermal scheduling (HTS) problem considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro plants. TLBO is a recently developed evolutionary algorithm based on two basic concept of education namely teaching phase and learning phase. In first phase, learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves. The algorithm does not require any algorithm-specific parameters which makes the algorithm robust. Numerical results for two sample test systems are presented to demonstrate the capabilities of the proposed TLBO approach to generate optimal solutions of HTS problem. To test the effectiveness, three different cases namely, quadratic cost without prohibited discharge zones; quadratic cost with prohibited discharge zones and valve point loading with prohibited discharge zones are considered. The comparison with other well established techniques demonstrates the superiority of the proposed algorithm.