Teaching-Learning Based Optimization (TLBO)

Optics scholars did not only discover optical phenomena and laws governing them. Some of them also invented impressive optical systems and instruments or offered us techniques to juggle with optical signals and rays. One typical example of the impressive optical systems is the camera obscura invented by Ibn Al-Haytham. For techniques enabling us to easily handle optical rays, one can mention Young's method to handle rays put into play by refraction. Nine centuries before him, Ibn Sahl proposed an elegant method to manipulate refraction related rays. These three examples will be handled in this paper, together with a historical overview inviting the reader to be in the context of this fascinating works.

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

To ensure stability, security, and protection of electrical equipment from the damage the suitable coordination must be made in interconnected networks. In this paper, the nonlinear multivariable optimization techniques have been used with different performance indexes: Sequential quadratic programming (SQP), Sequential quadratic programming legacy (SQP-Legacy), Interior-Point and Active-Set for IEEE- 8 bus test system.  This system consists of twenty-eight protective relays divided into fourteen directional overcurrent relays (DOCR) and fourteen distance relays (DR). It has been tested in the ETAP environment to obtain three-phase short circuit current at the near and far end faults and operating time for all DOC relays for near-end fault as well as test the second zone time for distance relays (TZ2) with pilot signal (WP)and without pilot signal (WOP) of the proposed algorithm was used to reduce overall operating time of DOC relays and obtain optimal values for time multiplier set...

This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo search algorithm (dCSA). The standard cuckoo search algorithm is a metaheuristics search algorithm that mimic the behavior of brood parasitism of some cuckoo species and Levy flight behavior of some fruit flies and birds. It, however uses fixed value for control parameters (control probability and step size) and this method have drawbacks with respect to quality of the solutions and number of iterations to obtain optimal solution. Therefore, the dCSA is developed to address these problems in the CSA by introducing random inertia weight strategy to the control parameters so as to make the control parameters dynamic with respect to the proximity of a cuckoo to the optimal solution. The developed dCSA was compared with CSA using ten benchmark test functions. The results obtained indicated the superiority of dCSA over CSA by generating a near global optimal result for 9 out of the ten bench...

A comparison between different modern population based optimization methods applied to the design of scannable circular antenna arrays is presented in this paper. This design of scannable circular arrays considers the optimization of the amplitude and phase excitations across the antenna elements to operate with optimal performance in the whole azimuth plane (360 • ). Simulation results for scannable circular arrays with the amplitude and phase excitation optimized by genetic algorithms, particle swarm optimization and the differential evolution method are provided. Furthermore, in order to set which design case could provide a better performance in terms of the side lobe level and the directivity, a comparative analysis of the performance of the optimized designs with the case of conventional progressive phase excitation is achieved. Simulation results show that

Artificial Intelligence (AI) control techniques involve the use of algorithms and models that mimic human intelligence and allow robots to learn from data, solve complex problems, and form opinions. These methods are extensively used in many different domains, such as data analysis, robotics, automation, and control systems. In recent years, the integration of renewable energy sources has led to the expansion and sophistication of power systems resulting in recurrent load frequency fluctuations.  The best control method to handle these fluctuations might not be obtained by strictly applying traditional or conventional control procedures. Hence, AI computing methods,  are increasingly being employed to address these challenges. This paper aims to provide a detailed review of various AI control techniques such as Fuzzy Logic Control (FLC), Teaching-Learning-Based Optimization (TLBO), The Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Bee Algorithms (BA), for load frequency management in power systems to enable other researchers explore the strength and weaknesses/ limitations of each technique for solving frequency control challenges in power systems. This review showed that there have been limited exploration of the Bees Algorithm in handling Load Frequency Control  in power systems.

Character recognition is an image analysis method, where handwritten images are given as input to a system and then the job of the system is to recognize them on the basis of information available about them. Pattern recognition capability of human beings cannot be imitated, however up to a certain extent it can be achieved by the use of neural network. In this paper an attempt is made to recognize English characters by the use of back propagation algorithm (BPA) as well as Teaching Learning Based Optimization (TLBO). TLBO is a recent algorithm used to solve many real world problems, which is inspired by practical environment of a class room, whereas Back Propagation algorithm is a generalization of Least Mean Square algorithm.

Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.

In an electric power network, from view point of many consumers, power generation, and transmission should become exactly without interruption. Because the distribution system relates directly to consumers so should have high reliability. Overcurrent relays are often used for primary protection of distribution lines and secondary protection of radial transmission lines. The main objective in protection systems is the rapid separation of minimum amount of equipment as much as possible and without operation interference. These should be done after error detection in the system. This paper proposes proper coordination of directional overcurrent relays (DOCR) based on Particle swarm optimization (PSO) algorithm with take into account of fault current direction restriction. The proposed algorithm has been tested in the standard IEEE 30 bus distribution system.

We propose a swarm-based optimization algorithm inspired by air currents of a tornado. Two main air currentsspiral and updraft-are mimicked. Spiral motion is designed for exploration of new search areas and updraft movements is deployed for exploitation of a promising candidate solution. Assignment of just one search direction to each particle at each iteration, leads to low computational complexity of the proposed algorithm respect to the conventional algorithms. Regardless of the step size parameters, the only parameter of the proposed algorithm, called tornado diameter, can be efficiently adjusted by randomization. Numerical results over six different benchmark cost functions indicate comparable and, in some cases, better performance of the proposed algorithm respect to some other metaheuristics.

A comparison between different modern population based optimization methods applied to the design of scannable circular antenna arrays is presented in this paper. This design of scannable circular arrays considers the optimization of the amplitude and phase excitations across the antenna elements to operate with optimal performance in the whole azimuth plane (360 •). Simulation results for scannable circular arrays with the amplitude and phase excitation optimized by genetic algorithms, particle swarm optimization and the differential evolution method are provided. Furthermore, in order to set which design case could provide a better performance in terms of the side lobe level and the directivity, a comparative analysis of the performance of the optimized designs with the case of conventional progressive phase excitation is achieved. Simulation results show that

The operating time of directional overcurrent relays (DOCRs) can be reduced with user-defined relay characteristics considering plug setting (PS), time multiplier setting (TMS), and relay characteristic coefficients (λ and γ). This study presents a comparative analysis of relay coordination with standard and user-defined relay characteristics. The proposed relay coordination scheme is formulated as a non-linear constraint optimization problem. The grey wolf optimization (GWO) algorithm is used to determine the optimal relay settings and total operating time of DOCRs. The performance of the proposed scheme is tested on the standard 8-bus, 9-bus, and 15-bus systems. The results show that the total operating time of DOCRs with user-defined relay characteristics is better than that with standard relay characteristics. The results of the GWO algorithm are compared with the performance of optimization techniques used in literature to solve the relay coordination problem.

Now-a-days to ensure power continuity and system’s reliability the protection system is to be designed properly for distribution systems to handle the faults to avoiding the damage to the equipments and to the service engineers. Different types of relays with different working principles are used to detect different types of faults in the system. In order to avoid mal operation of relays, proper coordination is to be carried out. The objective of this paper is to maintain the relay coordination as well as to decrease the working time of relays by optimizing the values of time dial setting (TDS) using linear programming problem technique (LPP). The inequality constraints guarantee the coordination margin for each primary or backup relay pairs having a fault very close to the primary relay. Simulation is carried out on a IEEE 15 bus balanced radial distribution system with 3 different types of relays namely standard inverse, extremely inverse and very inverse relay and the results are...

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

Emission of greenhouse gases and depletion of fossil fuel reserves are two key drivers, which are forcing the mankind to generate the future energy demand from the renewable energy resources. These resources are generally distributed in nature and are directly integrated at distribution levels. Increasing penetration of the distributed energy resources in distribution power networks creates additional operational and control issues. These are mostly regulatory, economical load dispatching, power quality and protection issues. Generally power distribution systems are protected with the help of dedicated over current based protection schemes. But increasing share of distributed energy resources penetration in electric utilities poses a serious threat to the existing protection coordination schemes of the distribution systems. Distributed energy resources connected distribution networks become interconnected in nature and protection coordination schemes, which are designed for unidirectional flow of fault currents become ineffective/non-functional. Therefore, new protection coordination schemes are required for providing the adequate protection coordination for distributed energy resources connected electric power networks. In the available literature, the protection coordination schemes for radial distribution systems and developments in the area of protection coordination are discussed in detail. A thorough review for all these protection coordination schemes for distribution systems with and without distributed energy resources is done in this review article. It includes the analytical and artificial intelligence based techniques application for coordination of protective relays in the distribution systems. The limitations and research gaps in the area of protection coordination schemes are also presented in this review article. The aim of this research paper is to bring all the available research in the area of relay coordination on one platform, so that it will help the emerging researcher to identify the future scope of relay coordination application for distributed energy resources connected distribution systems.

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in single-objective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to realworld thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.

We propose a swarm-based optimization algorithm inspired by air currents of a tornado. Two main air currentsspiral and updraft-are mimicked. Spiral motion is designed for exploration of new search areas and updraft movements is deployed for exploitation of a promising candidate solution. Assignment of just one search direction to each particle at each iteration, leads to low computational complexity of the proposed algorithm respect to the conventional algorithms. Regardless of the step size parameters, the only parameter of the proposed algorithm, called tornado diameter, can be efficiently adjusted by randomization. Numerical results over six different benchmark cost functions indicate comparable and, in some cases, better performance of the proposed algorithm respect to some other metaheuristics.

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 su er 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 modi ed teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. e proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversi cation measure, and intensi cation 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. is 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. e 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 learningbased 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 e ciency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

The genetic algorithm is regarded as one of the foremost evolutionary algorithms, especially in optimization problems. In addition to optimization problems, genetic algorithms can contribute to solving other problems. In the context of social learning, particularly recommendation systems, researchers are usually faced with a problem of lack of data when testing their approaches. This is where the genetic algorithm can intervene to generate additional simulation data from an already existing database. In this article, we will outline how we can apply the genetic algorithm to address the lack of data, particularly in our context of recommendation systems within social learning.

Optics scholars did not only discover optical phenomena and laws governing them. Some of them also invented impressive optical systems and instruments or offered us techniques to juggle with optical signals and rays. One typical example of the impressive optical systems is the camera obscura invented by Ibn Al-Haytham. For techniques enabling us to easily handle optical rays, one can mention Young's method to handle rays put into play by refraction. Nine centuries before him, Ibn Sahl proposed an elegant method to manipulate refraction related rays. These three examples will be handled in this paper, together with a historical overview inviting the reader to be in the context of this fascinating works.

Voltage stability represents one of the main issues in electrical power system. Under voltage load shedding (UVLS) has long been regarded as one of the most successful techniques to prevent the voltage collapse. However, the ordinary load shedding schemes do not consider the different load models and decreasing in the economic cost that resulted from load disconnection, so the dual techniques of load shedding with reactive compensation are needed. Usually loads being modeled as constant power, while in fact of load flow the various load models are utilized. An investigation of optimal dual load shedding with reactive compensation for distribution system based on direct backward forward sweep method (DBFSM) load flow along with a comparison among the other load models are presented in this paper. The teaching learning-based optimization (TLBO) algorithm is executed in order to reduce power losses and enhance the voltage profile. This algorithm is tested and applied to IEEE-16 bus distribution test system to find the optimal superior capacitor size and placement while minimizing load shading for the network. Five different load shedding sequences are considered and the optimization performance of load models demonstrated the comparison through MATLAB program.

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

The increased utilization of nonlinear devices is resulting in damage to power distribution infrastructure by introducing harmonics into power system networks, which in turn causes distortion in voltage and current signals. A novel solution called Shunt Active Power Filter (SAPF) has been developed to address this issue using power electronics. This study aims to provide a method that is efficient and cost-effective for lowering harmonics and improving power quality in distribution infrastructure. The proposed method combines the Teaching learning-based optimization (TLBO) technique with an Artificial Neural Network Controller (TLBO-ANN) in conjunction with SAPF. The primary objective of the TLBO-ANN algorithms in SAPF is to minimise total harmonic distortion (THD) for maximum system efficiency. Initially, Gain values (Ki, Kp) for a regular Proportional-Integral controller are optimised with the Particle Swarm Optimisation (PSO) technique. Those optimized parameters obtained from the PSO-tuned PI controller serve as input and target datasets for training the ANN controller. Subsequently, the TLBO algorithm is utilized to further refine the ANN controller by finding the optimal weight and bias values. Using MATLAB/SIMULINK software, we compare the performance of the proposed algorithm to that of the PSO-tuned PI controller and traditional PI controller. The findings from the simulation suggest that a SAPF utilizing a TLBO-trained ANN controller could improve THD in the supplying current while maintaining harmonics within IEEE-519 accepting levels.

The expanding proliferation of components for engineering applications requires greater optimisation of parameters, which consequently increases the need for more efficient boring practices. The Taguchi Pareto-Box Behnken design is an effective optimisation procedure for the process parametric optimisation of the IS 2062 E250 steel plates. However, the weakness of the Taguchi method in its inability to distinguish which parameters have greater effects on the boring process needs to be further suppressed. Consequently, this study investigates the coupling of the firefly algorithm to the Taguchi-Pareto-Box Behnken design method for the processing of the IS 2062 E250 steel plates during the boring operation. Linear programmes were developed for the problem formulation with two variants of the objective function definition. In the first variant, the Box Behnken design optimized parameters and the firefly-oriented optimisation procedure was addressed to attain optimal solutions. For the ...

In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD–TLBO) was developed to optimize the boring process, which promotes surface roughness as the output. At the same time, the speed, feed, and depth of cut are taken as the inputs. The case examines experimental data from the literature on the boring of IS 2062 E250 steel plates. The proposed method draws from a recent idea on the Taguchi-Pareto-Box Behnken design method that argues for a possible relationship between the Taguchi-Pareto method and the Box Behnken design method. This idea was used as a basis for the further argument that teaching learning-based optimization has a role in the further optimization of the established TPBBD method. The optimal solutions were investigated when the objective function was generated using the Box Behnken design in a case. It was replaced with the regression method in the other case, and the python programming codes were used to ...

In this paper the modification to ‘Teaching–Learning Based Optimization (TLBO) called Modified Teaching–Learning Based Optimization (MTLBO) based on particle swarm optimization principle has been proposed. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution. The process of MTLBO is divided into two parts, the first part consists of the ‘Teacher Phase ’ means learning from the teacher and the second part consists of the ‘Learner Phase ’ means learner learns by interacting with other learner having better knowledge and from the best learner knowledge treated as team leader among all learners. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on classification using neural network in data mining.

This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

With the shortcomings on the solution given for most-recent optimization problems, decision-makers from different fields yearn the existence of tenacious breakthrough. In fact, they all shared the same obligation to optimize work efficiency, whether to minimize cost, consumption or to maximize the profit acquirement. Metaheuristic search is the more-advanced method proven to be useful for difficult optimization tasks. Moreover, development records also signalized rapid development of these algorithms, contributing several notable and powerful optimization algorithms. Among them, Symbiotic Organisms Search (SOS) received noticeable attention due to its simplicity and also its parameter-less nature. Nonetheless, several considerable issues are still challenging for further development. For instance, local optima and premature convergence issues found from any improper and inefficiency computational procedure on higher dimensional problems. Also, exploitation and exploration trade-off is another essential issue involving stability for optimal performance. In that case, this work proposed a new evolutionary approach named SOS 2.0. There are two distinct features associated with the evolution: Self-Parameter-Updating (SPU) technique and chaotic maps sequencing. Both features are integrated for a better balance of exploration and exploitation in which SPU focuses on exploration and chaotic map focuses on exploitation instead. This work also applied benchmarks function tests and engineering design optimization problem in advance for validation purpose of the performance. The experimental results showed that SOS 2.0 delivers not only better performance from its predecessor and also several recent SOS modifications which can be concluded as one successive approach for better SOS algorithm, but also enhances the computation efficiency and capability of searching optimal solution.

Effort estimation in software development (SEE) is a crucial concern within the software engineering domain, as it directly impacts cost estimation, scheduling, staffing, planning, and resource allocation accuracy. In this scientific article, the authors aim to tackle this issue by integrating machine learning (ML) techniques with metaheuristic algorithms in order to raise prediction accuracy. For this purpose, they employ a multilayer perceptron neural network (MLP) to perform the estimation for SEE. Unfortunately, the MLP network has numerous drawbacks as well, including weight dependency, rapid convergence, and accuracy limits. To address these issues, the SSA Algorithm is employed to optimize the MLP weights and biases. Simultaneously, the SSA algorithm has shortcomings in some aspects of the search mechanisms as well, such as rapid convergence and being susceptible to the local optimal trap. As a result, the genetic algorithm (GA) is utilized to address these shortcomings through fine-tuning its parameters. The main objective is to develop a robust and reliable prediction model that can handle a wide range of SEE problems. The developed techniques are tested on twelve benchmark SEE datasets to evaluate their performance. Furthermore, a comparative analysis with state-ofthe-art methods is conducted to further validate the effectiveness of the developed techniques. The findings demonstrate that the developed techniques surpass all other methods in all benchmark problems, affirming their superiority.

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved Binary Teaching-Learning Based Optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner's replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.

Effort estimation in software development (SEE) is a crucial concern within the software engineering domain, as it directly impacts cost estimation, scheduling, staffing, planning, and resource allocation accuracy. In this scientific article, the authors aim to tackle this issue by integrating machine learning (ML) techniques with metaheuristic algorithms in order to raise prediction accuracy. For this purpose, they employ a multilayer perceptron neural network (MLP) to perform the estimation for SEE. Unfortunately, the MLP network has numerous drawbacks as well, including weight dependency, rapid convergence, and accuracy limits. To address these issues, the SSA Algorithm is employed to optimize the MLP weights and biases. Simultaneously, the SSA algorithm has shortcomings in some aspects of the search mechanisms as well, such as rapid convergence and being susceptible to the local optimal trap. As a result, the genetic algorithm (GA) is utilized to address these shortcomings through fine-tuning its parameters. The main objective is to develop a robust and reliable prediction model that can handle a wide range of SEE problems. The developed techniques are tested on twelve benchmark SEE datasets to evaluate their performance. Furthermore, a comparative analysis with state-ofthe-art methods is conducted to further validate the effectiveness of the developed techniques. The findings demonstrate that the developed techniques surpass all other methods in all benchmark problems, affirming their superiority.

In this paper, an enhanced version of the Salp Swarm Algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) diversification of the SSA population referred as SSA std , (ii) SSA parameters are tuned using a self-adaptive technique based Genetic Algorithm (GA) referred as SSA GA−tuner. The novelty of developing a self-adaptive SSA is to enhance its performance through balancing search exploration and exploitation. The enhanced SSA versions are evaluated using twelve benchmark functions. The diversified population of SSA std enhances convergence behavior, and self-adaptive parameter tuning of SSA GA−tuner improves the convergence behavior as well, thus improving performance. The comparative evaluation against nine well-established methods shows the superiority of the proposed SSA versions. The enhancement amount in accuracy was between 2.97% and 99% among all versions of algorithm. In a nutshell, the proposed SSA version shows a powerful enhancement that can be applied to a wide range of optimization problems.

The modern power system networks are very complex and often consist of multiloop structures with increased penetration of renewable energy sources-based distributed generations. Directional overcurrent relays (DORs) are the key protection devices in such networks and their coordination has a profound impact on the overall protection of networks. Optimisation of DOR settings is an important concern in protection coordination in power systems. In this study, optimisation of DOR settings, namely time dial setting and pickup current (I p) setting is achieved by adopting symbiotic organism search technique which is a recently proposed proficient optimiser imitating biological give and take policy while searching for an optimum. Computational ability of the technique to coordinate DORs is validated in IEEE 6-bus and WSCC 9-bus test systems. Results show that the new technique causes for notable reduction in relays' operating time, with maintaining reliable coordination margin for each primary/backup relay pair, in comparison with other techniques. Also, computation time to find optimum solution is less using the new method.

In this paper, the technique of restoring the pattern even after the element failure is demonstrated in linear arrays (LA). The process involves in determining the amplitude excitation coefficients of each element in the linear array using grasshopper algorithm (GHA). Linear array with 20 elements is considered for the implementation, while the analysis is carried out using the radiation pattern in terms of side lobe level. Two cases of element failure are considered. In the first case, second element failure is inflicted, while in the second case, the same is repeated with 30-element linear array. The simulation is carried out using MATLAB, and the results are analyzed using the corresponding radiation pattern plots and convergence plots.

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved Binary Teaching-Learning Based Optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner's replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.

Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. This paper tries to improve the structure of basic SSA to enhance solution accuracy, reliability and convergence speed. A new control parameter, inertia weight, is added to adjust the present best solution. The new method known as improved salp swarm algorithm (ISSA) is tested in feature selection task. The ISSA algorithm is consolidated with the Knearest neighbor classier for feature selection in which twenty-three UCI datasets are utilized to assess the performance of ISSA algorithm. The ISSA is compared with the basic SSA and four other swarm methods. The results demonstrated that the proposed method produced superior results than the other optimizers in terms of classification accuracy and feature reduction.

In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD-TLBO) was developed to optimize the boring process, which promotes surface roughness as the output. At the same time, the speed, feed, and depth of cut are taken as the inputs. The case examines experimental data from the literature on the boring of IS 2062 E250 steel plates. The proposed method draws from a recent idea on the Taguchi-Pareto-Box Behnken design method that argues for a possible relationship between the Taguchi-Pareto method and the Box Behnken design method. This idea was used as a basis for the further argument that teaching learning-based optimization has a role in the further optimization of the established TPBBD method. The optimal solutions were investigated when the objective function was generated using the Box Behnken design in a case. It was replaced with the regression method in the other case, and the python programming codes were used to execute the computations. Then the optimal solutions concerning the parameters of speed, feed rate, depth of cut, and nose radius were evaluated. With the Box Behnken as the objective function for the TLBO method, convergence was reached at 50 iterations with a class population of 5. The optimal parametric solutions are 800 rpm of speed, 0.06 min/min of feed rate, 1 min for depth of cut, and 0 min for nose radius. On the use of the regression method for the objective function, while the TLBO method was deployed, convergence was experienced after 50 iterations with a class population of 200 students. The optimal parametric solution is 1135rpm of speed, 0.06 min/min of feed rate, 1024 min of the depth of cut, and 0.61 min of nose radius. The speed, depth of cut, and nose radius showed higher values, indicating the use of more energy resources to accomplish the optimal goals using the regression method-based objective function. Therefore, the proposed method constitutes a promising route to optimize further the results of the Taguchi-Pareto-Box Behnken design for boring operation improvement.

The teaching-learning optimization (TLBO) technique is applied to unbraced steel frames with semi-rigid beam-to-column connections and semi-rigid column-to-base connections. The algorithm for design produces optimized steel frames by choosing appropriate sections from a set of standard steel sections. These standard steel sections, including wide-flange profiles (W), are provided by the American Institute of Steel Construction (AISC). The first example is subjected to the displacement, inter-story drift, and stress constraints of AISC-Load and Resistance Factor Design (LRFD) regulations. In addition, the displacement and stress limitations of the AISC-Allowable Stress Design (ASD) standard and the geometric (size) requirements are implemented in the last two examples. In finite element analysis, optimal designs of three alternative unbraced frames are performed with and without semi-rigid beam-to-column and column-to-base plate connections. All optimization techniques are encoded in the high-level programming language C# version 17.4.4 to integrate with SAP2000 version 22's Open Application Programming Interface (OAPI). The analysis results indicate that the rigidity of connections is determining parameter in the weight optimization of unbraced steel frames.

The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.

The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods.

A novel waverider design methodology is proposed with a variable shock angle based on the osculating cones' theory. The expected shape with special requirements can be generated by arranging the distribution of shock angle using the new method. Contrast investigation is conducted on three waveriders, which are designed with constant, decreasing and increasing shock angle distribution starting from symmetry plane at the average shock angle of 12 •. The feasibility of the proposed approach is validated by the computational fluid dynamics (CFD) simulation. The results show that the distribution of shock angle has a significant influence on the lift-to-drag ratio and volumetric efficiency. Waverider with decreasing shock angle owns the greater volume and volumetric efficiency but with weak aerodynamic performance. The waverider with increasing shock angle has a higher lift-to-drag ratio and enhanced the static stability but with lower volumetric efficiency. The performance of waveriders with variable shock angle under off-design and blunted conditions is also investigated, which reveals a great overall aerodynamic performance and good robustness. INDEX TERMS Hypersonic waverider, variable shock angle, lift-to-drag ratio, off-design condition, bluntness.

In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. It analyzes and present strong rules discovered in databases using different measures of interestingness. The process of discovering interesting and unexpected rules from large data sets is known as association rule mining. This refers to a very general model that allows relationships to be found between items of a database. An association rule is an implication or ifthen-rule which is supported by data. The association rules problem was first formulated in and was called the marketbasket problem. In his paper trying to hybrid the algorithm, Genetic algorithm (GA) and Particle Swarm Optimization (PSO) is used to improve the fitness value of items by using the dataset. Keywords—GA, PSO, Association Rule, Market basket, Data mining.

In this paper the modification to ‘Teaching–Learning Based Optimization (TLBO) called Modified Teaching–Learning Based Optimization (MTLBO) based on particle swarm optimization principle has been proposed. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution. The process of MTLBO is divided into two parts, the first part consists of the ‘Teacher Phase ’ means learning from the teacher and the second part consists of the ‘Learner Phase ’ means learner learns by interacting with other learner having better knowledge and from the best learner knowledge treated as team leader among all learners. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on classification using neural network in data mining.