Black Hole Optimization Algorithm Research Articles

Black-hole optimisation algorithm with FOPID-based automation intelligence photovoltaic system for voltage and power issues

ABSTRACT The article discusses the challenges related to voltage and power management in automated intelligent photovoltaic (PV) systems. It suggests that Fractional Order Proportional Integral Derivative (FOPID) controllers can help address these challenges effectively. The research aims to create a highly adaptable and precise automated intelligent PV system that focuses on managing voltage and power issues. It introduces a novel approach using the Black Hole Optimization (BHO) algorithm and leverages the advantages of FOPID controllers for automation. The study also considers thermal models for batteries within the automated intelligent PV system and explores different options to handle voltage and power issues stemming from PV panels. It involves fine-tuning 15 parameters using the BHO algorithm and concludes that one configuration (Instance 1) is preferable due to its superior accuracy and scalability. This choice is supported by objective validation. In essence, your abstract is about improving the performance and automation of PV systems, particularly in dealing with voltage and power challenges, through the application of FOPID controllers and the innovative BHO algorithm.

Optimizing the configuration of deep learning models for music genre classification

Music genre categorization is a fundamental use of sound processing methods in the realm of music retrieval. Typically, people are responsible for categorizing music genres. Machine learning approaches can automate this procedure. Therefore, in recent years, several approaches have been suggested to achieve this objective. Nevertheless, the given findings indicate that there is still a discrepancy between the observed results and an optimal categorization method. Hence, this paper introduces a novel approach for accurately forecasting music genres by using deep learning methodologies. The proposed approach involves preprocessing the input signals and then representing the characteristics of each signal using a combination of Mel Frequency Cepstral Coefficients (MFCC) and Short-Time Fourier Transform (STFT) features. Subsequently, a convolutional neural network (CNN) is applied to process each group of these characteristics. The proposed technique utilizes two CNN models to analyze MFCC and STFT data. Although the structure of these models is identical, the hyper-parameters of each model are individually adjusted using the black hole optimization (BHO) algorithm. Here, the optimization method fine-tunes the hyperparameters of each CNN model to minimize their training error. Ultimately, the results of two Convolutional Neural Network (CNN) models are combined to determine the music genre using a classifier based on SoftMax. The efficacy of the suggested methodology in categorizing music genres has been assessed using the GTZAN and Extended-Ballroom datasets. The experimental findings demonstrated that the suggested approach achieved classification accuracies of 95.2 % and 95.7 % in the two datasets, respectively, indicating its superiority over earlier efforts.

Evaluating teaching quality in colleges using combination of artificial neural networks (ANNs) and black hole optimization (BHO)

Automated evaluation of educational quality can be an effective measure of the efficiency of teaching methods, enabling the determination of the best teaching approach based on the educational environment conditions. Machine learning techniques have been used in various research studies as tools for automated evaluation of teaching quality. However, the low accuracy of classical learning models in quantitative quality estimation remains a challenge. To address this issue, one can utilize optimization strategies for learning models or combine multiple learning models as an ensemble system. These aspects are studied in the current paper. In the proposed method, automated evaluation of English teaching quality is performed through two phases: "identification of prominent quality evaluation indicators" and "quality estimation based on ensemble learning." In the first phase, the Black Hole Optimization (BHO) algorithm is used to determine the minimum number of indicators required for accurate quality estimation. In the second phase, the proposed method combines multiple artificial neural networks (ANNs) to predict education quality. In this ensemble model, the configuration and weight vector of each ANN are adjusted by the BHO algorithm to minimize training error. Then, an averaging strategy is employed to determine the final output of the ensemble system. The performance of the proposed method is evaluated using real English teaching data. Based on the results, the proposed method can achieve an average accuracy of 98.53 % in estimating English teaching quality, which represents a 2.19 % improvement compared to previous methods.

A distributed SDN-based intrusion detection system for IoT using optimized forests.

Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods.

Prostate Cancer Detection through MR Images based on Black Hole Optimization Algorithm

Prostate Cancer Detection through MR Images based on Black Hole Optimization Algorithm

Optimization of micro grid with distributed energy resources using physics based meta heuristic techniques

AbstractRecently, modern power systems depend heavily on MicroGrids (MGs), which can accommodate Distributed Energy Resources (DERs) economically and with high flexibility. MGs integrated with DERs can assist in enhancing energy security, significant cost savings, and reduction in emission of greenhouse gases. In this paper, the assessment of operating performance of proposed MG system with DERs is employed to investigate the multi‐objective problems of cost optimization and economic scheduling. A grid‐connected Micro‐grid (MG) combined with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is implemented to model the problem. This proposed model is considered as a test system for cost optimization and battery charging/discharging optimization. The developed framework is presented as multi‐objective function with constraints that can be tackled using an effective optimization technique. The above stochastic multi‐objective problem is optimized using various commonly used Physics based Meta‐heuristic techniques such as Simulated Annealing (SA), Harmony Search (HS), Slime Mold Algorithm (SMA), Gravitational Search Algorithm (GSA), Black Hole Optimization (BHO), Sine Cosine Algorithm (SCA), Multiverse optimization (MVO) and Lightning Search Algorithm (LSA). The assessment of the aforementioned physics‐based optimization techniques used on the proposed MG test system is compared using the results. According to the analysis, Black Hole Optimization (BHO) and Lightning Search Algorithm (LSA) both provide greater cost savings overall and for battery charging, respectively. The suggested optimization methods will take the BESS charging/discharging pattern and total cost savings into account.

Performance-based active controller design for nonlinear structures using modified black hole optimization

This paper presents a novel approach that facilitates the design of active controllers to mitigate seismically induced damage in structural systems. The proposed method is based on stochastic Modified Black Hole optimization algorithm. Two traditional controllers, namely Proportional-Integral-Derivative (PID) and Linear–Quadratic Gaussian (LQG) controllers were designed, and their performance was demonstrated on a benchmark 20-story steel-framed building. Evaluation criteria were defined to satisfy constraints on various response quantities, including drift, base shear, ductility, residual story drift, and control force. The constraint limits were defined in view of performance-based design requirements for the benchmark structure. The performance of the controllers was contrasted with that of traditional LQG, and significant reductions of all response quantities were achieved for design-level earthquakes.

Automation intelligence photovoltaic system for power and voltage issues based on Black Hole Optimization algorithm with FOPID

As voltage and power issues across automation intelligence photovoltaic system, the efficient of Fractional Order Proportional Integral Derivative (FOPID) controller can be the primary approach to preventing unstable the current and voltage sensors, and this strategy is considered as an automation intelligent computing, in providing an automated intelligent photovoltaic computing system solution to select the appropriate FOPID gains for the most cases with four cases, many challenges are bound to be faced: (1) design an automated intelligent photovoltaic system including scalability and precision toward voltage and power issues for prioritizing current and voltage sensors, and (2) technical measured and simulated including the gap between convergences and an accurate matching amongst four cases considering all FOPID gains. Based on previous studies, no study had provided a solution for automation intelligent photovoltaic system during these gains settings issues. This study aimed to propose a novel automation intelligence photovoltaic system for voltage and power issues across current and voltage sensors based on Black Hole Optimization (BHO) algorithm to provide an efficient automation intelligence system using FOPID gains. The batteries, including three thermal model for three batteries have met the importance intelligent batteries in the automated intelligence PV system, must be augmented to develop system. Four cases for voltage and power issues conclude for the three PV panels. In the first case, a new automated intelligent PVs Panels between lowest and upper bounds is generated on the basis of the automated system with select fifteen gains for tuning and different bounds for other cases acquired via intelligent BHO algorithm. The execution of automated intelligent system possesses the precision and scalability presentation within tuned BHO algorithm. The objective validation results which indicate that the case 1 is a better case.

Optimal Pole-Swapping in Bipolar DC Networks Using Discrete Metaheuristic Optimizers

Bipolar direct current (DC) networks are emerging electrical systems used to improve the distribution capabilities of monopolar DC networks. These grids work with positive, negative, and neutral poles, and they can transport two times the power when compared to monopolar DC grids. The distinctive features of bipolar DC grids include the ability to deal with bipolar loads (loads connected between the positive and negative poles) and with unbalanced load conditions, given that the loads connected to the positive and neutral poles are not necessarily equal to the negative and neutral ones. This load imbalance deteriorates voltages when compared to positive and negative poles, and it causes additional power losses in comparison with balanced operation scenarios. This research addresses the problem of pole-swapping in bipolar DC networks using combinatorial optimization methods in order to reduce the total grid power losses and improve the voltage profiles. Bipolar DC networks with a non-solidly grounded neutral wire composed of 21 and 85 nodes are considered in the numerical validations. The implemented combinatorial methods are the Chu and Beasley genetic algorithm, the sine-cosine algorithm, and the black-hole optimization algorithm. Numerical results in both test feeders demonstrate the positive effect of optimal pole-swapping in the total final power losses and the grid voltage profiles. All simulations were run in the MATLAB programming environment using the triangular-based power flow method, which is intended for radial distribution system configurations.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time.

Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction

In this paper, a new approach for hybridizing Rough Set Quick Reduct and Relative Reduct approaches with Black Hole optimization algorithm is proposed. This algorithm is inspired of black holes. A black hole is a region of spacetime where the gravitational field is so strong that nothing— not even light— that enters this region can ever escape from it. Every black hole has a mass and charge. In this Algorithm, each solution of problem is considered as a black hole and gravity force is used for global search and the electrical force is used for local search. The proposed algorithm is compared with leading algorithms such as, Rough Set Quick Reduct, Rough Set Relative Reduct, Rough Set particle swarm optimization based Quick Reduct, Rough Set based PSO Relative Reduct, Rough Set Harmony Search based Quick Reduct, and Rough Set Harmony Search based Relative Reduct.

DESIGN OF OPTIMAL INVERSE KINEMATIC SOLUTION FOR HUMANOID ROBOTIC ARMS

One of the main problems in robotics is the Inverse Kinematics (IK) problem. In this paper, three optimization algorithms are proposed to solve the IK of Humanoid Robotic Arms (HRAs). A Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), and Black Hole Optimization (BHO) algorithms are proposed in order to optimize the parameters of the proposed IK. Also, in this paper, each optimization method is applied on both right and left arms to find the desired positions and required angles with a minimum error. Denavit-Hartenberg (D-H) method is used to design and simulate the mathematical model of HRAs for both arms in which each arm has five Degree Of Freedom (DOF). The HRAs model is tested for performance by several positions to be reached by both arms in the same time to find which optimization algorithm is better. Optimal solution obtained by SSO, PSO and BHO algorithms are evaluated and listed in comparison table between them. These optimization algorithms are assessed by calculating the Computational Time (CT) and Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Calculation and simulation results showed that BHO algorithm is better than the other optimization algorithms from point of view of CT and RMSE. The worst RMSE is 0.0864 was calculated using PSO algorithm. But longer CT is 7.6521 second, which was calculated using SSO. While the best RMSE and shorter CT.are and 3.0156 second respectively were calculated by BHO algorithm. Moreover, in this paper, the Graphical User Interface (GUI) is designed and built for motional characteristics of the HRAs model in the Forward Kinematics (FK) and IK. The optimization algorithms are designed using MATLAB package facilities to simulate the HRAs model and the solution of IK problem.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

Improved Black Hole optimization algorithm for data clustering

Algorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization algorithm is a nature-inspired algorithm that belongs to the meta-heuristic category. The Black-Hole algorithm (BH) simulates the black hole phenomenon which is formed from a star with massive size and very high gravitational power. The algorithm starts with a population of a specific size of possible solutions and then gets evaluated by selecting the best one as a black hole. In the suggested modifications, we have introduced a new idea for generating the stars absorbed by the black hole. The star movement towards the black hole has also been modified to increase exploration capabilities. The modified algorithm was used to prove its effectiveness in data clustering without any prior knowledge about the nature of the provided data. Several benchmark datasets and statistical techniques have been used to evaluate the performance of suggested modification. The experiment results promised that the improved algorithm can overcome popular optimization algorithms.

Feature selection based on chaotic binary black hole algorithm for data classification

With the advance of generating high-dimensional data, feature selection is the most significant procedure to guarantee selecting the most discriminative subset of features and to improve the classification performance. As a result, a binary black hole optimization algorithm (CBBHA) has been developed by getting inspired from natural phenomena. In this paper, the most discriminating features are selected by a new chaotic binary black hole algorithm (CBBHA) where chaotic maps embedded with movement of stars in the BBHA. Ten chaotic maps are employed. Experiments on three chemical datasets show the proposed algorithm, CBBHA, has an advantage over the standard BBHA in terms of selecting relevant features with a high classification performance. Additionally the performance of CBBHA is compared with BBHA in term of the computational time efficiency which is revealing that CBBHA outperforms the BBHA.

Scheduling scientific workflows on virtual machines using a Pareto and hypervolume based black hole optimization algorithm

The problem of workflow scheduling on virtual machines in a cloud environment is to find the near optimal permutation of the assignment of independent computational jobs on a set of machines that satisfies conflicting objectives. This problem is known to be an NP-hard problem. Evolutionary multiobjective algorithms are optimization methods to solve such problems. hypervolume is one of the most important criteria that is used to both as solution evaluation and as a guidance for near-optimal selection of a set of solutions called the Pareto front. In this paper, a new hypervolume-based multiobjective algorithm is proposed for driving the Pareto front. To this end, we extend the single-objective Black hole heuristic algorithm based on the adopted $$\theta $$ -dominance relation to improving the diversity and convergence to an optimal Pareto front. The conflicting objectives are resource utilization, resource cost, and the workflow makespan (completion time). Also to presenting the appropriate scheduling algorithm, we have proven the correctness of the proposed algorithm by providing the behavioral model of the suggested system using model checking tool. For this purpose, we first introduce the behavioral model of the proposed system using Abstract state machine and extract the properties of the algorithm in the form of linear temporal logic. Then we encode the algorithm using the model checker tool Abstract state Machine Meta-model-based Language and verify the accuracy of the algorithm based on the expected properties, reachability, fairness, and deadlock-free. In order to demonstrate the effectiveness of our method we: (1) extended the WorkflowSim tools, (2) applied it to both balanced and imbalanced workflows and (3) compared results to algorithms, Strength Pareto Evolutionary Algorithm-2, Non-dominated Sorting Genetic Algorithm-2, Multi-Swarm MultiObjective Optimization, Intelligent Water Drops algorithm and Genetic Algorithm and Pareto-based Grey Wolf Optimizer. The comparisons show that by increasing the number of users requests and their correlations, the proposed algorithm can find more optimal Pareto front.

An Enhanced Version of Black Hole Algorithm via Levy Flight for Optimization and Data Clustering Problems

The processes of retrieving useful information from a dataset are an important data mining technique that is commonly applied, known as Data Clustering. Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, and data clustering problem in particular. Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems, in which it is a population-based metaheuristic that emulates the phenomenon of the black holes in the universe. In this instance, every solution in motion within the search space represents an individual star. The original BH has shown a superior performance when applied on a benchmark dataset, but it lacks exploration capabilities in some datasets. Addressing the exploration issue, this paper introduces the levy flight into BH algorithm to result in a novel data clustering method “Levy Flight Black Hole (LBH)”, which was then presented accordingly. In LBH, the movement of each star depends mainly on the step size generated by the Levy distribution. Therefore, the star explores an area far from the current black hole when the value step size is big, and vice versa. The performance of LBH in terms of finding the best solutions, prevent getting stuck in local optimum, and the convergence rate has been evaluated based on several unimodal and multimodal numerical optimization problems. Additionally, LBH is then tested using six real datasets available from UCI machine learning laboratory. The experimental outcomes obtained indicated the designed algorithm's suitability for data clustering, displaying effectiveness and robustness.

Load Frequency Control in Microgrids Based on a Stochastic Noninteger Controller

In this paper, an adaptive multiobjective fractional-order fuzzy proportional–integral–derivative controller is proposed for the load frequency control (LFC) of islanded microgrids (MGs), while benefiting from the assets of electric vehicles (EVs) in this respect. Although the use of battery energy storage systems (BESS) can solve the unbalance effects between the load and supply of an isolated MG, their high cost and tendency toward degradation are restrictive factors, which call for the use of alternative power balancing options. In recent years, the concept of utilizing the BESSs of EVs, also known as vehicle-to-grid (V2G) concept, for frequency support of MGs has attracted a lot of attention. In order to allow the V2G controller operate optimally under a wide range of operation conditions caused by the intermittent behavior of renewable energy resources, a new multiobjective fractional-order control strategy for the EVs in V2G scenarios is proposed in this paper. Moreover, since the performance of the controller depends on its parameters, optimization of these parameters can play a significant role in promoting the output performance of the LFC control; hence, a modified black hole optimization algorithm is utilized for the adaptive tuning of the noninteger fuzzy PID controller coefficients. The performance of the proposed LFC is evaluated by using real world wind and solar radiation data. Finally, the extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller tracks frequency with lower deviation and fluctuation and is more robust in comparison with the prior-art controllers used in all the case studies.

Shipboard Microgrids: A Novel Approach to Load Frequency Control

Due to the fast development of renewable energy systems and the severe limitations enforced by the marine pollution protocol, the utilizing of wind turbines (WTs), solar generation, sea wave energy (SWE), and energy storage systems (ESS) in marine vessel power systems have been attracting a lot of attention. Hence, a marine vessel power system with photovoltaic, WT, SWE, and ESS can be considered as a specific mobile islanded microgrid. Consequently, the main target of this paper is to design a new optimal fractional order fuzzy PD+I load frequency controller (LFC) for islanded microgrids in a ship power system. Since the performance of the controller depends on its parameters, the optimization of these coefficients can play a significant role in improving the output performance of the LFC control. Accordingly, a modified black hole optimization algorithm is utilized for the adaptive tuning of the coefficients of noninteger fuzzy PD+I controller. The performance of the shipboard microgrid is evaluated by utilizing real-world wind power fluctuation and solar radiation data. Finally, the extensive studies and hardware-in-the-loop simulations are applied to prove that the proposed controller can track the reference frequency with lower deviation as well as it is more robust in comparison with the prior-art controllers utilized in the case studies.

RETRACTED ARTICLE: Optimal non-integer PID controller for a class of nonlinear systems: multi-objective modified black hole optimization algorithm

RETRACTED ARTICLE: Optimal non-integer PID controller for a class of nonlinear systems: multi-objective modified black hole optimization algorithm