Black Hole Algorithm Research Articles

Settlement-based cost optimization of geogrid-reinforced pile-supported foundation

Cost optimization of Geogrid-Reinforced Pile-Supported Foundation (GRPSF) requires the minimum construction cost among all design alternatives within both ultimate limit state (ULS) and serviceability limit state (SLS) criteria. Usually, the optimization is conducted by selecting a limited number of design alternatives based on experience and then comparing them, which often does not lead to the real optimal design. This paper presents a novel optimization framework to systematically determine the design parameters to achieve the minimum construction cost for GRPSF, considering both ULS and SLS constraints that are relevant to post-construction performance and constructability. This framework is a hybrid of surrogate modeling and Finite Element Method (FEM) to calculate the post-construction settlement of GRPSF and search for the optimal design. Genetic Algorithm improved Black Hole Algorithm (BH-GA) was developed to determine the optimal values of design variables, including pile length and spacing, pile cap geometry, and geogrid layers and layout. The proposed approach can quickly identify the optimal design by exhausting all possible combinations of design parameters. Two well-documented case histories of GRPSF were redesigned using this framework, which validated its applicability and effectiveness in optimizing the design of GRPSF.

Improved Black Hole Algorithm for Intelligent Traffic Navigation

Improved Black Hole Algorithm for Intelligent Traffic Navigation

Factual power loss reduction by enriched black hole algorithm

This paper presents enriched black hole algorithm (EBHA) for solving optimal reactive power problem. In this work black hole algorithm based on membrane computing is projected. In black hole algorithm evolution of the population is through pushing the candidates in the course of the most excellent candidate in iterations and black hole which swap with those in the search space. Membrane computing is also branded as P system and it has multisets of objects with evolution rules in the membrane structure. Membrane structure is alike ingrained tree of section that demarcate the areas, and root is labelled as skin. Chemical substances (multisets of objects) are there inside the section (membranes) of a cell and the chemical reactions (evolution rules) that take place within the cell. Proposed enriched black hole algorithm (EBHA) has been evaluated in IEEE 14,300 bus test system. Loss reduction achieved.

Factual power loss reduction by enriched black hole algorithm

Factual power loss reduction by enriched black hole algorithm

Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data

This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to identify a limited and stable set of discriminative genes without sacrificing classification accuracy, whereas most current methods have encountered challenges in extracting disease-related information from a vast amount of redundant genes. The proposed approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce the dimensionality of feature space and then utilizes the suggested hybrid DBH algorithm to determine a smaller set of significant genes. The proposed approach was evaluated on eight benchmark gene expression datasets, and then, was compared against the latest state-of-art techniques to demonstrate algorithm efficiency. The comparative study shows that the proposed approach achieves a significant improvement as compared with existing methods in terms of classification accuracy and the number of selected genes. Moreover, the performance of the suggested method was examined on real RNA-Seq coronavirus-related gene expression data of asthmatic patients for selecting the most significant genes in order to improve the discriminative accuracy of angiotensin-converting enzyme 2 (ACE2). ACE2, as a coronavirus receptor, is a biomarker that helps to classify infected patients from uninfected in order to identify subgroups at risk for COVID-19. The result denotes that the suggested MRMR-DBH approach represents a very promising framework for finding a new combination of most discriminative genes with high classification accuracy.

KAB: A new k-anonymity approach based on black hole algorithm

K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. Although generalization-based k-anonymity approaches can achieve the privacy protection objective, they suffer from information loss. Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. We propose, in this paper, a novel algorithm based on a simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), to address such limitations. Experiments on real data set show that data utility has been improved by our approach compared to k-anonymity, BHA-based k-anonymity and clustering-based k-anonymity approaches.

A novel operation strategy based on black hole algorithm to optimize combined cooling, heating, and power-ground source heat pump system

To improve the efficiency of a combined cooling, heating, and power-ground source heat pump system (CCHP-GSHP), ground source heat pump cooling and heating start factors are proposed. The start factors can control the GSHP cooling and heating start load rates. Moreover, they can reduce the operating time of GSHP with a low coefficient of performance (COP) and increase the operating time with a high COP during the absorption refrigeration. A following electric load with start factors (FEL-SF) is presented as a novel operation strategy for guiding the system operation, and is compared with a traditional FEL. In the FEL-SF model, the rated capacity of the power generation unit, seasonal start factors, and seasonal GSHP energy ratios are established as decision variables. The system’s optimal objective is providing an optimal comprehensive performance in regards to energy, the environment, and the economy. The black hole (BH) algorithm is used to solve the optimization problem. A hotel building is selected as an example for analysis. The research results show that the FEL-SF operation strategy has better performance (the comprehensive performance reaches 4.0%) throughout the year. The effect of the FEL-SF strategy is superior for each index in each season. In addition, the setting of the GSHP start factors is suitable for most regions with different climates.

A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

Clustering is a process of randomly selecting k-cluster centers also grouping the data around those centers. Issues of data clustering have recently received research attention and as such, a nature-based optimization algorithm called Black Hole (BH) has said to be suggested as an arrangement to data clustering issues. The BH as a metaheuristic which is elicited from public duplicates the black hole event in the universe, whereas circling arrangement in the hunt space addresses a solo star. Even though primordial BH has shown enhanced execution taking place standard datasets, it doesn't have investigation capacities yet plays out a fine local search. In this paper, another crossover metaheuristic reliant on the mix of BH algorithm as well as genetic algorithm suggested. Genetic algorithm represents its first part of the algorithm which prospects the search space and provides the initial positions for the stars. Then, the BH algorithm utilizes the search space and finds the best solution until the termination condition is reached. The proposed hybrid approach was estimated on synchronized nine popular standard functions where the outcomes indicated that the process generated enhanced outcome with regard to robustness compared to BH and the benchmarking algorithms in the study. Furthermore, it also revealed a high convergence rate which used six real datasets sourced of the UCI machine learning laboratory, indicating fine conduct of the hybrid algorithm on data clustering problems. Conclusively, the investigation showed the suitability of the suggested hybrid algorithm designed for resolving data clustering issues.

Cluster‐based data transmission scheme in wireless sensor networks using black hole and ant colony algorithms

SummarySince sensor nodes use limited battery power, sensor nodes power is considered to be a challenge and fundamental issue in wireless networks. Wireless sensor networks (WSNs) have recently used clustering‐based methods and different routing protocols in clusters which are full of sensor nodes. In this way, cluster heads (CHs), which are regarded as the selected nodes among other nodes within a given cluster, accumulate the information transmitted from their own cluster and send it to the sink. In this method, they try to control power consumption in a balanced way. In clustering method and routing among network nodes, network lifetime is enhanced which leads to achieving the best efficiency and productivity. In this paper, the optimized black hole algorithm is employed to select an optimal CH from sensor nodes. The CH selection is optimized by the free buffer of nodes, residual energy, and distance. The path between source CH and sink is identified by using ant colony optimization (ACO) algorithm. The combination of black hole algorithm and ant colony algorithm, with respect to clustering and routing, leads to the optimization of the proposed method in terms of operational criteria such as power consumption and eventually lifetime enhancement. The results obtained from simulating the proposed method in Matlab environment indicated that it has better results in comparison with other methods. The outputs of the obtained results from the proposed method indicated that it outperformed the other four comparative methods in terms of packet delivery rate and the number of transmitted packets to the CH and to the sink.

A modified unconscious search algorithm for data clustering

Clustering is a widely used data mining technique with a diverse set of applications. Since clustering is an NP-hard problem, finding high-quality solutions for large-scale clustering problems can be an arduous and computationally expensive task. Therefore, many metaheuristics are utilized to solve these problems efficiently. In this paper, a modified unconscious search (US) and its k-means hybrid for data clustering are proposed with two main modifications: (1) generating initial population by combining solutions of k-means and random solutions, (2) replacing the usual local search step of the original US by an existing Heuristic Search method. Modified US is tested on the seven following well-known benchmarks from the UCI machine learning directory: Iris, Wine, Glass, Cancer, Vowel, CMC, and Ecoli. The results are then compared against metaheuristics, such as genetic algorithm, particle swarm optimization (PSO), black hole algorithm, hybrid of PSO k-means, and accelerated chaotic PSO. The results of experiments show that, on average, the quality of best solutions obtained by the proposed methods on all seven datasets is 0.176% better than the quality of the other six algorithms applied for experimentations.

Optimal Load Shedding to Avoid Risks of Voltage Collapse Using Black Hole Algorithm

This paper proposes a novel methodology for the optimization of load shedding based on voltage-dependent load model accounting stability constraints using Black Hole (BH) optimization technique under emergency conditions. Line voltage stability index relation is derived for the selection of load bus. Load buses for load shedding are identified on the basis of large sensitivity of minimum value of line voltage stability index with respect to the system load. The load shedding has been optimized subject to stability constraints using BH algorithm. Testing of the developed algorithm has been done using IEEE 14, 25 and 118 bus power systems. The optimized results of the proposed algorithm have been compared on the basis of statistical inference with results obtained using well-established optimization techniques. Simulation results authenticate that the proposed methodology mitigates a considerable amount of load shedding and the system can be prevented from blackouts.

Training Feedforward Neural Network Using Enhanced Black Hole Algorithm: A Case Study on COVID-19 Related ACE2 Gene Expression Classification.

The aim of this paper is twofold. First, black hole algorithm (BHA) is proposed as a new training algorithm for feedforward neural networks (FNNs), since most traditional and metaheuristic algorithms for training FNNs suffer from the problem of slow coverage and getting stuck at local optima. BHA provides a reliable alternative to address these drawbacks. Second, complementary learning components and Levy flight random walk are introduced into BHA to result in a novel optimization algorithm (BHACRW) for the purpose of improving the FNNs’ accuracy by finding optimal weights and biases. Four benchmark functions are first used to evaluate BHACRW’s performance in numerical optimization problems. Later, the classification performance of the suggested models, using BHA and BHACRW for training FNN, is evaluated against seven various benchmark datasets: iris, wine, blood, liver disorders, seeds, Statlog (Heart), balance scale. Experimental result demonstrates that the BHACRW performs better in terms of mean square error (MSE) and accuracy of training FNN, compared to standard BHA and eight well-known metaheuristic algorithms: whale optimization algorithm (WOA), biogeography-based optimizer (BBO), gravitational search algorithm (GSA), genetic algorithm (GA), cuckoo search (CS), multiverse optimizer (MVO), symbiotic organisms search (SOS), and particle swarm optimization (PSO). Moreover, we examined the classification performance of the suggested approach on the angiotensin-converting enzyme 2 (ACE2) gene expression as a coronavirus receptor, which has been overexpressed in human rhinovirus-infected nasal tissue. Results demonstrate that BHACRW-FNN achieves the highest accuracy on the dataset compared to other classifiers.

Rotated Black Hole: A New Heuristic Optimization for Reducing Localization Error of WSN in 3D Terrain

Wireless sensor network (WSN) attracts the attention of more and more researchers, and it is applied in more and more environment. The localization information is one of the most important information in WSN. This paper proposed a novel algorithm called the rotated black hole (RBH) algorithm, which introduces a rotated optimal path and greatly improves the global search ability of the original black hole (BH) algorithm. Then, the novel algorithm is applied in reducing the localization error of WSN in 3D terrain. CEC 2013 test suit is used to verify the performance of the novel algorithm, and the simulation results show that the novel algorithm has better search performance than other famous intelligence computing algorithms. The localization simulation experiment results reveal that the novel algorithm also has an excellent performance in solving practical problems. WSN localization 3D terrain intelligence computing rotated the black hole algorithm.

Optimal Recharge Scheduler for Drone-to-Sensor Wireless Power Transfer

Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices. Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios.

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.

Iris Segmentation Based on Black Hole Algorithm for Biometric System

Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.

Investigating the Efficiency of Swarm Algorithms for Bridge Strengthening by Conversion to Tied-Arch: A Numerical Case Study on San Luis Bridge

One of the current problems in engineering is the collapse of bridges that cross rivers due to the scour of their piers. This can lead to collapse and thus high costs associated with reconstruction. One way of avoiding bridge collapses is the installation of an arch-shaped superstructure that goes over the bridge, which by means of hangers lifts the deck to replace the damaged piers. A problem arises when the hangers are tight since keeping the properties of the deck of the original bridge is mandatory in order to avoid serious damages to the bridge. This paper proposes the use of algorithms with great performance in the literature, which are Black Hole and cuckoo search, to find the correct order to tight the hangers as well as the magnitude that minimizes the difference in stresses along the concrete with respect to the original bridge. Finally, the results associated with various real instances are presented according to the San Luis bridge, where the proper functioning of the Black Hole Algorithm can be observed, as opposed to cuckoo search and 4 other techniques. This implies that the use of metaheuristic techniques presents acceptable results in reasonable calculation times, validating the implementation of these techniques for this problem.

A hybrid DBH-VNS for high-end equipment production scheduling with machine failures and preventive maintenance activities

The high-end equipment features with high value, complicated manufacturing process, and high status, and it thus brings a huge challenge to increase reliability, quality, and productivity during the production. In order to tackle this challenge and achieve automation, integration, and intelligence this paper proposes a hybrid metaheuristic for an integrated order scheduling and maintenance planning model with position-based processing time, parallel-batching processing, and multiple manufacturers. During the production, the continuous operation of the machine increases the probability of failure, and the repair work can eliminate the failure For each order, we derive some useful lemmas and develop an optimal algorithm to schedule jobs within it. Then, given the order assignment and sequence in the manufacturers, we propose a dynamic programing algorithm to make the decision on the maintenance planning. Subsequently, the investigated problem is proved to be NP-hard, thus, we propose a hybrid discrete black hole algorithm and variable neighborhood search (DBH-VNS) approach to solve the integrated problem. Some improvements are integrated into the proposed algorithm to obtain the competitive results, which include discrete encoding-based population updating scheme, the modified neighborhoods, and the VNS-based local search. Finally, we conduct computational experiments and the results demonstrate the effectiveness and validity of the proposed hybrid metaheuristic.

Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines

Cloud computing has drastically transformed the means of computing in past few years. Apart from numerous advantages, it suffers with a number of issues including resource under-utilization, load balancing and power consumption. The workload prediction is being widely explored to solve these issues using time series analysis regression and neural networks based models. The time series analysis based models are unable to capture the dynamics in the workload behavior whereas neural network based models offer better accuracy on the cost of high training time. This paper presents a workload prediction model based on extreme learning machines (ELM) whose learning time is very low and forecasts the workload more accurately. The performance of the model is evaluated over two real world cloud server workloads i.e. CPU and Memory demand traces of Google cluster and compared with predictive models based on state-of-art techniques including Auto Regressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Linear Regression (LR), Differential Evolution (DE), Blackhole Algorithm (BhA), and Propagation (BP). It is observed that the proposed model outperforms the state-of-art techniques by reducing the mean prediction error up to 100% and 99% on CPU and memory request traces respectively.

Determination of the friction capacity of driven piles using three sophisticated search schemes

Proper approximation of friction capacity (FC) of driven piles is a noticeable issue in geotechnical engineering. Hence, the pivotal focus of the current research is on proposing reliable predictors for evaluating this parameter. Three artificial neural networks (ANNs) improved by firefly algorithm (FA), multi-tracker optimization algorithm (MTOA), and black hole algorithm (BHA) are used to estimate the FC when it is affected by four key factors, namely pile length, pile diameter, vertical effective stress, and undrained shear strength. Checking the optimization process of different population sizes revealed that the MTOA and BHA need the population twice as many as the FA does (400 vs. 200). The results showed that all three models can properly comprehend the association of FC to the mentioned parameters. According to the values of root mean square error (RMSE) as well as the coefficient of determination (R2) obtained in the prediction phase (6.9606 and 0.8827, 8.5411 and 0.8370, 6.0454 and 0.9194, respectively, for the FA-ANN, MTOA-ANN, and BHA-ANN), the BHA-ANN is more accurate than two other algorithms, however, the MTOA-ANN gained the largest accuracy of training. The suggested models proved the high efficiency of hybrid predictors and can be potential alternatives to experimental and traditional approaches.