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black hole algorithm for determining model parameter in self-potential data

JOURNAL OF APPLIED GEOPHYSICS 2018年 148卷 189-200页

作者： Sungkono Warnana, Dwa Desa Inst Teknol Sepuluh Nopember Dept Phys Kampus ITS Sukolilo Surabaya 60111 Indonesia Inst Teknol Sepuluh Nopember Dept Geophys Engn Kampus ITS Sukolilo Surabaya 60111 Indonesia

Analysis of self-potential (SP) data is increasingly popular in geophysical method due to its relevance in many cases. However, the inversion of SP data is often highly nonlinear. Consequently, local search algorithms commonly based on gradient approaches have often failed to find the global optimum solution in nonlinear problems. black hole algorithm (BHA) was proposed as a solution to such problems. As the name suggests, the algorithm was constructed based on the black hole phenomena. This paper investigates the application of BHA to solve inversions of field and synthetic self-potential (SP) data. The inversion results show that BHA accurately determines model parameters and model uncertainty. This indicates that BHA is highly potential as an innovative approach for SP data inversion. (C) 2017 Elsevier B.V. All rights reserved.

关键词： black hole algorithm Estimation model Model uncertainty Self-potential data

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black hole algorithm: A comprehensive survey

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APPLIED INTELLIGENCE 2022年第10期52卷 11892-11915页

作者： Abualigah, Laith Abd Elaziz, Mohamed Sumari, Putra Khasawneh, Ahmad M. Alshinwan, Mohammad Mirjalili, Seyedali Shehab, Mohammad Abuaddous, Hayfa Y. Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Galala Univ Dept Artificial Intelligence Sci & Engn Suez Egypt Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea World Islamic Sci & Educ Univ Informat Technol Amman Jordan Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This paper provides an in-depth literature review of the black hole algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applications of BHA in engineering problems, clustering, task scheduling, image processing, etc. have been thoroughly reviewed as well. This review article sheds lights on the pros and cons of this algorithm and enables finding a right variant of this algorithm for a certain application area. The paper concludes with an in-depth future direction.

关键词： black hole algorithm Optimization algorithms Meta-heuristics Real-word problems Optimization problems algorithm Optimization

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Hybrid Firefly and black hole algorithm Designed for XGBoost Tuning Problem: An Application for Intrusion Detection

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IEEE ACCESS 2023年 11卷 28551-28564页

作者： Yong, Xin Gao, Yuelin North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Key Lab Intelligent Informat & Data P Yinchuan 750021 Ningxia Peoples R China

Computer networks have touched every aspect of human life, it cannot be overstated that cyber security is of great importance and significance. Intrusion detection techniques play an important role in the field of network security, but it also faces significant challenges. In this paper, we propose a Hybrid Firefly and black hole algorithm (HFBHA) for parameter tuning of the XGBoost model and apply it to the study of intrusion detection systems. Firstly, the algorithm designs a double black hole mechanism by introducing the concept of the second black hole and adjusting the moving trajectory of the stars using the attraction of both black holes. Secondly, an improved initialization method of the stars is proposed, where a star that crosses the event horizon of the black hole has an opportunity to be replaced by a new star around the black hole. Finally, a combination of the firefly perturbation strategy and mutation operator is proposed to improve the global search capability of the algorithm. Both the effectiveness of the proposed method on the XBGoost parameter tuning problem and the feasibility of this strategy on intrusion detection applications are verified by comparison experiments based on the NSL-KDD dataset.

关键词： Classification algorithms Machine learning algorithms Computer networks Machine learning Intrusion detection Tuning Support vector machines black hole algorithm firefly algorithm intrusion detection XGBoost

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On the Study of Commercial Losses in Brazil: A Binary black hole algorithm for Theft Characterization

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IEEE TRANSACTIONS ON SMART GRID 2018年第2期9卷 676-683页

作者： Ramos, Caio C. O. Rodrigues, Douglas de Souza, Andre N. Papa, Joao P. Sao Paulo State Univ Dept Elect Engn BR-17033360 Bauru Brazil Univ Fed Sao Carlos Comp Sci Dept BR-13565905 Sao Carlos SP Brazil Sao Paulo State Univ Dept Comp BR-17033360 Bauru Brazil

According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called black hole algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.

关键词： Commercial losses black hole algorithm optimum-path forest

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Solving travelling salesman problem using black hole algorithm

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SOFT COMPUTING 2018年第24期22卷 8167-8175页

作者： Hatamlou, Abdolreza Islamic Azad Univ Khoy Branch Dept Comp Sci Khoy Iran

Over the last few decades, many nature-inspired algorithms have been proposed for solving complex and difficult problems. Each algorithm has its own merits and drawbacks. One of the most recent nature-inspired algorithms, which has been applied successfully in many applications, is black hole (BH) algorithm. BH algorithm is a population-based meta-heuristic algorithm that is inspired by the black hole phenomenon. It starts with a random population of solutions to the given optimization problem. The most excellent solution at each iteration which has the best fitness is chosen to be the black hole and the other form the stars. The black hole pulls the stars towards it and causes them to search the problem space for finding optimal solution. In this paper, the application of the BH algorithm on solving travelling salesman problem (TSP) is investigated. The aim of TSP is to find a tour in a set of cities in such a way, each city is visited exactly once and return to the starting city where the length of the tour is minimized. In order to evaluate the efficiency of the BH algorithm, it has been tested on several benchmark data sets and compared to other well-known algorithms. The experimental results show that the BH algorithm can find high-quality solutions compared to genetic algorithm, ant colony optimization and particle swarms optimization algorithms. Moreover, the BH algorithm is faster than other test algorithms.

关键词： black hole algorithm Travelling salesman problem Meta-heuristic algorithms

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A new hybrid image segmentation approach using clustering and black hole algorithm

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COMPUTATIONAL INTELLIGENCE 2023年第2期39卷 194-213页

作者： Dhanachandra, Nameirakpam Chanu, Y. Jina Singh, Kh. Manglem Natl Inst Technol EE Dept Imphal 795001 Manipur India Natl Inst Technol CSE Dept Imphal Manipur India

Clustering technique is used in image segmentation because of its simple and easy approach. However, the existing clustering techniques required prior information as input and the performance are entirely dependent on this prior information, which is the main drawback of the clustering approaches. Therefore, many researchers are trying to introduce a novel method with user free parameter. We proposed a clustering method, that is, independent of user parameters and later we used a region merging technique to improve the performance of the clustering output. In this article, we proposed a hybrid image segmentation method which is based on a clustering algorithm and black hole algorithm. In the clustering technique, we have used recursive density estimation technique of surrounding pixels. After clustering technique, presence of small segments may be present and it would give lower a performance of segmentation output. Therefore, a segment is merged with another segment by finding best matched segment. black hole algorithm concept has been used to define the fitness of each segment and to find the best matching segment. We have compared the proposed method with the other clustering-based segmentation methods and different evaluation indices are used to calculate the performance, and the result proved the effectiveness of the proposed algorithm.

关键词： black hole algorithm clustering image segmentation region merging

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Extreme Learning Machine Soft-Sensor Model With Different Activation Functions on Grinding Process Optimized by Improved black hole algorithm

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IEEE ACCESS 2020年 8卷 25084-25110页

作者： Xie, W. Wang, J. S. Xing, C. Guo, S. S. Guo, M. W. Zhu, L. F. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114051 Liaoning Peoples R China

Aiming at predicting the key economic and technical indicators (Granularity and Ore content)in the grinding production process, the extreme learning machine (ELM) soft-sensor model with different activation functions on grinding process optimized by improved black hole (BH) algorithm was proposed. Based on the selected auxiliary variables for the soft-sensor model of the grinding, the KPCA method is used to reduce the dimensionality of the high-dimensional data. In order to investigate the influence of different activation functions on the prediction accuracy of the ELM model, seven continuous function (Arctan, Hardim, Morlet, ReLu, Sigma, Sin and Tanh) are used as the activation function of the ELM neural network to establish soft-sensor models respectively. For the shortcomings that ELM model weights and offset values are arbitrarily given so as to result in the low prediction accuracy and low reliability, an improved BH algorithm based on the golden sine operator and the Levy flight operator (GSLBH) was used to optimize the parameters of the ELM neural network. Simulation results show that the model has better generalization and prediction accuracy, and can meet the real-time control requirements of the grinding process.

关键词： Grinding process soft-sensor ELM neural network black hole algorithm activation function

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Hybrid parallelization of the black hole algorithm for systems on chip

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INTEGRATED COMPUTER-AIDED ENGINEERING 2022年第3期29卷 297-311页

作者： Akamatu, Saulo de Lima, Denis Pereira Pedrino, Emerson Carlos Univ Fed Sao Carlos Dept Comp Sci Rodovia Washington LuisKm 235 BR-13565905 Sao Carlos SP Brazil

black hole (BH) is a bioinspired metaheuristic algorithm based on the theory of relativity in which a sufficiently compact mass can deform the space-time to form a black hole, where no particles or electromagnetic radiation can escape from it. Thus, such an approach is based on the concept of a population of individuals (stars) representing solutions for a given computational problem to be optimized. In the literature, such an approach has been used to solve clustering problems, among others, since it is parameter-free and simple to implement. In this article, due to such characteristics, a hybrid solution, in software/hardware, of parallelization of the BH algorithm is proposed, aiming at accelerating its processing in hardware through a methodology that allows any user, even non-expert, implement hardware accelerators, for optimization problems, among others, through a high level tool. A System on Chip (SoC) platform was used for this implementation, containing a Zynq chip from Xilinx, which has two ARM cores and an FPGA. The BH algorithm was implemented in software first and then in hardware for runtime comparison purposes to validate this approach. Also, in this paper, simpler and more popular optimization algorithms, such as Particle Swarm Optimization (PSO), Gravitational Search (GSA), and Big Bang - Big Crunch (BB-BC), along with simpler datasets, were used for comparison purposes, due to its ease of implementation and to keep a fairer comparison with BH as realized in other works in the literature. Therefore, the results obtained were satisfactory in terms of execution time and quality, with an average speedup of 25 times compared to the same implementation in software. In the future, it is intended to use this procedure to implement more recent clustering and optimization algorithms with larger datasets as well.

关键词： black hole algorithm SoC hybrid hardware clustering

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A Novel Hybrid Clustering Approach Based on black hole algorithm for Document Clustering

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IEEE ACCESS 2022年 10卷 97310-97326页

作者： Malik, Fazila Khan, Salabat Rizwan, Atif Atteia, Ghada Samee, Nagwan Abdel COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Jeju Natl Univ Dept Comp Engn Jeju Si 63243 Jeju Special Se South Korea Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia Misr Univ Sci & Technol Comp Engn Dept Giza 12511 Egypt

In information retrieval and text mining, document clustering is a big challenge because the amount of document collection has been increasing, day by day. The problem of clustering is NP-hard, use of meta-heuristic algorithms to solve these problems could be an effective method. When the solution space is large, traditional methods are unable to find a solution in a reasonable amount of time. K-means is a heuristic clustering algorithm, two main issues with heuristic algorithms are the early convergence and trapping in local optima. Moreover, finding the right number of clusters is one of the main drawbacks of the k-means algorithm. The correct value of k is always confusing, different researchers used different methods to solve this problem. To overcome these mentioned problems, this study presents a novel Hybrid approach for document clustering. One of the challenges in existing BH algorithm is the input data type. Recently, the algorithm was only accepting textual data. Another flaw in the existing model is that it doesn't choose how many clusters k to form automatically, and the centroids are chosen at random in it. In this paper, we have constructed a Hybrid cluster identification approach which consists of the Elbow method and Silhouette score for cluster k identification. This paper mainly offers three novel combination of model to represent text documents, namely i) K-mean++ - BH + TF-IDF with fix k ii) K-mean++ - BH + W2V with fix k iii) Hybrid black hole with automated k. The proposed improvements have validated on the document clustering problem. Cluster analysis based on two evaluation measures, external (Purity) and internal measures (Silhouette score) are used to report the findings. Experiments have been carried out on the four al-phanumeric datasets (Doc50, Reuters, WebKB and News20) as well as on two numeric datasets (Iris and Wine) respectively. The complete result analysis is reported in detail with respect to each research contribution to compar

关键词： Clustering algorithms Heuristic algorithms Clustering methods Text analysis Data mining Task analysis Document clustering black hole algorithm k-mean data mining comparative analysis

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Study on stability of high embankment slope based on black hole algorithm

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ENVIRONMENTAL EARTH SCIENCES 2016年第20期75卷 1381页

作者： Gao, Wei Wang, Xu Dai, Shuang Chen, Dongliang Hohai Univ Coll Civil & Transportat Engn Minist Educ Geomech & Embankment Engn Key Lab 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China

The fill is one important engineering measure to construct civil engineering, such as highway, airport, etc. However, the slope instability of embankment is very complex, which may develop locally, near the facing, within the embankment, or through the foundation soil as local, surficial, general, or deep-seated failure. To analyze the stability of one high embankment slope for one airport in the plateau loess area, whose height is about 73 m, one new method based on Spencer method and new black hole algorithm has been proposed. Using this new method, the stability of the high embankment slope has been analyzed. And the computing effect and computing efficiency of the new method have been compared with some other methods proposed in previous studies. The results show that, for its small slope angle, which is about 20 degrees, the stability of this slope can be guaranteed, and the computing effect and computing efficiency of the new method are all very good. Moreover, the effect of main engineering measures on the stability of high embankment slope has been investigated comprehensively. The results show that, to improve the slope stability, it is a very effective method to compact the filling soil, and enlargement of the slope angle can reduce the slope stability very seriously.

关键词： High embankment slope Stability black hole algorithm Compaction technology Slope angle

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