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Black Hole Algorithm and Its Applications

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Computational Intelligence Applications in Modeling and Control

Part of the book series: Studies in Computational Intelligence ((SCI,volume 575))

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Abstract

Bio-inspired computation is a field of study that connects together numerous subfields of connectionism (neural network), social behavior, emergence field of artificial intelligence and machine learning algorithms for complex problem optimization. Bio-inspired computation is motivated by nature and over the last few years, it has encouraged numerous advance algorithms and set of computational tools for dealing with complex combinatorial optimization problems. Black Hole is a new bio-inspired metaheuristic approach based on observable fact of black hole phenomena. It is a population based algorithmic approach like genetic algorithm (GAs), ant colony optimization (ACO) algorithm, particle swarm optimization (PSO), firefly and other bio-inspired computation algorithms. The objective of this book chapter is to provide a comprehensive study of black hole approach and its applications in different research fields like data clustering problem, image processing, data mining, computer vision, science and engineering. This chapter provides with the stepping stone for future researches to unveil how metaheuristic and bio-inspired commutating algorithms can improve the solutions of hard or complex problem of optimization.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India

    Santosh Kumar, Deepanwita Datta & Sanjay Kumar Singh

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  1. Santosh Kumar
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  2. Deepanwita Datta
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  3. Sanjay Kumar Singh
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Correspondence to Santosh Kumar .

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Editors and Affiliations

  1. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Research and Development Centre, Vel Tech University, Chennai, India

    Sundarapandian Vaidyanathan

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Kumar, S., Datta, D., Singh, S.K. (2015). Black Hole Algorithm and Its Applications. In: Azar, A., Vaidyanathan, S. (eds) Computational Intelligence Applications in Modeling and Control. Studies in Computational Intelligence, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-11017-2_7

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