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Multi-objective Swarm Intelligence pp 105–121Cite as

Binary Ant Colony Optimization for Subset Problems

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 592))

Abstract

Many optimization problems involve selecting the best subset of solution components. Besides, many other optimization problems can be modelled as a subset problem. This chapter focuses on developing a new framework in ant colony optimization (ACO) for optimization problems that require selection rather than ordering with an application to feature selection for regression problems as a representative for subset problems. This is addressed through three steps that are: explaining the main guidelines of developing an ant algorithm, demonstrating different solution representations for subset problems using ACO algorithms, and proposing a binary ant algorithm for feature selection for regression problems.

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Authors and Affiliations

  1. Cairo University Cairo, Cairo, Egypt

    Nadia Abd-Alsabour

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  1. Nadia Abd-Alsabour
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Correspondence to Nadia Abd-Alsabour .

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Editors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  2. Department of Computer Science and Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, India

    Alok Kumar Jagadev

  3. Gandhi Institute for Technological Advancement, Bhubaneswar, India

    Mrutyunjaya Panda

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Abd-Alsabour, N. (2015). Binary Ant Colony Optimization for Subset Problems. In: Dehuri, S., Jagadev, A., Panda, M. (eds) Multi-objective Swarm Intelligence. Studies in Computational Intelligence, vol 592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46309-3_4

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  • DOI: https://doi.org/10.1007/978-3-662-46309-3_4

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  • Print ISBN: 978-3-662-46308-6

  • Online ISBN: 978-3-662-46309-3

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