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Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization

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Book cover Evolutionary and Swarm Intelligence Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 779))

Abstract

This chapter presents the basis of Artificial Bee Colony (ABC) algorithm and the modifications that were incorporated to the algorithm to solve constrained, multi-objective and combinatorial type of optimization problems. In the modified ABC algorithm for constrained optimization, the greedy selection mechanism is replaced with Deb’s rules to favor the search towards feasible regions. In the ABC algorithm proposed for multi-objective optimization, a non-dominated sorting procedure is employed to rank the individuals based on Pareto-dominance rules. Combinatorial type of problems can also be efficiently solved by the ABC algorithm incorporated with a local search compatible with combinatorial type problems. In the second part of the chapter, an application of the ABC algorithm to colormap quantization is presented. Results of the ABC algorithm was compared to those of k-means, fuzzy-c-means and particle swarm optimization algorithms. It can be reported that compared to the k-means and fuzzy-c-means algorithms, the ABC algorithm has the advantage of working with multi-criterion cost functions and being more efficient compared to particle swarm optimization algorithm.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Erciyes University, 38039, Melikgazi, Kayseri, Turkey

    Bahriye Akay

  2. Institute of Natural and Applied Science, Erciyes University, 38039, Melikgazi, Kayseri, Turkey

    Kader Demir

Authors
  1. Bahriye Akay
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  2. Kader Demir
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Correspondence to Bahriye Akay .

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Editors and Affiliations

  1. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  2. ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Pramod Kumar Singh

  3. Electronics and Communication Sciences Unit (ECSU), Indian Statistical Institute, Kolkata, West Bengal, India

    Nikhil R. Pal

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Akay, B., Demir, K. (2019). Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization. In: Bansal, J., Singh, P., Pal, N. (eds) Evolutionary and Swarm Intelligence Algorithms. Studies in Computational Intelligence, vol 779. Springer, Cham. https://doi.org/10.1007/978-3-319-91341-4_3

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