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Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization

Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Unconstrained ABC Constrained ABC Multi-objective ABC Combinatorial ABC ABC for color map quantization 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringErciyes UniversityMelikgazi, KayseriTurkey
  2. 2.Institute of Natural and Applied ScienceErciyes UniversityMelikgazi, KayseriTurkey

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