Abstract
Solving optimization problems can be achieved by many optimization algorithms. Swarm algorithms are part of these optimizations algorithms which based on community-based thinking. Bio-inspired algorithms are these algorithms that are artificially inspired from natural biological systems. Artificial Bee colony algorithm is a modern swarm intelligence algorithm inspired by real bees foraging behavior, and real bees’ community communication techniques. This chapter discusses Artificial bee colony algorithm (ABC) and other algorithms that are driven from it such as “Adaptive Artificial Bee Colony” (AABC), “Fast mutation artificial bee colony” (FMABC), and “Integrated algorithm based on ABC and PSO” (IABAP). Comparisons between these algorithms and previous experiments results are mentioned.
The chapter presents some case studies of ABC like traveling salesman problem, job scheduling problems, and software testing. The study discusses the conceptual modeling of ABC in these case studies.
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Rekaby, A. (2014). Different Artificial Bee Colony Algorithms and Relevant Case Studies. In: Chen, L., Kapoor, S., Bhatia, R. (eds) Intelligent Systems for Science and Information. Studies in Computational Intelligence, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-04702-7_6
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DOI: https://doi.org/10.1007/978-3-319-04702-7_6
Publisher Name: Springer, Cham
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