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Analysis and Implementation of Artificial Bee Colony Optimization in Constrained Optimization Problems
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Author(s): Soumya Sahoo (C. V. Raman College of Engineering, India), Sushruta Mishra (C. V. Raman College of Engineering, India), Brojo Kishore Kishore Mishra (C. V. Raman College of Engineering, India)and Monalisa Mishra (C. V. Raman College of Engineering, India)
Copyright: 2018
Pages: 20
Source title:
Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
Source Author(s)/Editor(s): Sujata Dash (North Orissa University, India), B.K. Tripathy (VIT University, India)and Atta ur Rahman (University of Dammam, Saudi Arabia)
DOI: 10.4018/978-1-5225-2857-9.ch021
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Abstract
The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. Swarm Intelligence appears in biological swarms of certain insect species. It gives rise to complex and often intelligent behavior through complex interaction of thousands of autonomous swarm members. In this chapter, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems.
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