Abstract
Artificial Bee Colony (ABC) is a recent swarm intelligence based approach to solve nonlinear and complex optimization problems. Exploration and exploitation are the two important characteristics of the swarm based optimization algorithms. Exploration capability of an algorithm is the capability of exploring the solution space to find the possible solution while exploitation capability of an algorithm is the capability of exploiting a particular region of the search space for a better solution. Usually, exploration and exploitation capabilities are contradictory in nature, i.e., a better exploration capability results a worse exploitation capability and vice versa. An economic and efficient algorithm can explore the complete solution space and shows a convergent behavior after a finite number of trials. Exploration and exploitation capabilities, are quantified using various diversity measures. In this paper, an analytical study has been carried out for various diversity measures for ABC process.
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Sharma, H., Bansal, J.C., Arya, K.V. (2013). Diversity Measures in Artificial Bee Colony. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_26
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DOI: https://doi.org/10.1007/978-81-322-1038-2_26
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