Abstract
Swarm-based metaheuristics, inspired from intelligent social behaviors in nature, have achieved wider acceptance among researchers as compared to other population-based methods. The success of any swarm-based algorithm highly depends upon the mechanism of social interaction which maintains the balance between exploration and exploitation. This research examines these two significant cornerstones of top five swarm-based metaheuristics using diversity measurement. The results show that ACO and FA maintained balance between exploration and exploitation throughout iterations thus achieved better results as compared to counterparts taken in this study.
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The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia for supporting this research under Postgraduate Incentive Research Grant, Vote No.U560.
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Salleh, M.N.M. et al. (2018). Exploration and Exploitation Measurement in Swarm-Based Metaheuristic Algorithms: An Empirical Analysis. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_3
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