Abstract
Cat swarm optimization (CSO) is a novel metaheuristic based on swarm intelligence, presented in 2006 has demonstrated great potential generating good results and excellent performances simulating the behavior of domestic cats using two behavior: seeking and tracing mode, this mode are classified using a mixture rate (MR), this parameter finally defines the number of individuals who work by exploring and exploiting. This work presents an improvement structure of a binary cat swarm optimization using a total reboot of the population when loss diversity it is detected.
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Acknowledgements
The author Broderick Crawford is supported by grant CONICYT/FONDECYT/REGULAR/1171243 and Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455
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Crawford, B., Soto, R., Caballero, H. (2018). Solving the Set Covering Problem Using Cat Swarm Optimization Algorithm with a Variable Mixture Rate and Population Restart. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Applied Computational Intelligence and Mathematical Methods. CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-319-67621-0_14
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