CHENG Le, HAN Lixin, ZENG Xiaoqin, et al., “A New Cockroach Colony Optimization Algorithm for Global Numerical Optimization,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 73-79, 2017, doi: 10.1049/cje.2016.06.030
Citation: CHENG Le, HAN Lixin, ZENG Xiaoqin, et al., “A New Cockroach Colony Optimization Algorithm for Global Numerical Optimization,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 73-79, 2017, doi: 10.1049/cje.2016.06.030

A New Cockroach Colony Optimization Algorithm for Global Numerical Optimization

doi: 10.1049/cje.2016.06.030
Funds:  This work is supported by grants from City University of Hong Kong (No.7004051), the National Natural Science Foundation of China (No.60571048, No.60873264, No.60971088), the Qing Lan Project, the Natural Science Foundation of Education Bureau of Jiangsu Province (No.16KJB520049), and Innovation Foundation of Huaian College of Information Technology (No.hxyc2015001).
  • Received Date: 2014-10-16
  • Rev Recd Date: 2015-04-06
  • Publish Date: 2017-01-10
  • Inspired by the behavior of cockroaches in nature, this paper presents a new optimization algorithm called Cockroach colony optimization (CCO). In the CCO algorithm, nests of cockroaches are placed at the "corner" of the search space. The current best solution to the optimization problem called food can split some of the search targets by applying the logistic multi-peak map and the margin control strategies. By using a particular search scheme, the individual cockroaches can accomplish a highly efficient global and local search in each crawling process from a nest to a search target. The paper provides a formal convergence proof for the CCO algorithm. Experiment results show that the CCO algorithm can be applied to solve global numerical optimization problems with the characteristics of quick convergence and high precision.
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