Volume 30 Issue 4
Jul.  2021
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CHENG Le, CHANG Lyu, SONG Yanhong, WANG Haibo, XU Yihan, BIAN Yuetang. A Bionic Optimization Technique with Cockroach Biological Behavior[J]. Chinese Journal of Electronics, 2021, 30(4): 644-651. doi: 10.1049/cje.2021.05.006
Citation: CHENG Le, CHANG Lyu, SONG Yanhong, WANG Haibo, XU Yihan, BIAN Yuetang. A Bionic Optimization Technique with Cockroach Biological Behavior[J]. Chinese Journal of Electronics, 2021, 30(4): 644-651. doi: 10.1049/cje.2021.05.006

A Bionic Optimization Technique with Cockroach Biological Behavior

doi: 10.1049/cje.2021.05.006

This work is supported by the National Natural Science Foundation of China (No.51975239), the Ministry of Education Research of Social Sciences (No.17YJC790002), the Natural Science Foundation of Jiangsu Province (No.BK20191214), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.20KJA120001), the Qing Lan Project, the Huaian City Science and Technology Plan Project (No.HAB202070, No.HAP201904, No.HAP201909), and the Innovation Foundation of Jiangsu Vocational College of Electronics and Information (No.HXYC2019002, No.JSEIYQ2020002).

  • Received Date: 2019-10-28
    Available Online: 2021-07-19
  • Publish Date: 2021-07-05
  • Many practical engineering problems can be abstracted as corresponding function optimization problems. During the last few decades, many bionic algorithms have been proposed for this problem. However, when optimizing for large scale problems, such as 1000 dimensions, many existing search techniques may no longer perform well. Inspired by the social model of cockroaches, this paper presents a novel search technique called Cooperation cockroach colony optimization (CCCO). In the CCCO algorithm, two kinds of special biological behavior of cockroach, wall-following and nest-leaving, are simulated and the whole population is divided into wall-following and nest-leaving populations. By the collaboration of the two populations, CCCO accomplishes the computation of global optimization. The crucial parameters of CCCO are set by the self-adaptive method. Moreover, a discussion on group model design is provided in this paper. The CCCO algorithm is evaluated with shifted test functions (1000 dimensions). Three state-of-the-art cockroach-inspired algorithms are used for the comparative experiments. Furthermore, CCCO is applied to a real-world optimization problem concerning spread spectrum radar poly-phase. Experiment results show that the CCCO algorithm can be applied to optimize large-scale problems with the good performance.
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