Volume 30 Issue 4
Jul.  2021
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CHENG Le, CHANG Lyu, SONG Yanhong, et al., “A Bionic Optimization Technique with Cockroach Biological Behavior,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 644-651, 2021, doi: 10.1049/cje.2021.05.006
Citation: CHENG Le, CHANG Lyu, SONG Yanhong, et al., “A Bionic Optimization Technique with Cockroach Biological Behavior,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 644-651, 2021, doi: 10.1049/cje.2021.05.006

A Bionic Optimization Technique with Cockroach Biological Behavior

doi: 10.1049/cje.2021.05.006
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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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  • K. Behrooz, N. T. Trung, T. T. Tam, et al., "Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO:A novel hybrid intelligent method", Defence Technology, Vol.17, No.1, pp.85-99, 2021.
    X. Y. Xia and Y. R. Zhou, "Performance analysis of ACO on the quadratic assignment problem", Chinese Journal of Electronics, Vol.27, No.1, pp.26-34, 2018.
    Z. H. Ding, Z. R. Lu and J. K. Liu, "Parameters identification of chaotic systems based on artificial bee colony algorithm combined with cuckoo search strategy", Science China (Technological Sciences), Vol.61, No.3, pp.417-426, 2018.
    R. Li, J. B. Yang, X. G. Tuo, et al., "Unfolding neutron spectra from water-pumping-injection multilayered concentric sphere neutron spectrometer using self-adaptive differential evolution algorithm", Nuclear Science and Techniques, Vol.32, No.3, pp.43-53, 2021.
    A. Liu, X. D. Deng, L. Ren, et al., "An inverse power generation mechanism based fruit fly algorithm for function optimization", Journal of Systems Science and Complexity, Vol.32, No.2, pp.634-656, 2019.
    L. Cheng, "New bionic algorithm:Cockroach swarm optimization", Computer Engineering and Applications, Vol.44, No.34, pp.44-66, 2008. (in Chinese)
    Z. H. Chen and H. Y. Tang, "Cockroach swarm optimization", 2010 IEEE International Conference on Computer Engineering and Technology, Chengdu, China, pp.653-655, 2010.
    Q. Yuan and W. M. Huang, "Dynamic fusion of artificial fish-swarm algorithm and cockroach swarm optimization with differential evolution mutation and its application in grid task scheduling", Computer Applications and Software, Vol.29, No.5, pp.175-177, 2012. (in Chinese)
    I. C. Obagbuwa, A. O. Adewumi and A. A. Adebiyi, "Stochastic constriction cockroach swarm optimization multidimensional space function problems for multidimensional space function problems", Mathematical Problems in Engineering, Vol.2014, pp.1-12, 2014.
    T. C. Havens, C. J. Spain, N. G. Salmon, et al., "Roach infestation optimization", 2008 IEEE Swarm Intelligence Symposium, St. Louis, MO, USA, pp.1-7, 2008.
    L. Cheng, L. X. Han and X. Q. Zeng, "Adaptive cockroach colony optimization for rod-like robot navigation",Journal of Bionic Engineering, Vol.12, No.2, pp.324-337, 2015.
    H. C. Tsai, "Roach infestation optimization with friendship centers", Engineering Applications of Artificial Intelligence, Vol.39, pp.109-119, 2015.
    I. C. Obagbuwa and A. P. Abidoye, "Adaptive cockroach swarm algorithm", International Conference on Numerical Analysis and Applied Mathematics, Rhodes, GREECE, pp.1-7, 2017
    L. Cheng, L. X. Han and X. Q. Zeng, "A new cockroach colony optimization algorithm for global numerical optimization", Chinese Journal of Electronics. Vol.26, No.1, pp.73-79, 2017.
    R. Jeanson, C. Rivault, J. Deneubourg, et al., "Selforganized aggregation in cockroaches", Animal Behavior, Vol.69, No.1, pp.169-180, 2005.
    J. Halloy, G. Sempo, G. Caprari, et al., "Social integration of robots into groups of cockroaches to control self-organizined choices", Science, Vol.318, No.16, pp.1155-1158, 2007.
    J. Ame, J. Halloy, C. Rivault, et al., "Collegial decision making based on social amplification leads to optimal group formation", Proceedings of the National Academy of Sciences, Vol.103, No.15, pp.5835-5840, 2006.
    K. Tang, X. Yao, P. N. Suganthan, et al., "Benchmark functions for the CEC'2008 special session and competition on large scale global optimization", IEEE Congress on Evolutionary Computation, Hefei, Anhui, China, pp.1-18, 2007.
    S. Garnier, C. Jost, J. Raphal, et al., "Collective decision-making by a group of cockroach-like robots", 2005 Swarm Intelligence Symposium, Pasadena, California, pp.233-240, 2005.
    M. Li, J. Kou and D. Lin, The Basic Theory and Application of Genetic Algorithm, Science Press, Beijing, China, pp.16-342003.
    T. Lu, Random Functional Analysis and Applications, Qingdao Ocean University Press, Qingdao,China, pp.35-46,1990.
    N. Mladenovi´c, J. Petrovic and V. Kovacevic-Vujicic, et al.,"Solving spread-spectrum radar polyphase code design problem by tabu search and variable neighborhood search", European Journal of Operational Research, Vol.151, No.2, pp.389-399,2003.
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