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您的检索式：作者名="Yuetang Bian"
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| 1 | A New Cockroach Colony Optimization Algorithm for Global Numerical Optimization显示文摘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. | CHENG Le HAN Lixin ZENG Xiaoqin BIAN Yuetang | 2017 | Chinese Journal of Electronics2017，26，1： | 3 |
| 2 | Adaptive Cockroach Colony Optimization for Rod-Like Robot Navigation显示文摘 | Le Cheng Lixin Han Xiaoqin Zeng Yuetang Bian Hong Yan | 2015 | Journal of Bionic Engineering2015，12，2： | 2 |
| 3 | A Bionic Optimization Technique with Cockroach Biological Behavior显示文摘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-ofthe-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. | CHENG Le CHANG Lyu SONG Yanhong WANG Haibo XU Yihan BIAN Yuetang | 2021 | Chinese Journal of Electronics2021，30，4： | 1 |
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