基于RNA遗传操作的改进蝙蝠算法
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摘要
蝙蝠算法作为一种新型的元启发式算法,具有优越的寻优能力和广泛的应用空间,同时也存在着收敛速度和精度的制约问题及个体之间欠缺交互等问题,针对这些不足,引入了RNA遗传算法增强个体之间的交流,通过信息的交叉和变异等变化措施,加快了算法的搜索能力,提高了搜索精度.通过测试函数验证了改进后的算法具有较好的收敛精度、可靠性和稳定性,大大提升了蝙蝠算法的寻优能力.
As a new metaheuristic algorithm,the bat algorithm has excellent search capability and can be applied to a variety of scenarios. However,the bat algorithm has problems with regard to its convergence rate and precision and the lack of interaction between individuals. In response to these deficiencies,the RNA genetic algorithm was introduced to enhance the interaction between individuals. Through the change of information,such as crossover and mutation,the search speed and precision of the algorithm can be improved. The test functions proved that the improved algorithm has good robustness,reliability and stability,which considerably improve the search capability of the bat algorithm.
As a new metaheuristic algorithm,the bat algorithm has excellent search capability and can be applied to a variety of scenarios. However,the bat algorithm has problems with regard to its convergence rate and precision and the lack of interaction between individuals. In response to these deficiencies,the RNA genetic algorithm was introduced to enhance the interaction between individuals. Through the change of information,such as crossover and mutation,the search speed and precision of the algorithm can be improved. The test functions proved that the improved algorithm has good robustness,reliability and stability,which considerably improve the search capability of the bat algorithm.
引文
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[3]Chakri A,Khelif R.New directional bat algorithm for continuous optimization problems[J].Expert Systems with Applications,2017,69:159-175.
[4]Ahmadianfar I,IAdib A.Optimizing multireservoir operation:Hybrid of bat algorithm and differential evolution[J].Journal of Water Resoures Planning and Management,2016,142(2):25-27.
[5]Chen Meiwen,Zhong Yiwen,Wang Lijin,et al.Bat algorithm with one-dimension learning[J].Journal of Chinese Computer Systems,2015,36(11):2614-2616.
[6]吴华锋,陈信强,毛奇凰,等.基于自然选择策略的蚁群算法求解TSP问题[J].通信学报,2013,34(4):166-170.Wu Huafeng,Chen Xinqiang,Mao Qihuang,et al.Natural selection of ant colony algorithm for solving TSPproblems[J].Journal of Communication Strategy,2013,34(4):166-170(in Chinese).
[7]秦全德,程适,李丽,等.人工蜂群算法研究综述[J].智能系统学报,2014,9(2):127-135.Qin Quande,Cheng Shi,Li Li,et al.An overview of artificial bee colony algorithms[J].Journal of Intelligent Systems,2014,9(2):127-135(in Chinese).
[8]Yang X S.A new meta heuristic bat-inspired algorithm[J].Nature Inspired Cooperative Strategies for Optimization,2010:65-74.
[9]盛孟龙,贺兴时,丁文静.蝙蝠算法的全局收敛性分析[J].纺织高校基础科学学报,2013,26(4):543-547.Sheng Menglong,He Xingshi,Ding Wenjing.Analysis of bat algorithm’s global convergence[J].Basic Sciences Journal of Textile Universities,2013,26(4):543-547(in Chinese).
[10]李雅梅,曹益华.基于Powell机制的改进蝙蝠算法[J].微电子学与计算机,2015,32(3):73-80.Li Yamei,Cao Yihua.An improved bat algorithm based on powell mechanism[J].Microelectronics&Computer,2015,32(3):73-76(in Chinese).
[11]屈迟文,傅彦铭,侯勇顺.融合入侵杂草算子的蝙蝠算法[J].计算机应用与软件,2015,32(4):243-246.Qu Chiwen,Fu Yanming,Hou Yongshun.Bat algorithm fused with invasive weed operator[J].Computer Applications and Software,2015,32(4):243-246(in Chinese).
[12]尚俊娜,刘春菊,岳克强,等.具有自学习能力的变异蝙蝠优化算法及性能仿真[J].系统仿真学报,2017,29(2):301-308.Shang Junna,Liu Chunju,Yue Keqiang,et al.Variation bat algorithm with self-learning capability and its property analysis[J].Journal of System Simulation,2017,29(2):301-308(in Chinese).
[13]Tao Jili,Wang Ning.DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes[J].Computers and Chemical Engineering,2017,31(12):1602-1618.
[14]Wang Kangtai,Wang Ning.A novel RNA genetic algorithm for parameter estimation of dynamic systems[J].Chemical Engineering Research and Design,2010,88(11):1485-1493.
[15]肖辉辉,段艳明.基于DE算法改进的蝙蝠算法的研究及应用[J].计算机仿真学报,2014,3(1):272-301.Xiao Huihui,Duan Yanming.Research and application of improved bat algorithm based on DE algorithm[J].Journal of Computer Simulation,2014,31(1):272-301(in Chinese).
[16]罗亮,吴文峻,张飞.面向云计算数据中心的能耗建模方法[J].软件学报,2014,25(7):1371-1387.Luo Liang,Wu Wenjun,Zhang Fei.Modeling method of energy consumption for cloud computing data center[J].Software Journal,2014,25(7):1371-1387(in Chinese).
[17]肖辉辉.基于单纯形法的蝙蝠算法[J].河池学院学报,2016,36(2):60-66.Xiao Huihui.Bats algorithm based on simplex method[J].Journal of Hechi University,2016,36(2):60-66(in Chinese).
[18]岳伟娜,马吉明,苏日建,等.基于反向学习机制的蝙蝠算法[J].湖北民族学院学报:自然科学版,2016,34(3):251-255.Yue Weina,Ma Jiming,Su Rijian,et al.Reverse learning mechanism based on bat algorithm[J].Journal of Hubei Institute for Nationalities:Natural Science Edition,2016,34(3):251-255(in Chinese).