\r耿艳香^{1, 2}，张立毅^{1, 2}，孙云山²，费 腾²，蒋师贤²，马嘉骏\r²\r
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AuthorsHTML:\r耿艳香^{1, 2}，张立毅^{1, 2}，孙云山²，费 腾²，蒋师贤²，马嘉骏\r²\r
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AuthorsListE:\rGeng Yanxiang^{1, 2}，Zhang Liyi^{1, 2}，Sun Yunshan²，Fei Teng²，Jiang Shixian²，Ma Jiajun\r²\r
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AuthorsHTMLE:\rGeng Yanxiang^{1, 2}，Zhang Liyi^{1, 2}，Sun Yunshan²，Fei Teng²，Jiang Shixian²，Ma Jiajun\r²\r
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Unit:\r\r1. 天津大学电气自动化与信息工程学院，天津 300072；\r
\r\r2. 天津商业大学信息工程学院，天津 300134\r
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Unit_EngLish:\r1. School of Electrical and Information Engineering，Tianjin University，Tianjin 300072，China；
2. School of Information Engineering，Tianjin University of Commerce，Tianjin 300134，China\r
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Abstract_Chinese:\r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.\r
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Abstract_English:\rAs 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.\r
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Keyword_Chinese:蝙蝠算法；RNA遗传；交叉；变异\r

Keywords_English:bat algorithm；RNA inheritance；crossover；variation\r


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