工程科学学报.第42卷.第12期：1674-1684.2020年12月 Chinese Journal of Engineering,Vol.42,No.12:1674-1684,December 2020 https://doi.org/10.13374/j.issn2095-9389.2020.07.26.002;http://cje.ustb.edu.cn 一种基于差分进化的正弦余弦算法 刘小娟，王联国⑧ 甘肃农业大学信息科学技术学院.兰州730070 ☒通信作者，E-mail:wanglg@gsau.edu.cn 摘要正弦余弦算法是一种新型仿自然优化算法，利用正余弦数学模型来求解优化问题.为提高正弦余弦算法的优化精度 和收敛速度，提出了一种基于差分进化的正弦余弦算法.该算法通过非线性方式调整参数提高算法的搜索能力、利用差分进 化策略平衡算法的全局探索能力及局部开发能力并加快收敛速度、通过侦察蜂策略增加种群多样性以及利用全局最优个体 变异策略增强算法的局部开发能力等优化策略来改进算法，最后通过仿真实验和结果分析证明了算法的优异性能 关键词智能优化算法：正弦余弦算法：差分进化算法：侦察蜂策略：全局最优个体变异策略 分类号TP18 A sine cosine algorithm based on differential evolution LIU Xiao-juan,WANG Lian-guo College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China Corresponding author,E-mail:wanglg@gsau.edu.cn ABSTRACT In 2016,a novel naturally simulated optimization algorithm,termed the sine cosine algorithm (SCA),was proposed by Seyedali Mirjalili from Australia.This algorithm uses the sine cosine mathematical model to solve optimization problems and has attracted extensive attention from numerous scholars and researchers at home and abroad over the last few years.However,similar to other swarm intelligence optimization algorithms,SCA has numerous shortcomings in optimizing some complex function problems.To address the defects of basic SCA,such as low optimization precision,easy dropping into the local extremum,and slow convergence rate, a sine cosine algorithm based on differential evolution (SCADE)was proposed.First,the search capabilities of the new algorithm was improved by adjusting parameter in a nonlinear manner and ensuring that each individual adopts the same parameters.andr. Then,differential evolution strategies,including crossover,variation,and selection,were adopted to fully utilize the leading role of the globally optimal individual and information of other individuals in the population.This approach balanced the global exploration and local development abilities and accelerated the convergence rate of the algorithm.Next,using the reconnaissance bees'strategy,random initialization was performed on individuals whose fitness values showed no improvement in continuous nlim times,which increased the population diversity and improved the global exploration ability of the algorithm.Moreover,the globally optimal individual variation strategy was used to conduct a fine search near the optimal solution,which enhanced the local development ability and optimization accuracy of the algorithm.Based on the above optimization strategies,the algorithm exhibits improvements and its excellent performance is validated by the result analysis of a simulation experiment. KEY WORDS intelligent optimization algorithm;sine cosine algorithm;differential evolution algorithm;reconnaissance bees' strategy:globally optimal individual variation strategy 收稿日期：2020-07-26 基金项目：甘肃农业大学科技创新基金资助项目(GAU-XKJS-2018-251):甘肃省教育信息化建设专项任务资助项目(2011-02)：国家自然科 学基金资助项目(61751313)一种基于差分进化的正弦余弦算法 刘小娟，王联国苣 甘肃农业大学信息科学技术学院，兰州 730070 苣通信作者，E-mail：wanglg@gsau.edu.cn 摘    要    正弦余弦算法是一种新型仿自然优化算法，利用正余弦数学模型来求解优化问题. 为提高正弦余弦算法的优化精度 和收敛速度，提出了一种基于差分进化的正弦余弦算法. 该算法通过非线性方式调整参数提高算法的搜索能力、利用差分进 化策略平衡算法的全局探索能力及局部开发能力并加快收敛速度、通过侦察蜂策略增加种群多样性以及利用全局最优个体 变异策略增强算法的局部开发能力等优化策略来改进算法，最后通过仿真实验和结果分析证明了算法的优异性能. 关键词    智能优化算法；正弦余弦算法；差分进化算法；侦察蜂策略；全局最优个体变异策略 分类号    TP18 A sine cosine algorithm based on differential evolution LIU Xiao-juan，WANG Lian-guo苣 College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China 苣 Corresponding author, E-mail: wanglg@gsau.edu.cn ABSTRACT    In 2016, a novel naturally simulated optimization algorithm, termed the sine cosine algorithm (SCA), was proposed by Seyedali Mirjalili from Australia. This algorithm uses the sine cosine mathematical model to solve optimization problems and has attracted extensive attention from numerous scholars and researchers at home and abroad over the last few years. However, similar to other swarm intelligence optimization algorithms, SCA has numerous shortcomings in optimizing some complex function problems. To address the defects of basic SCA, such as low optimization precision, easy dropping into the local extremum, and slow convergence rate, a sine cosine algorithm based on differential evolution (SCADE) was proposed. First, the search capabilities of the new algorithm was improved by adjusting parameter r1 in a nonlinear manner and ensuring that each individual adopts the same parameters r1 , r2 , r3 , and r4 . Then, differential evolution strategies, including crossover, variation, and selection, were adopted to fully utilize the leading role of the globally optimal individual and information of other individuals in the population. This approach balanced the global exploration and local development abilities and accelerated the convergence rate of the algorithm. Next, using the reconnaissance bees’ strategy, random initialization was performed on individuals whose fitness values showed no improvement in continuous nlim times, which increased the population diversity and improved the global exploration ability of the algorithm. Moreover, the globally optimal individual variation strategy was used to conduct a fine search near the optimal solution, which enhanced the local development ability and optimization accuracy of the algorithm. Based on the above optimization strategies, the algorithm exhibits improvements and its excellent performance is validated by the result analysis of a simulation experiment. KEY  WORDS    intelligent optimization algorithm； sine cosine algorithm； differential evolution algorithm； reconnaissance bees ’ strategy；globally optimal individual variation strategy 收稿日期: 2020−07−26 基金项目: 甘肃农业大学科技创新基金资助项目（GAU-XKJS-2018-251）；甘肃省教育信息化建设专项任务资助项目（2011-02）；国家自然科 学基金资助项目（61751313） 工程科学学报，第 42 卷，第 12 期：1674−1684，2020 年 12 月 Chinese Journal of Engineering, Vol. 42, No. 12: 1674−1684, December 2020 https://doi.org/10.13374/j.issn2095-9389.2020.07.26.002; http://cje.ustb.edu.cn