摘要
针对经典花授粉算法容易陷入局部最优解和收敛速度慢的缺点,提出一种增强型透镜成像策略和随机邻域变异策略的花授粉算法。通过增强型透镜成像策略扩展花授粉算法的搜索空间,增加解的多样性,有助于算法跳出局部最优解。引入随机邻域变异策略,借助邻域内的信息指导算法搜索,增强算法的收敛精度和搜索速度。对改进后的花授粉算法和四种其他改进算法在CEC2013测试函数上进行比较,实验证明改进后的多策略花授粉算法不论是收敛精度还是搜索速度都比对比算法优秀。最后把多策略花授粉算法应用在汽车传动参数模型上研究该算法的实际效用,结果表明多策略花授粉算法在汽车传动参数优化问题上都优于对比算法。
Classic flower pollination algorithm(FPA)can be easily exposed to the shortcomings of local optimal solution and slow convergence velocity.In view of these shortcomings,this paper proposed an FPA with an enhanced lens imaging strategy and random neighborhood-based mutation strategy.The lens imaging strategy could help the algorithm to avoid the shortcoming of local optimal solution by expanding the search space of FPA to increase the diversity of the solution.The introduction of random neighborhood-based mutation strategy could enhance the convergence accuracy and search speed of the algorithm by gui-ding algorithm search with information in the neighborhood.A comparison of the improved FPA with four other improved algorithms on CEC2013 test function found that the improved multi-strategy FPA performed better than the comparison algorithms in both convergence accuracy and search speed.To study its practical utility,this paper applied the multi-strategy FPA into the automobile transmission parameter model and the results indicate that multi-strategy FPA was better than the comparison algorithm in optimization of automobile transmission parameters.
作者
李大海
伍兆前
王振东
Li Dahai;Wu Zhaoqian;Wang Zhendong(School of Information Engineering,Jiangxi University of Science&Technology,Ganzhou Jiangxi 341099,China)
出处
《计算机应用研究》
CSCD
北大核心
2022年第8期2388-2396,2402,共10页
Application Research of Computers
基金
国家自然科学基金资助项目(61563019,61562037)。
关键词
随机邻域变异
透镜成像
花授粉算法
参数优化
收敛精度
random neighborhood variation
lens imaging
flower pollination algorithm(FPA)
parameter optimization
convergence accuracy
