基于混合策略改进的蛇优化算法及其应用  

An improved snake optimization algorithm based on hybrid strategies and its application

作　　者：梁昔明 史兰艳 龙文[2] LIANG Xi-ming;SHI Lan-yan;LONG Wen(School of Science,Beijing University of Civil Engineering and Architecture,Beijing 102616;School of Mathematics and Statistics,Guizhou University of Finance and Economics,Guiyang 550025,China)

机构地区：[1]北京建筑大学理学院,北京102616 [2]贵州财经大学数学与统计学院,贵州贵阳550025

出　　处：《计算机工程与科学》2024年第4期693-706,共14页Computer Engineering & Science

基　　金：国家自然科学基金(12361106);贵州省自然科学基金重点项目(黔科合基础-ZK[2003]重点003);中央支持地方科研创新团队项目(PXM2013_014210_000173);北京建筑大学2021年校级教育科学研究项目(Y2113)。

摘　　要：针对基本蛇优化算法求解优化问题时易陷入局部最优的问题,提出了一种基于维度选择策略、选择交配策略和重新分组策略的改进蛇优化算法(SSO)。算法SSO在基本蛇优化算法在战斗或交配阶段引入维度选择策略,由随机概率选择每条蛇个体在不同维度的位置更新模式,以避免迭代后期出现个体位置停滞现象;同时引入选择交配策略,选择适应度值小的部分个体进行战斗或交配,剩余个体利用探索阶段位置更新公式进行位置更新,以提高战斗或交配阶段的探索能力;采用重新分组策略,个体每迭代10次都将随机打乱并重新分组,以增加种群多样性,提高算法寻优能力。利用30个标准无约束优化问题进行了数值实验,结果表明,相比于基本蛇优化算法SO等6种对比算法,算法SSO的寻优能力更强,且对求解高维优化问题更有效。用算法SSO优化BP神经网络的初始权值和阈值,实验结果表明所得SSO-BP神经网络在红酒分类和预测鲍鱼年龄时的准确性和稳定性优于其他对比神经网络。To solve the problem that the basic snake optimization algorithm easily falls into local optimization,an improved snake optimization algorithm(SSO)based on dimension selection strategy,selection mating strategy,and re-grouping strategy is proposed.The SSO algorithm introduces the dimension selection strategy in the combat or mating stage of the basic snake optimization algorithm.The random probability is used to select the position update mode of each snake individual in different dimensions,so as to avoid the phenomenon of individual position stagnation in the later stage of iteration.The selection mating strategy is introduced in the combat or mating stage,and a part of individuals with smaller fitness values are selected for combat or mating.The remaining individuals use the exploration stage position update formula for position update to improve the exploration ability of the combat or mating stage.The re-grouping strategy is used,and the individuals are randomly disrupted and re-grouped every ten iterations to increase population diversity and improve the optimization ability of the algorithm.Numerical experiments on 30 standard unconstrained optimization problems show that compared with six comparative algorithms such as the basic snake optimization algorithm SO,the SSO algorithm has stronger optimization ability and is more effective for solving high-dimensional optimization problems.The SSO algorithm is used to optimize the initial weights and thresholds of BP neural networks.Experimental results show that the SSO-BP neural network has better accuracy and stability than other comparative neural networks in classifying wines and predicting abalone age.

关 键 词：蛇优化算法 维度选择策略 选择交配策略 重新分组策略 数值实验 

分 类 号：TP018[自动化与计算机技术]