多策略改进的蛇优化算法
Improved Snake Optimizer of Multi-strategy
权浩迪 1刘勇国 1傅翀 1朱嘉静 1张云 1兰刚 1李巧勤1
作者信息
- 1. 电子科技大学 信息与软件工程学院,四川 成都 610054
- 折叠
摘要
为改进蛇优化算法(Snake Optimizer,SO)在探索方式、变量计算、空间搜索方式和种群更新方式等方面存在的不足,提出了一种多策略改进的蛇优化算法(Improved Snake Optimizer,ISO).首先,提出探索寻优策略,根据个体相对于优势个体的位置更新自身的位置,使种群在前期快速收敛到最优解附近.其次,优化变量计算方式,将SO算法中的指数运算改进为多项式运算,提高SO的时间效率.同时引入动态调整搜索空间的机制,随种群进化迭代次数的增加逐步扩展搜索范围以提高寻优能力.最后,引入优势进化策略,淘汰适应度较差的个体并结合优势个体的基因产生新个体,快速提高种群优势基因比例以增加收敛速度.对不同基准测试函数进行寻优实验,分别与经典SO算法和5 种启发式算法进行对比,结果表明ISO具有较强的寻优能力.为进一步验证所提算法的高效性和实用性,将ISO应用于全连接神经网络的优化问题,结果表明基于ISO优化的神经网络具有更优的分类效果.
Abstract
An Improved Snake Optimizer(ISO)of multi-strategy is proposed to address the limitations of the Snake Optimizer(SO)in exploration strategy,variable computation,space searching and population updating.Firstly,an optimized exploration strategy is proposed,where individuals update their positions based on their relative positions to the best individual.This allows the population to quickly converge to the vicinity of the optimal solution in the early stage.Secondly,variable computation is optimized by replacing the exponential operations in the SO with polynomial operations to improve the time efficiency of SO.Additionally,we introduce the dynamic adjustment mechanism of the search space,gradually expanding the search range with the increase in population evolution iterations to enhance the optimization capability.Finally,an advantage evolution strategy is introduced,which eliminates individuals with lower fitness and combines the genes of dominant individuals to generate new individuals.This strategy accelerates convergence by rapidly increasing the proportion of dominant genes in the population.Experiments were conducted on different benchmark test functions,comparing ISO with the classical SO and five heuristic algorithms.The results demonstrate that ISO exhibits strong optimization capabilities.To further validate the efficiency and practicality of the proposed algorithm,ISO is applied to the optimization problem of fully connected neural networks.The results show that neural networks optimized based on ISO achieve superior classification perform-ance.
关键词
蛇优化算法/启发式算法/优化问题/多策略改进/神经网络Key words
snake optimizer/heuristic algorithm/optimization problem/multi-strategy improvement/neural network引用本文复制引用
基金项目
国家科技基础资源调查专项(2022FY102002)
国家自然科学基金资助项目(62202084)
四川省自然科学基金资助项目(2022NSFSC0883)
四川省自然科学基金资助项目(2022NSFSC0958)
出版年
2024
国家科技期刊平台
NSTL
万方数据