摘要
为解决持续爆破算法寻优精度低、易陷入局部最优等问题,提出一种多策略改进的持续爆破算法。在阶段寻优过程中,基于历史阶段最优解提出新的动态爆破半径,提高算法的寻优精度;通过对阶段最优解进行反向变异提高算法跳出局部最优的能力。通过阶段局部最优解向阶段最优解的方向移动的策略更新种群位置,实现种群信息的有效交互。实验结果表明,改进算法的寻优精度和收敛速度明显提升,在求解高维函数优化问题时也有较好的寻优表现。
To deal with the problems of the continued explosion algorithm,such as low optimization accuracy and easiness to fall into local optimum,a multi-strategy improved continued explosion algorithm was proposed.In the process of phased optimization,a dynamic bursting radius was proposed based on the historical phase optimal solution to improve the optimization accuracy of the algorithm.The ability of the algorithm to jump out of the local optimum was improved by the reverse mutation of the phased optimal solution.The population position was updated using the strategy of moving the phased local optimal solution to the direction of the phased optimal solution,which realized the effective interaction of population information.Experimental results show that the optimization accuracy and convergence speed of the proposed algorithm are significantly improved.In addition,the proposed algorithm also has better performances in solving high-dimensional function optimization problems.
作者
戴泽敏
曹连英
DAI Ze-min;CAO Lian-ying(School of Science,Northeast Forestry University,Harbin 150040,China)
出处
《计算机工程与设计》
北大核心
2023年第1期148-157,共10页
Computer Engineering and Design
基金
中央高校基本科研业务费专项基金项目(2572018BC20)
黑龙江省自然科学基金项目(C201408)。
关键词
持续爆破算法
寻优性能
局部最优
函数优化
反向学习
动态半径
高维优化
continued explosion algorithm
optimization ability
local optimum
function optimization
opposition-based learning
dynamic radius
high-dimensional optimization
