基于改进鲸鱼优化算法的测试性优化分配方法
Testability Optimal Allocation Method Based on Improved Whale Optimisation Algorithm
何健夫 1赵国扬 1郑晓霞1
作者信息
- 1. 成都航空职业技术学院,成都 610100
- 折叠
摘要
传统的测试性分配方法无法解决多目标优化问题,因此将费用函数和分配模型相结合,求解在各影响因素情况下费用最少问题,提出一种基于自适应权重的鲸鱼优化算法(SIN-WOA).该算法通过增加自适应权重系数来增强算法的局部寻优能力,提高收敛速度.最后,选取实际案例运用综合加权分配法确定各影响因素的权重值和系数,选取故障检测率(FDR)进行指标分配对比.结果表明:改进鲸鱼优化算法能够解决多目标优化问题,并且收敛速度较快,求解精度较高.
Abstract
The traditional testability assignment method can not solve the multi-objective optimization problem.Therefore,a whale optimization algorithm based on adaptive weights(SIN-WOA)is proposed to solve the problem of minimum cost under various influencing factors by combining the cost function and allocation model.By increasing the adaptive weight coefficient,the local opti-mization ability of the algorithm is enhanced and the convergence speed is improved.Finally,a practical case is selected to determine the weight value and coefficient of each influencing factor by comprehensive weighted distribution method,and fault detection rate(FDR)is selected for index distribution and comparison.The results show that the improved whale optimization algorithm can solve the multi-objective optimization problem,the convergence speed is fast and the solving precision is high.
关键词
测试性优化分配/鲸鱼优化算法/惩罚函数/自适应权重Key words
testability optimal allocation/whale optimization algorithm/penalty function/adaptive weight引用本文复制引用
基金项目
人工智能四川省重点实验室开放基金(2021RYY02)
四川省无人系统智能感知控制技术工程实验室项目(WRXT2023-001)
出版年
2024
NETL
NSTL
万方数据