A Multi-Strategy Improvement Secretary Bird Optimization Algorithm for Engineering Optimization Problems

数学优化 计算机科学 人口 多目标优化 理论(学习稳定性) 最优化问题 算法 数学 机器学习 社会学 人口学
作者
Song Qin,Junling Liu,Xiaobo Bai,Gang Hu
出处
期刊:Biomimetics [Multidisciplinary Digital Publishing Institute]
卷期号:9 (8): 478-478 被引量:4
标识
DOI:10.3390/biomimetics9080478
摘要

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
xuexixiaojin完成签到 ,获得积分10
1秒前
打打应助陈pc采纳,获得10
2秒前
邱半仙发布了新的文献求助10
3秒前
科研通AI6.3应助amanda采纳,获得10
4秒前
斯文败类应助lion_wei采纳,获得20
5秒前
wly发布了新的文献求助10
6秒前
7秒前
超级幻梅完成签到,获得积分10
8秒前
8秒前
10秒前
11秒前
超级幻梅发布了新的文献求助10
12秒前
nenoaowu发布了新的文献求助10
12秒前
12秒前
13秒前
情怀应助小刘不是恋爱脑采纳,获得10
15秒前
grs发布了新的文献求助10
15秒前
香蕉觅云应助子勿语采纳,获得10
15秒前
16秒前
79发布了新的文献求助10
16秒前
可爱的函函应助nenoaowu采纳,获得10
17秒前
tt完成签到,获得积分10
18秒前
18秒前
19秒前
单纯向雪完成签到 ,获得积分10
19秒前
20秒前
bysl完成签到,获得积分10
20秒前
大模型应助曾经的成风采纳,获得10
20秒前
22秒前
nenoaowu完成签到,获得积分10
22秒前
xixi完成签到 ,获得积分10
24秒前
谷风习习发布了新的文献求助10
24秒前
24秒前
hjhhjh完成签到,获得积分10
25秒前
25秒前
科研通AI6.3应助79采纳,获得10
26秒前
26秒前
27秒前
852应助陶醉延恶采纳,获得10
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Development Across Adulthood 1000
Chemistry and Physics of Carbon Volume 18 800
The formation of Australian attitudes towards China, 1918-1941 660
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6449828
求助须知:如何正确求助?哪些是违规求助? 8262372
关于积分的说明 17603100
捐赠科研通 5513509
什么是DOI,文献DOI怎么找? 2903166
邀请新用户注册赠送积分活动 1880227
关于科研通互助平台的介绍 1721655