精英反向黄金正弦鲸鱼算法及其工程优化研究
Study on Elite Opposition-Based Golden-Sine Whale Optimization Algorithm and its Application of Project Optimization
查看参考文献17篇
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文摘
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针对鲸鱼优化算法( Whale Optimization Algorithm,WOA)存在的收敛速度慢、寻优稳定性不足等问题,本文提出了精英反向学习的黄金正弦鲸鱼优化算法( Elite Opposition-Based Golden-Sine Whale Optimization Algorithm, EGolden-SWOA). 利用精英反向学习策略提高种群的多样性和质量可以有效提升算法的收敛速度,同时引入黄金分割数优化WOA的寻优方式,从而协调算法的全局探索与局部开发能力.对20个单模态和多模态测试函数进行寻优实验,并与RLPSO( Reverse-learning and Local-learning Particle Swarm Optimization) 、IWOA( Improved Whale Optimization Algorithm based on nonlinear convergence factor)等多个算法进行对比,实验结果表明EGolden-SWOA具有更好的寻优精度和稳定性.进一步对EGolden-SWOA进行求解大规模问题的实验,实验结果表明EGolden-SWOA可以有效解决大规模优化问题.最后将EGolden-SWOA应用于压力容器和蝶形弹簧设计优化问题,结果表明EGolden-SWOA在工程优化方面的性能优于RCSA( Rough Crow Search Algorithm) 、CPSO( Co-evolutionary Particle Swarm Optimization)等改进算法,可以有效运用于实际工程优化问题. |
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其他语种文摘
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In order to improve the slow convergence rate and low stability of WOA, elite opposition-based golden-sine whale optimization algorithm is proposed. Elite opposition-based learning strategy is used to improve the diversity and quality of the population so that the convergence rate can be promoted. At the same time,golden ratio is introduced to improve the optimal method of WOA, so as to ocoordinate the global exploration and local exploitation. Twenty unimodal and multimodal benchmark functions are tested and compared with that of other algorithms,and the experimental results show that EGolden-SWOA has a better performance in convergence rate and stability. The high dimensional function test shows that EGolden-SWOA perform well in solving large scale optimization problem. Finally,EGolden-SWOA is applied to the optimization design of the pressure vessel and tension /compression spring, the result shows that its performance in project optimization is better than RCSA and CPSO, it can be effectively applied to project optimization. |
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来源
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电子学报
,2019,47(10):2177-2186 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2019.10.020
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关键词
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鲸鱼优化算法
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精英反向学习
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黄金正弦算法
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大规模优化问题
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工程优化
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地址
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上海工程技术大学管理学院, 上海, 201620
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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上海市自然科学基金
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文献收藏号
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CSCD:6668671
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参考文献 共
17
共1页
|
|
1.
Mirjalili S. The whale optimization algorithm.
Advances in Engineering Software,2016,95(5):51-67
|
CSCD被引
624
次
|
|
|
|
|
2.
Prakash D B. Optimal siting of capacitors in radial distribution network using whale optimization algorithm.
Alexandria Engineering Journal,2016,56(4):499-509
|
CSCD被引
11
次
|
|
|
|
|
3.
Zhao H. Energy-Related CO_2 emissions forecasting using an improved LSSVM model optimized by whale optimization algorithm.
Energies,2017,10(7):874-888
|
CSCD被引
2
次
|
|
|
|
|
4.
Saidala R K. Improved whale optimization algorithm case study: clinical data of anaemic pregnant woman.
Data Engineering and Intelligent Computing,2016:271-281
|
CSCD被引
1
次
|
|
|
|
|
5.
Aziz M A E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation.
Expert Systems with Applications,2017,83(17):242-256
|
CSCD被引
20
次
|
|
|
|
|
6.
Oliva D. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm.
Applied Energy,2017,200(16):41-154
|
CSCD被引
28
次
|
|
|
|
|
7.
Ling Y. Levy flight trajectory-based whale optimization algorithm for global optimization.
IEEE Access,2017,5(99):6168-6186
|
CSCD被引
20
次
|
|
|
|
|
8.
Tizhoosh H R. Opposition-based learning: a new scheme for machine intelligence.
International Conference on Computational Intelligence for Modelling,Control and Automation, 2005 and International Conference on Intelligent Agents,Web Technologies and Internet Commerce,2005:695-701
|
CSCD被引
1
次
|
|
|
|
|
9.
夏学文. 具备反向学习和局部学习能力的粒子群算法.
计算机学报,2015,38(7):1397-1407
|
CSCD被引
44
次
|
|
|
|
|
10.
魏文红. 一种基于反向学习的约束差分进化算法.
电子学报,2016,44(2):426-436
|
CSCD被引
5
次
|
|
|
|
|
11.
谢承旺. 应用档案精英学习和反向学习的多目标进化算法.
计算机学报,2017,40(3):757-772
|
CSCD被引
13
次
|
|
|
|
|
12.
谢承旺. 应用精英反向学习的多目标烟花爆炸算法.
电子学报,2016,44(5):1180-1188
|
CSCD被引
12
次
|
|
|
|
|
13.
周新宇. 一种精英反向学习的粒子群优化算法.
电子学报,2013,41(8):1647-1652
|
CSCD被引
57
次
|
|
|
|
|
14.
Tanyildizi E. Golden sine algorithm: a novel math-inspired algorithm.
Advances in Electrical & Computer Engineering,2017,17(2):71-78
|
CSCD被引
45
次
|
|
|
|
|
15.
龙文. 求解大规模优化问题的改进鲸鱼优化算法.
系统工程理论与实践,2017,37(11):2983-2994
|
CSCD被引
59
次
|
|
|
|
|
16.
Hassanien A E. A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems.
Journal of Ambient Intelligence & Humanized Computing,2018:1-25
|
CSCD被引
2
次
|
|
|
|
|
17.
He Q. An effective co-evolutionary particle swarm optimization for constrained engineering design problems.
Engineering Applications of Artificial Intelligence,2007,20(1):89-99
|
CSCD被引
71
次
|
|
|
|
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