[1]马 健,李海明,李 鑫.基于改进差分进化鲸鱼算法的经济负荷分配[J].计算机技术与发展,2022,32(03):132-138.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 022]
　MA Jian,LI Hai-ming,LI Xin.Economic Load Distribution Based on Improved Differential Evolution Whale Algorithm[J].,2022,32(03):132-138.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 022]
点击复制

基于改进差分进化鲸鱼算法的经济负荷分配()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年03期
页码:
132-138
栏目:
应用前沿与综合
出版日期:
2022-03-10

文章信息/Info

Title:
Economic Load Distribution Based on Improved Differential Evolution Whale Algorithm
文章编号:
1673-629X(2022)03-0132-07
作者:
马 健; 李海明; 李 鑫
上海电力大学,上海 201306
Author(s):
MA Jian; LI Hai-ming; LI Xin
Shanghai Electric Power University,Shanghai 201306,China
关键词:
鲸鱼优化算法; 差分进化算法; 自适应策略; 淘汰机制; 经济负荷分配
Keywords:
whale optimization algorithm; differential evolution algorithm; adaptive strategy; elimination mechanism; economic load distribution
分类号:
TP301;TM73
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 03. 022
摘要:
针对电力系统经济负荷分配这一典型的非凸、非线性、组合优化问题,提出一种将改进差分进化算法和鲸鱼算法相结合的优化算法。 该算法首先在鲸鱼优化算法中引入了非线性的收敛变化策略,加速寻优算法的迭代;再利用差分进化算法的交叉和选择,丰富算法种群个体信息,增强优化算法的全局收敛性;同时引入一种淘汰机制,将适应度较好的个体信息更快地保留用于下一次鲸鱼优化算法的迭代,提高了求最优解的速度和精度;最后,对多个经济负荷分配问题进行了测试,将该算法与标准鲸鱼算法、标准差分进化算法进行对比,验证了差分进化鲸鱼算法可以更合理地配置电力系统的经济负荷,能够有效找到可行解,避免陷入局部最优,能实现经济负荷的合理分配。
Abstract:
For the typical nonconvex,nonlinear,combinatorial optimization problem of economic load distribution in power system,? an optimization algorithm combining improved differential evolutionary algorithm and whale algorithm is proposed. The algorithm first introduces a nonlinear convergence change strategy in the whale optimization algorithm to accelerate the iteration of the optimization algorithm,and then uses the crossover and selection of the differential evolution algorithm to enrich the individual information of the algorithm population and enhance the global convergence of the optimization algorithm. At the same time,it introduces an elimination mechanism to retain the information of individuals with better adaptability faster for the next iteration of the whale optimization algorithm,which improves the speed and accuracy of solving the optimal solution. Finally,the improved algorithm was tested on several economic load allocation problems,and the results were compared with the standard whale algorithm and the standard differential evolutionary algorithm to verify that the differential evolutionary whale algorithm can allocate the economic load of the power system more rationally and can effectively find feasible solutions to avoid falling into the local optimum,and can realize the reasonable distribution of economic load.

相似文献/References:

[1]杨思燕[],陈为胜[]. 基于数据同化的图像融合方法研究[J].计算机技术与发展,2014,24(11):69.
　YANG Si-yan[],CHEN Wei-sheng[]. Research on Image Fusion Method Based on Data Assimilation[J].,2014,24(03):69.
[2]刘莉莉,曹宝香. 基于差分进化算法的K-Means算法改进[J].计算机技术与发展,2015,25(10):88.
　LIU Li-li CAO,Bao-xiang. Improvement of K-Means Algorithm Based on Differential Evolution Algorithm[J].,2015,25(03):88.
[3]刘学丰. 基于差分进化算法的英语考试智能组卷[J].计算机技术与发展,2016,26(01):181.
　LIU Xue-feng. Intelligent Test Paper Composition on English Examination Based on Differential Evolution Algorithm[J].,2016,26(03):181.
[4]李 强,周井泉,张严凯.基于参数自适应差分进化算法的 Web 服务组合[J].计算机技术与发展,2018,28(03):101.[doi:10.3969/ j. issn.1673-629X.2018.03.021]
　LI Qiang,ZHOU Jing-quan,ZHANG Yan-kai.Web Service Composition Based on Differential Evolution Algorithm with Adaptive Parameters[J].,2018,28(03):101.[doi:10.3969/ j. issn.1673-629X.2018.03.021]
[5]周艳平,蔡 素.一种自适应差分进化算法及应用[J].计算机技术与发展,2019,29(07):119.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 024]
　ZHOU Yan-ping,CAI Su.An Adaptive Differential Evolution Algorithm and Its Application[J].,2019,29(03):119.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 024]
[6]赵传武,黄宝柱,阎跃观,等.一种非线性权重的自适应鲸鱼优化算法[J].计算机技术与发展,2020,30(10):7.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 002]
　ZHAO Chuan-wu,HUANG Bao-zhu,YAN Yue-guan,et al.An Adaptive Whale Optimization Algorithm of Nonlinear Inertia Weight[J].,2020,30(03):7.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 002]
[7]李梓成,代永强.一种改进的鲸鱼优化算法[J].计算机技术与发展,2023,33(02):173.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 026]
　LI Zi-cheng,DAI Yong-qiang.An Improved Whale Optimization Algorithm[J].,2023,33(03):173.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 026]
[8]刘兴霖,黄 超,王 龙,等.基于聚类和 LSTM 的光伏功率日前逐时鲁棒预测[J].计算机技术与发展,2023,33(03):120.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 018]
　LIU Xing-lin,HUANG Chao,WANG Long,et al.Clustering and LSTM-based Robust Day-ahead Hourly Forecasting of Photovoltaic Power[J].,2023,33(03):120.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 018]
[9]夏 超,欧阳平,李 明,等.基于混沌精英和 Levy 飞行策略的鲸鱼优化算法[J].计算机技术与发展,2024,34(04):180.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 027]
　XIA Chao,OUYANG Ping,LI Ming,et al.Whale Optimization Algorithm Based on Chaotic Elite and Levy Flight Strategy[J].,2024,34(03):180.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 027]

更新日期/Last Update: 2022-03-10