Summary of New Group Intelligent Optimization Algorithms

NAVIGATE

Table of Contents

STATISTICS

Viewed3289

Downloads1954

Summary of New Group Intelligent Optimization Algorithms

[HTML] PDF下载 (1954)

[1]GAO Yuelin,YANG Qinwen,WANG Xiaofeng,et al.Summary of New Group Intelligent Optimization Algorithms[J].Journal of Zhengzhou University (Engineering Science),2022,43(03):21-30.[doi:10.13705/j.issn.1671-6833.2022.03.007]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 03 Page number: 21-30 Column: Public date: 2022-04-10

Title:: Summary of New Group Intelligent Optimization Algorithms

Author(s):: GAO Yuelin¹; 2; YANG Qinwen¹; 2; WANG Xiaofeng¹; LI Jiahang²; 3; SONG Yanjie⁴; 1.School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China;
2.Ningxia Key Laboratory of Intelligent Information and Data Processing，North Minzu University, Yinchuan 750021, China;
3.School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China;
4.College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China

Keywords:: swarm intelligence optimization algorithm; bat algorithm; fruit fly optimization algorithm; whale optimization algorithm; salp swarm algorithm; harris hawks optimization algorithm

CLC:: TP18；TP301

DOI:: 10.13705/j.issn.1671-6833.2022.03.007

Abstract:: Intelligent optimization algorithms could be divided into four categories: nature-like optimization algorithm, evolutionary algorithm, plant growth simulation algorithm，and swarm intelligence optimization algorithm.The swarm intelligence optimization algorithm was the most important type of algorithm.It played an important role in solving complex engineering problems, and together with image processing, fault detection, path planning, particle filtering, feature selection, production scheduling, intrusion detection, support vector machines, wireless sensors, neural network models, and got more extensive applications in other fields.In recent years, intelligent optimization algorithms such as bat algorithm, fruit fly optimization algorithm, whale optimization algorithm, salp swarm algorithm, and harris hawks optimization algorithm were widely used.Based on these five new swarm intelligence optimization algorithm, the model, characteristics, improvement strategies and application fields of the algorithm were reviewed.It analyzed the development opportunities and future trends it faced from theoretical investigations, improvement strategy and application studies, and provided a guidance on algorithm application.Findings showed that swarm intelligence optimization algorithm could perform well on many classic problems, but still should be expanded in the fields of multi-objective optimization, multi-constraint optimization, dynamic optimization, and mixed variable optimization.Effective parameter control of different groups of intelligent optimization algorithm in the face of various specific problems was still the focus of future studies.Co-evolution from populations, exploring more efficient hybrid methods and search strategies could be feasible solutions.

References:: [1] 韩丽霞. 自然启发的优化算法及其应用研究[ D] . 西安:西安电子科技大学,2009.
[2] 李士勇,李研,林永茂. 智能优化算法与涌现计算 [M] . 北京:清华大学出版社,2019.
[3] METROPOLIS N,ROSENBLUTH A W,ROSENBLUTH M N,et al. Equation of state calculations by fast computing machines [ J ]. The journal of chemical physics, 1953,21(6):1087-1092.
[4] KIRKPATRICK S, Jr GELATT C D,VECCHI M P. Optimization by simulated annealing [ J ] . Science, 1983,220(4598) :671-680.
[5] HOSSEINI H S. Problem solving by intelligent water drops[C] / / 2007 IEEE Congress on Evolutionary Computation. Piscataway:IEEE,2007:3226-3231.
[6] HOSSEINI H S. Intelligent water drops algorithm[ J] . International journal of intelligent computing and cybernetics,2008,1(2) :193-212.
[7] 华一村,刘奇奇,郝矿荣,等. 非规则 Pareto 前沿面多目标进化优化算法研究综述[ J] . 郑州大学学报 (工学版) ,2021,42(1) :1-8.
[8] HOLLAND J H. Adaptation in natural and artificial systems: an introductory analysis with application to biology[M] . Cambridge:MIT press,1992.
[9] STORN R, PRICE K. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces [ J ] . Journal of global optimization, 1997,11(4) :341-359.
[10] MEHRABIAN A R,LUCAS C. A novel numerical optimization algorithm inspired from weed colonization [ J] . Ecological informatics,2006,1(4) :355-366.
[11] YANG X S. Flower pollination algorithm for global optimization[C] / / Unconventional Computation and Natural Computation. Berlin:Springer,2012: 240-249.
[12] BENI G, WANG J. Swarm intelligence in cellular robotic systems [ J] . Robots and biological systems: towards a new bionics, 1993,102: 703-712.
[13] COLORNI A, DORIGO M, MANIEZZO V,et al. Distributed optimization by ants colonies[C] / / Proceedings of the 1st European Conference on Artificial Life. Paris: El Sevier Publishing, 1991: 134-142.
[14] KENNEDY J,EBERHART R. Particle swarm optimization[ C] / / Proceedings of ICNN′95-International Conference on Neural Networks. Piscataway: IEEE,1995: 1942-1948.
[15] YANG X S. A new metaheuristic bat-inspired algorithm[ J] . Computer knowledge & technology, 2010, 284: 65-74.
[16] 潘文超. 果蝇最佳化演算法:最新演化式计算技术 [M] . 台中:沧海书局, 2011.
[17] MIRJALILI S,LEWIS A. The whale optimization algorithm[ J] . Advances in engineering software,2016,95: 51-67.
[18] MIRJALILI S,GANDOMI A H,MIRJALILI S Z,et al. Salp swarm algorithm:a bio-inspired optimizer for engineering design problems[ J] . Advances in engineering software,2017,114:163-191.
[19] HEIDARI A A,MIRJALILI S,FARIS H, et al. Harris hawks optimization:algorithm and applications[ J] . Future generation computer systems,2019,97:849-872.
[20] 许德刚,赵萍. 蝙蝠算法研究及应用综述[ J] . 计算机工程与应用,2019,55(15) :1-12,31.
[21] 李苗苗,王秋萍,惠蕙. 分数阶策略和带有 Lévy 飞行的螺旋蝙蝠算法[ J] . 计算机工程与应用,2021, 57(18) :75-81.
[22] 倪昌浩,邹海. 基于改进蝙蝠算法的移动机器人路径规划方法研究[ J] . 制造业自动化,2021,43( 6) : 53-56,62.
[23] ESKANDARI S,JAVIDI M M. A novel hybrid bat algorithm with a fast clustering-based hybridization [ J ] . Evolutionary intelligence,2020,13(3) :427-442.
[24] MUGEMANYI S,QU Z Y,RUGEMA F X,et al. Optimal reactive power dispatch using chaotic bat algorithm [ J] . IEEE access,2020,8:65830-65867.
[25] CHAKRI A,KHELIF R,BENOUARET M, et al. New directional bat algorithm for continuous optimization problems[ J] . Expert systems with applications,2017, 69:159-175.
[26] 陈凯镔,陶沙沙,向峻伯,等. 改进 BA 的汽车发动机 DCS 故障检测优化 [ J ] . 机械设计与制造, 2020 (10) :176-181.
[27] 祖宏亮. 基于模糊聚类的图像分割算法研究 [ D] .哈尔滨:哈尔滨理工大学,2020.
[28] PAN W T. A new fruit fly optimization algorithm:taking the financial distress model as an example [ J ] . Knowledge-based systems,2012,26:69-74.
[29] 霍慧慧. 果蝇优化算法及其应用研究[ D] . 太原:太原理工大学,2015.
[30] 宋杰,许冰,杨淼中. 基于自适应步长果蝇优化算法图像分割 [ J] . 计算机工程与应用, 2020, 56 ( 4) : 184-190.
[31] YUAN X F, DAI X S, ZHAO J Y, et al. On a novel multi-swarm fruit fly optimization algorithm and its application[ J] . Applied mathematics and computation, 2014,233:260-271.
[32] CHEN C. RWFOA:a random walk-based fruit fly optimization algorithm[ J] . Soft computing,2020,24( 16) : 12681-12690.
[33] FAN Y,WANG P J,HEIDARI A A,et al. Rationalized fruit fly optimization with sine cosine algorithm:a comprehensive analysis [ J] . Expert systems with applications,2020,157:113486.
[34] 党建武,谭凌. 改进果蝇算法优化加权极限学习机的入侵检测[J]. 系统仿真学报,2021,33(2):331-338.
[35] 信成涛,邹海,盛超,等. 新型果蝇优化算法的最佳熵阈值图像分割[ J] . 微电子学与计算机,2019,36 (4) :52-56.
[36] 王涛,RYAD C. 非线性权重和收敛因子的鲸鱼算法 [ J] . 微电子学与计算机,2019,36(1) :11-15.
[37] 龙文,蔡绍洪,焦建军,等. 求解大规模优化问题的改进鲸鱼优化算法 [ J ] . 系统工程理论与实践, 2017,37(11) :2983-2994.
[38] FAN Q,CHEN Z J,ZHANG W,et al. ESSAWOA:enhanced whale optimization algorithm integrated with salp swarm algorithm for global optimization[ J] . Engineering with computers,2020,38:797-814.
[39] ABDEL-BASSET M, CHANG V, MOHAMED R. HSMA_WOA:a hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images [ J] . Applied soft computing,2020,95:106642.
[40] QAIS M H,HASANIEN H M,ALGHUWAINEM S. Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators [ J] . Applied soft computing,2020,86:105937.
[41] JADHAV A N, GOMATHI N. WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering[ J] . Alexandria engineering journal, 2018,57(3) :1569-1584.
[42] MOSTAFA A,HASSANIEN A E,HOUSENI M,et al. Liver segmentation in MRI images based on whale optimization algorithm [ J] . Multimedia tools and applications,2017,76(23) :24931-24954.
[43] 王斐,贾晓洪,李丽娟,等. 基于樽海鞘群算法的图像匹配方法 [ J] . 弹箭与制导学报, 2019, 39 ( 5) : 111-114.
[44] 陈涛,王梦馨,黄湘松. 基于樽海鞘群算法的无源时差定位 [ J ] . 电子与信息学报, 2018, 40 ( 7 ) : 1591-1597.
[45] 刘森,贾志成,陈雷,等. 基于樽海鞘群体优化非负矩阵分解的高光谱图像解混算法[ J] . 计算机辅助设计与图形学学报,2019,31(2) :315-323.
[46] WU J,NAN R J,CHEN L. Improved salp swarm algorithm based on weight factor and adaptive mutation [ J] . Journal of experimental & theoretical artificial intelligence,2019,31(3) :493-515.
[47] SHEKHAWAT S S, SHARMA H, KUMAR S, et al. BSSA: binary salp swarm algorithm with hybrid data transformation for feature selection [ J] . IEEE access, 2021,9:14867-14882.
[48] ABUALIGAH L,SHEHAB M,DIABAT A,et al. Selection scheme sensitivity for a hybrid salp swarm algorithm: analysis and applications [ J ] . Engineering with computers,2020:1-27.
[49] 马一鸣,石志东,赵康,等. 基于改进哈里斯鹰优化算法的 TDOA 定位[ J] . 计算机工程,2020,46( 12) : 179-184.
[50] 贾鹤鸣,康立飞,孙康健,等. 哈里斯鹰算法优化脉冲耦合神经网络的图像自动分割 [ J] . 应用科技, 2019,46(4) :16-20,25.
[51] QU C W,HE W,PENG X N,et al. Harris hawks optimization with information exchange[ J] . Applied mathematical modelling,2020,84:52-75.
[52] DU P,WANG J Z,HAO Y,et al. A novel hybrid model based on multi-objective harris hawks optimization algorithm for daily PM2. 5 and PM10 forecasting [ J] . Applied soft computing,2020,96:106620.
[53] CHEN H L, JIAO S, WANG M J, et al. Parameters identification of photovoltaic cells and modules using diversification-enriched harris hawks optimization with chaotic drifts[ J] . Journal of cleaner production,2020, 244:118778

Similar References:

Memo

Last Update: 2022-05-02