Research on swarm intelligence optimization algorithm

doi:10.19682/j.cnki.1005-8885.2020.0012

The Journal of China Universities of Posts and Telecommunications ›› 2020, Vol. 27 ›› Issue (3): 1-20.doi: 10.19682/j.cnki.1005-8885.2020.0012

Research on swarm intelligence optimization algorithm

Fei Wei¹,Liu /Cong¹,¹,Hu /Sheng

Received:2019-09-23 Revised:2019-12-13 Online:2020-06-24 Published:2020-08-30
Contact: Liu /Cong E-mail:liucong8@163.com
Supported by:
Science and Technology Research Program of Hubei Provincial Department of Education.; National Natural Science Foundation of China

Abstract

Abstract: The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem. It simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent individuals. This paper first introduces the principles of various swarm intelligent optimization algorithms. Then, the typical application of these swarm intelligence optimization algorithms in various fields is listed. After that, the advantages and defects of all swarm intelligence optimization algorithms are summarized. Next, the improvement strategies of various swarm intelligence optimization algorithms are explained. Finally, the future development of various swarm intelligence optimization algorithms is prospected.

Key words: bionics, natural heuristic algorithm, swarm intelligence algorithm, intelligent computing

CLC Number:

TP311

Fei Wei Liu /Cong Hu /Sheng . Research on swarm intelligence optimization algorithm[J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(3): 1-20.

References 1.

Dorigo M. Optimization, learning and natural algorithms. Ph D Thesis. Milano, Italy: Politecnico di Milano, 1992 2. Han Z H, Liu S G, Zhang G X, et al. A 3D measuring path planning strategy for intelligent CMMs based on an improved ant colony algorithm. International Journal of Advanced Manufacturing Technology, 2017, 93: 1487-1497 3. Abdulkader M M S, Gajpal Y, Elmekkawy T Y. Hybridized ant colony algorithm for the multi compartment vehicle routing problem. Applied Soft Computing, 2015, 37: 196-203 4. Zhao T, Pan X J, He Q F. Application of dynamic ant colony algorithm in route planning for UAV. Proceedings of the 7th International Conference on Information Science and Technology (ICIST’17), Apr 16-19, 2017, Da Nang, Vietnam. Piscataway, NJ, USA: IEEE, 2017: 433-437 5. Ahmadizar F. A new ant colony algorithm for makespan minimization in permutation flow shops. Computers and Industrial Engineering, 2012, 63(2): 355-361 6. Yan J G, Xing L N, Zhang Z S, et al. An improved ant colony algorithm for the dual time windows constraining job shop scheduling problem. Advanced Information Technology,Proceedings of the 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC’17), Mar 25-26, 2017, Chongqing, China. Piscataway, NJ, USA: IEEE, 2017: 104-108 7. Zeng M R, Xi L, Xiao A M. The free step length ant colony algorithm in mobile robot path planning. Advanced Robotics, 2016, 30(23): 1509-1514 8. Yan S, Yao X M. Design of routing protocol and node structure in wireless sensor network based on improved ant colony optimization algorithm. Proceedings of the 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA’17): Vol.1, Sept 23-25, 2017, Xi’an, China. Piscataway, NJ, USA: IEEE, 2017: 236-240 9. Zhang L Y, Xioa C, Fei T. Improved ant colony optimization algorithm based on RNA computing. Automatic Control and Computer Sciences, 2017, 51(5): 366-375 10. Aimoerfu, Shi M Y, Li C F, et al. Implementation of the protein sequence model based on ant colony optimization algorithm.Proceedings of the IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS’17): May 25-27, 2017, Wuhan, China. Piscataway, NJ, USA: IEEE, 2017: 611-615 11. Rukundo O, Cao H Q. Advances on image interpolation based on ant colony algorithm. SpringerPlus, 2016, 5: Article 403 12. Shen C, Wang D, Tang S M, et al. Hybrid image noise reduction algorithm based on genetic ant colony and PCNN. The Visual Computer, 2016, 33(11): 1373-1384 13. Reddy T N, Supreethi K P. Optimization of K-means algorithm: Ant colony optimization. Proceedings of the 2017 International Conference on Computing Methodologies and Communication (ICCMC’17), Jul 18-19, 2017, Erode, India. Piscataway, NJ, USA: IEEE, 2017: 53-0-535 14. Kennedy J, Eberhart R C. Particle swarm optimization. Proceedings of the IEEE 1995 International Conference on Neural Networks (ICNN’95), Nov 27-Dec 1, 1995, Perth, Australia. Piscataway, NJ, USA: IEEE, 1995: 1942-1948 15. Gong Y J, Li J J, Zhou Y, et al. Genetic learning particle swarm optimization. IEEE Transactions on Cybernetics, 2017, 46(10): 2277-2290 16. Phoemphon S, So-In C, Niyato D. A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization. Applied Soft Computing, 2018, 65: 101-120 17. Rosli A D, Adenan N S, Hashim H, et al. Application of particle swarm optimization algorithm for optimizing ANN model in recognizing ripeness of citrus. Proceedings of the 6th International Conference on Electronic Devices, Systems and Applications (ICEDSA’17), Aug 7-8, 2017, Kuching, Sarawak, Malaysia. IOP Conference Series: Materials Science and Engineering 340. IOP Publishing Ltd, 2018:10p 18. Chen S M, Hsin W C. Weighted fuzzy interpolative reasoning based on the slopes of fuzzy sets and particle swarm optimization techniques. IEEE Transactions on Cybernetics, 2017, 45(7): 1250-1261 19. Bouchebbat R, Gherbi S. Design and application of fuzzy immune PID adaptive control based on particle swarm optimization in thermal power plants. Proceedings of the 6th International Conference on Systems and Control (ICSC’17), May 7-9, 2017, Batna, Algeria. Piscataway, NJ, USA: IEEE, 2017: 33-38 20. Li X L, Qian J X. Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques. Journal of Circuits and Systems, 2003, 8(1): 1-6 (in Chinese) 21. Zhang L H, Dou Z Q, Sun G L. An improved artificial fish-swarm algorithm using cluster analysis. Recent Developments in Mechatronics and Intelligent Robotics: Proceedings of the International Conference on Mechatronics and Intelligent Robotics (ICMIR’17): Vol 1, May 20-21, 2017, Kunming, China. AISC 690. Berlin, Germany: Springer, 2017: 49-54 22. Sun T F, Zhang H, Liu S J, et al. Application of an artificial fish swarm algorithm in solving multiobjective trajectory optimization problems. Chemistry and Technology of Fuels and Oils, 2017, 53(3): 541-547 23. Janaki S D, Geetha K. Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm. Polish Journal of Medical Physics and Engineering, 2017, 23(2): 29-36 24. Lei X J, Yang X Q, Wu F X. Artificial fish swarm optimization based method to identify essential proteins. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, DOI: 10.1109/TCBB.2018.2865567 25. Talha M, Saeed M S, Mohiuddin G, et al. Energy optimization in home energy management system using artificial fish swarm algorithm and genetic algorithm. Advances in Intelligent Networking and Collaborative Systems: Proceedings of the 9th International Conference on Intelligent Networking and Collaborative Systems (INCoS’17): Aug 24-26, 2017, Torondo, Canada. LNDECT 8. Berlin, Germany: Springer, 2017: 203-213 26. Passino K M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 2002, 22(3): 52-67 27. Kumar K S, Jayabarathi T. Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm. International Journal of Electrical Power and Energy Systems, 2012, 36(1): 13-17 28. Dasgupta S, Das S, Biswas A, et al. Automatic circle detection on digital images with an adaptive bacterial foraging algorithm. Soft Computing: A Fusion of Foundations, Methodologies and Applications, 2010, 14(11): 1151-1164 29. Al-Hadi I A A, Hashim S Z M, Shamsuddin S M H. Bacterial foraging optimization algorithm for neural network learning enhancement. Proceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS’11), Dec 5-8, 2011, Melacca, Malaysia. Piscataway, NJ, USA: IEEE, 2011: 200-205 30. Wan M, Li L X, Xiao J H, et al. Data clustering using bacterial foraging optimization. Journal of Intelligent Information Systems, 2012, 38(2): 321-341 31. Bakwad K M, Pattnaik S S, Sohi B S, et al. Bacterial foraging optimization technique cascaded with adaptive filter to enhance peak signal to noise ratio from single image. IETE Journal of Research, 2009, 55(4): 173-179 32. Acharya D P, Panda G, Mishra S, et al. Bacteria foraging based independent component analysis. Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'07): Vol. 2, Dec 13-15, 2007, Sivakasi, India: Piscataway, NJ, USA: IEEE, 2007: 527-531 33. Wu C G, Zhang N, Jiang J Q, et al. Improved bacterial foraging algorithms and their applications to job shop scheduling problems. Adaptive and Natural Computing Algorithms: Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA’07): Part 1, Apr 11-14, 2007, Warsaw, Poland. LNCS 4431. Berlin, Germany. Springer, 2007: 562-569 34. Yang C G, Tu X Y, Chen J. Algorithm of marriage in honey bees optimization based on the wolf pack search. Proceedings of the 2007 International Conference on Intelligent Pervasive Computing (IPC’07), Oct 11-13, 2007, Jeju, Republic of Korea. Piscataway, NJ, USA: IEEE, 2007: 462-467 35. Badem H, Basturk A, Caliskan A, et al. A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory BFGS optimization algorithms. Neurocomputing, 2017, 266: 506-526 36. Kumar Y, Sahoo G. A two-step artificial bee colony algorithm for clustering. Neural Computing and Applications, 2017, 28(3): 537-551 37. Liu F X, Sun Y H, Wang G G, et al. An artificial bee colony algorithm based on dynamic penalty and Lévy flight for constrained optimization problems. Arabian Journal for Science and Engineering, 2018, 43(12): 7189-7208 38. Liang X, Ji Y M Huang M.. Solving hybrid flow-shop scheduling based on improved multi-objective artificial bee colony algorithm. Proceedings of the 2nd International Conference on Cloud Computing and Internet of Things (CCIOT’16), Oct 22-23, 2016, Dalian, China. Piscataway, NJ, USA: IEEE, 2016: 43-47 39. Sawant P T, Lbhattar P C, Bhattar C L. Enhancement of PV system based on artificial bee colony algorithm under dynamic conditions. Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics (RTEICT’16), May 20-21, 2016, Bangalore, India. IEEE, 2016: 1251-1255 40. Pérez C J, Vegarodríguez M A, Reder K, et al. A multi-objective artificial bee colony-based optimization approach to design water quality monitoring networks in river basins. Journal of Cleaner Production, 2017, 166: 579-589 41. Sajedi H, Mohammadi F G. Region based image steganalysis using artificial bee colony. Journal of Visual Communication and Image Representation, 2017, 44: 214-226 42. Dahan F, El Hindi K, Ghoneim A. Enhanced artificial bee colony algorithm for QoS-aware Web service selection problem. Computing, 2017, 99(5): 507-517 43. Chu S C, Tsai P W, Pan J S. Cat swarm optimization. Trends in Artificial Intelligence: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Aug 7-11, 2006, Guilin, China. LNCS 4099. Berlin, Germany. Springer, 2006: 854-858 44. Pradhan P M, Panda G. Solving multiobjective problems using cat swarm optimization. Expert Systems with Applications, 2012, 39(3): 2956-2964 45. Razzaq S, Maqbool F, Hussain A. Modified cat swarm optimization for clustering. Advances in Brain Inspired Cognitive Systems: Proceedings of the 8th International Conference on Brain Inspired Cognitive Systems (BICS’16), Nov 28-30, 2016, Beijing, China. LNCS10023. Berlin, Germany: Springer, 2016, 10023: 161-170 46. Saha S K, Ghoshal S P, Kar R, et al. Cat swarm optimization algorithm for optimal linear phase FIR filter design. ISA Transactions, 2013, 52(6): 781-794 47. Xu L, Hu W B. Cat swarm optimization-based schemes for resource-constrained project scheduling. Applied Mechanics and Materials, 2012, 220/221/222/223: 251-258 48. Bilgaiyan S, Sagnika S, Das M. Workflow scheduling in cloud computing environment using cat swarm optimization. Proceedings of the 2014 Advance Computing Conference (IACC’14), Feb 21-22, 2014, Gurgaon, India. Piscataway, NJ, USA: IEEE, 2014: 680-685 49. Yang X S. Nature-inspired metaheuristic algorithms. 2nd ed. Beckington, UK: Luniver Press, 2010 50. Sahu R K, Panda S, Padhan S. A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. International Journal of Electrical Power and Energy Systems, 2015, 64: 9-23 51. Senthilnath J, Omkar S N, Mani V. Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation, 2011, 1(3): 164-171 52. Horng M H. Vector quantization using the firefly algorithm for image compression. Expert Systems with Applications, 2012, 39(1): 1078-1091 53. Coelho L D S, Mariani V C. Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Computers and Mathematics with Applications, 2012, 64(8): 2371-2382 54. Nandy S, Sarkar P P, Das A. Analysis of a nature inspired firefly algorithm based back-propagation neural network training. Computer Science, 2012, 8(22): 207-220 55. Yang X S. A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization: Proceedings of the 4th International Workshop on Nature Inspired Cooperative Strategies (NICSO'10), May 12-14, 2010, Granada, Spain. SCI 284. Berlin, Germany: Springer, 2010: 284: 65-74 56. Ghanem W A H M, Jantan A. An enhanced bat algorithm with mutation operator for numerical optimization problems. Neural Computing and Applications, 2019, 31(S1): 617-651 57. Satapathy S C, Sri Madhava Raja N, Rajinikanth V, et al. Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Computing and Applications, 2018, 29(12): 1285-1307 58. Dao T K, Pan T S, Nguyen T T, et al. Parallel bat algorithm for optimizing makespan in job shop scheduling problems. Journal of Intelligent Manufacturing, 2018, 29(2): 451-462 59. Hamidzadeh J, Sadeghi R, Namaei N. Weighted support vector data description based on chaotic bat algorithm. Applied Soft Computing, 2017, 60: 540-541 60. Basetti V, Chandel A K. Optimal PMU placement for power system observability using Taguchi binary bat algorithm. Measurement, 2017, 95: 8-20 61. Yang C G, Tu X Y, Chen J. Algorithm of marriage in honey bees optimization based on the wolf pack search. Proceedings of the 2007 International Conference on Intelligent Pervasive Computing (IPC’07), Oct 11-13, 2007, Jeju, Republic of Korea. Piscataway, NJ, USA: IEEE, 2007: 462-467 62. Liu C Y, Yan X H, Wu H. The wolf colony algorithm and its application. Chinese Journal of Electronics, 2011, 20(2): 212-216 63. Zhang L Y, Zhang L, Liu S, et al. Three-dimensional underwater path planning based on modified wolf pack algorithm. IEEE Access, 2017, 5: 22783-22795 64. Menassel R, Nini B, Mekhaznia T. An improved fractal image compression using wolf pack algorithm. Journal of Experimental and Theoretical Artificial Intelligence, 2018, 30(3): 1-11 65. Chen Y B, Yang D, Yu J Q. Multi-UAV task assignment with parameter and time-sensitive uncertainty using modified two-part wolf pack search algorithm. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(6): 2853-2872 66. Gupta S, Saurabh K. Modified artificial wolf pack method for maximum power point tracking under partial shading condition. Proceedings of the 2017 International Conference on Power and Embedded Drive Control (ICPEDC’17), Mar 16-18, 2017, Chennai, India. Piscataway, NJ, USA: IEEE, 2017: 60-65 67. Li C M, Du Y C, Wu J X, et al. Synchronizing chaotification with support vector machine and wolf pack search algorithm for estimation of peripheral vascular occlusion in diabetes mellitus. Biomedical Signal Processing and Control, 2014, 9(1): 45-55 68. Meng X B, Liu Y, Gao X Z, et al. A new bio-inspired algorithm: Chicken swarm optimization. Advances in Swarm Intelligence: Proceedings of the International Conference in Swarm Intelligence (ICSI’14): Oct 17-20, 2014, Hefei, China. LNCS 8794. Berlin, Germany: Springer, 2014: 86-94 69. Liang J H, Wang L F, Ma M, et al. A fast SAR image segmentation method based on improved chicken swarm optimization algorithm. Multimedia Tools and Applications, 2018, 77(24): 31787-31805 70. Shayokh M A, Shin S Y. Bio inspired distributed WSN localization based on chicken swarm optimization. Wireless Personal Communications, 2017, 97(4): 5691-5706 71. Semlali S C B, Riffi M E, Chebihi F. Optimization of makespan in job shop scheduling problem by hybrid chicken swarm algorithm. Advanced Intelligent Systems for Sustainable Development: Proceedings of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’18): Jul 12-14, 2018, Tangier, Morocco. AISC 915. Berlin, Germany: Springer, 2018: 358-369 72. Wu Z Q, Yu D Q, Kang X H. Application of improved chicken swarm optimization for MPPT in photovoltaic system. Optimal Control Applications and Methods, 2018, 39(1): 1029-1042 73. Shelokar P S, Siarry P, Jayaraman V K, et al. Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation, 2007, 188(1): 129-142 74. Zhang J Y, Teng F. The weak economy emergency logistics path optimization algorithm based on fish swarm ant colony algorithm. Emerging Research in Artificial Intelligence and Computational Intelligence: Proceedings of the 4th International Conference on Artificial Intelligence and Computational Intelligence (AICI’12), Oct 26-28, 2012, Chengdu, China. CCIS 315. Berlin, Germany: Springer, 2012: 350-356 75. Dorigo M, Gambardella L M. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66 76. Clerc M, Kennedy J. The particle swarm: explosion, stability and convergence in multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 20(1):1671-1676 77. Van den Bergh F, Engelbrecht A P. Training product unit networks using cooperative particle swarm optimisers. Proceedings of the International Joint Conference on Neural Networks (IJCNN’01), Jul 15-19, Washington, DC, USA, Piscataway, NJ, USA: IEEE, 2001: 126-131 78. Angeline P J. Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. Evolutionary Programming VII: Proceedings of the 7th International Conference on Evolutionary Programming (EP’98), Mar 23-25, 1998, San Diego, CA, USA. LNCS 1447. Berlin, Germany: Springer, 1998: 601-610 79. Xiao J M, Zheng X M, Wang X H, et al. A modified artificial fish-swarm algorithm. Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA’06): Vol 1, Jun 21-23, 2006, Dalian, China. Piscataway, NJ, USA: IEEE, 2006: 3456-3460 80. Jiang J Q, Bo Y L, Song C Y, et al. Hybrid algorithm based on particle swarm optimization and artificial fish swarm algorithm. Advances in Neural Networks: Proceedings of the 9th International Conference on Neural Networks (ISNN’12), Jul 11-14, 2012, Shenyang, China. LNCS 7367. Berlin, Germany: Springer, 2012: 607-614 81. Datta T, Misra I S, Mangaraj B B, et al. Improved adaptive bacteria foraging algorithm in optimization of antenna array for faster convergence. Progress in Electromagnetics Research C, 2008, 1: 143-157 82. Biswas A, Das S, Abraham A, et al. Stability analysis of the reproduction operator in bacterial foraging optimization. Theoretical Computer Science, 2010, 411(21): 2127-2139 83. Chatzis S P, Koukas S. Numerical optimization using synergetic swarms of foraging bacterial populations. Expert Systems with Applications, 2011, 38(12): 15332-15343 84. Bakwad K M, Pattnaik S S, Sohi B S, et al. Hybrid bacterial foraging with parameter free PSO. Proceedings of the 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC’09), Dec 9-11, 2009, Coimbatore, India. Piscataway, NJ, USA: IEEE, 2010: 1077-1081 85. Akay B, Karaboga D. Parameter tuning for the artificial bee colony algorithm. Computational Collective Intelligence—Semantic Web, Social Networks and Multiagent Systems: Proceedings of the 1st International Conference on Computational Collective Intelligence (ICCCI’09), Oct 5-7, 2009, Wroclaw, Poland. LNCS 5796. Berlin, Germany: Springer, 2009: 608-619 86. Elabd M. A hybrid ABC-SPSO algorithm for continuous function optimization. Proceedings of the 2011 IEEE Symposium on Swarm Intelligence (SSI’11), Apr 11-15, 2011, Paris, France. Piscataway, NJ, USA: IEEE, 2011: 6p 87. Li Z Y, Wang W Y, Yan Y Y, et al. PS-ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Expert Systems with Applications, 2015, 42(22): 8881-8895 88. Banharnsakun A, Achalakul T, Sirinaovakul B. The best-so-far selection in artificial bee colony algorithm. Applied Soft Computing Journal, 2011, 11(2): 2888-2901 89. Orouskhani M, Mansouri M, Teshnehlab M. Average-Inertia weighted cat swarm optimization. Advances in Swarm Intelligence: Proceedings of the 2nd International Conference on Swarm Intelligence (ICSI’11), Jun 12-15, 2011, Chongqing, China. LNCS 6728. Berlin, Germany: Springer, 2011: 321-328 90. Yang S D, Yi Y L, Shan Z Y. Chaotic cat swarm algorithms for global numerical optimization. Advanced Materials Research, 2012, 602/603/604:1782-1786 91. Tsai P W, Pan J S, Chen S M, et al. Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Systems with Applications, 2012, 39(7): 6309-6319 92. Yang S D, Yi Y L, Lu Y P. Homotopy-inspired cat swarm algorithm for global optimization. Advanced Materials Research, 2012, 602/603/604: 1793-1797 93. Liu C, Gao Z Q, Zhao W H. A new path planning method based on firefly algorithm. Proceedings of the 5th International Joint Conference on Computational Sciences and Optimization, Jun 23-26, 2012, Harbin, China. Piscataway, NJ, USA: IEEE, 2012: 775-778 94. Farahani S M, Abshouri A A, Nasiri B, et al. Some hybrid models to improve firefly algorithm performance. International Journal of Artificial Intelligence, 2012, 8(S12) : 97-117 95. Hassanzadeh T, Meybodi M R. A new hybrid algorithm based on firefly algorithm and cellular learning automata. Proceedings of the 20th Iranian Conference on Electrical Engineering (ICEE’12), May 15-17, 2012, Tehran, Iran. Piscataway, NJ, USA: IEEE, 2012: 628-633 96. Tsai P W, Pan J S, Liao B Y, et al. Bat algorithm inspired algorithm for solving numerical optimization problems. Applied Mechanics and Materials, 2012, 148/149(148): 134-137 97. Meng X B, Gao X Z, Liu Y, et al. A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization. Expert Systems with Applications, 2015, 42(17/18): 6350-6364 98. Xie J, Zhou Y G, Chen H. A novel bat algorithm based on differential operator and Levy flights trajectory. Computational Intelligence and Neuroscience, 2013, Article 453812 99. Liang W H, He J H, Wang S X, et al. Improved cluster collaboration algorithm based on wolf pack behavior. Cluster Computing, 2019, 22(S3): 6181-6196 100. Dong R Y, Wang S S, Wang G , et al. Hybrid optimization algorithm based on wolf pack search and local search for solving traveling salesman problem. Journal of Shanghai Jiaotong University (Science), 2019, 24(1): 41-47 101. Chen S L, Yang R Y, Yang R H, et al. A parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimization. Discrete Dynamics in Nature and Society, 2016, Article 3795961 102. Qu C W, Zhao S A, Fu Y M, et al. Chicken swarm optimization based on elite opposition-based learning. Mathematical Problems in Engineering, 2017, Article 2734362 103. Chen Y L, He P L, Zhang Y H. Combining penalty function with modified chicken swarm optimization for constrained optimization. Proceedings of the 1st International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME’15), Apr, 11-13, 2015, Chongqing, China. Amsterdam, Netherlands: Atlantis Press, 2015: 1899-1907 104. Wu D H, Xu S P, Kong F. Convergence analysis and improvement of the chicken swarm optimization. IEEE Access, 2016, 4: 9400-9412 105. Liang S, Feng T, Sun G. Sidelobe-level suppression for linear and circular antenna arrays via the cuckoo search chicken swarm optimization algorithm. IET Microwaves Antennas and Propagation, 2017, 11(2): 209-218

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

[1]	Huang Junfu, Wang Yawen, Gong Yunzhan, Jin Dahai. Cross-project software defect prediction based on multi-source data sets [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(4): 75-87.
[2]	. Imp Raft: a consensus algorithm based on Raft and storage compression consensus for IoT scenario [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(3): 53-61.
[3]	. Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(1): 38-50.
[4]	Han Xu, Wang Hongsu, Zhang Sanqian, Fu Qunchao, Liu Jun. Sentence segmentation for classical Chinese based on LSTM with radical embedding [J]. The Journal of China Universities of Posts and Telecommunications, 2019, 26(2): 1-8.
[5]	Sun Jiaze, Ling Beilei. Density PSO-based software module clustering algorithm [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2018, 25(4): 38-47.
[6]	ZHANG Yi, HE Chun-jiang, LUO Yuan, CHEN Kai, XING Wu-chao. Improved perceptually non-uniform spectral compression for robust speech recognition [J]. Acta Metallurgica Sinica(English letters), 2013, 20(4): 122-126.
[7]	ZHANG Jun-guang; LÜ Ting-jie; ZHAO Yu-mei. Study on top-down estimation method of software project planning. [J]. Acta Metallurgica Sinica(English letters), 2006, 13(2): 108-111.

Research on swarm intelligence optimization algorithm

PDF

Knowledge

Abstract

Cite this article

share this article

References 1.

Related Articles 7

Recommended Articles

Metrics

Comments