Abstract
The group intelligent optimization algorithm provides some new ideas for solving many practical problems. These algorithms have stronger robustness and stronger search ability, and are easy to implement in parallel. It is easy to combine with other algorithms to improve the performance of the algorithm and solve complex practical problems. The effect of the experiment is more obvious. In this paper, the improvement and application of particle swarm optimization algorithm, fireworks algorithm and artificial bee colony algorithm in intelligent algorithm are reviewed, and the advantages and disadvantages are analyzed. The future development of intelligent algorithm is prospected.
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References
Guo Y, Song Y, Song C, Liu L, Ren H (2017) A particle swarm target tracking algorithm with improved inertia weight. Foreign Electron Meas Technol 36(01):17–20 (in Chinese)
Zhang L, Tang Y, Hua C, Guan X (2015) A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Appl Soft Comput 28:138–149
Zhang Q (2017) Study on particle swarm optimization algorithm and difference algorithm. Shandong University (in Chinese)
Zheng Y, Liu Y, Lu W et al (2016) A hybrid PSO-GA method for composing heterogeneous groups in collaborative learning In: International conference on computer science & education. IEEE
Wang Y, Cao J, Zhang F (2017) Improved chaotic particle swarm optimization algorithm based on simulated annealing. J Inner Mongolia Univ Technol (Nat Sci Ed) 6(03):173–177 (in Chinese)
Ren C, Ge H, Yang J, Yuan Y (2015) Artificial bee colony particle swarm optimization algorithm introducing mixed frog leap search strategy. Comput Eng Appl 51(22):38–41 (in Chinese)
Brabazon A, O’Neill M, McGarraghy S (2015) Bacterial foraging algorithms. Natural computing algorithms. Springer, Heidelberg, pp 187–199
Liu H, Shen X, Qu H, Wang P (2017) Study on temperature control of PID biogas dry fermentation based on particle swarm optimization. Comput Eng Des 38(03):784–788 (in Chinese)
Liu J, Mei Q, Yang D (2017) Neural network modeling of extreme speed learning machine based on blind moving particle swarm frequency decomposition. Inf Control 46(01):60–64 (in Chinese)
Zheng E, Jiang S (2017) Fractional order PID control based on improved particle swarm optimization algorithm. Control Eng 24(10):2082–2087 (in Chinese)
Yang Z, Chen Y (2016) Improved particle swarm optimization algorithm and its application in PID tuning. Control Eng 23(02):161–166 (in Chinese)
Zhao H, Li S (2016) Research on cloud computing resource scheduling method based on particle swarm optimization and RBF neural network. Comput Sci 43(03):113–117 (in Chinese)
Wang D, Liu X (2015) Resource scheduling of cloud computing platform based on improved particle swarm optimization algorithm. Appl Res Comput 32(11):3230–3234 (in Chinese)
Jin Y, Xue D, Zhang X, Li W (2018) Image retrieval research based on fusion-based scale-free particle swarm optimization algorithm. Microelectron Comput 35(01):36–40 (in Chinese)
Das AK, Biswas D, Halder S (2017) Analysis of de-noising techniques of non-stationary ECG signal based on wavelet and PSO optimized parameters for Savitzky golay filter. In: International conference on research in computational intelligence & communication networks, pp 39–44
Cheng B, Lu H, Huang Y, Xu K (2017) An adaptive excellent coefficient particle swarm optimization algorithm for solving TSP. J Comput Appl 37(03):750–754 (in Chinese)
Wu G (2016) Research on path planning problem based on particle swarm optimization algorithm. Yanshan University (in Chinese)
Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: International conference in swarm intelligence. Springer, Heidelberg, pp 55–364
Zheng S, Janecek A, Li J et al (2014) Dynamic search in fireworks algorithm. In: IEEE evolutionary computation, pp 3222–3229
Liu J, Zheng S, Tan Y (2013) The improvement on controlling exploration and exploitation of firework algorithm 7928:11–23
Zheng S, Janecek A, Tan Y (2013) Enhanced fireworks algorithm. In: IEEE congress on evolutionary computation (CEC). IEEE, pp 2069–2077
Li J, Zheng S, Tan Y (2014) Adaptive fireworks algorith. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 3214–3221
Yu C, Kelley LC, Tan Y (2015) Dynamic search fireworks algorithm with covariance mutation for solving the CEC 2015 learning based competition problems. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 1106–1112
Zheng YJ, Xu XL, Ling HF et al (2015) A hybrid fireworks optimization method with differential evolution operators. Neurocomputing 148:75–82
Yu C, Kelley L, Zheng S et al (2014) Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 3238–3245
Zheng S, Li J, Janecek A et al (2017) A cooperative framework for fireworks algorithm. IEEE/ACM Trans Comput Biol Bioinform 14(1):27–41
Li J, Zheng S, Tan Y (2017) The effect of information utilization: introducing a novel guiding spark in the fireworks algorithm. IEEE Trans Evol Comput 21(1):153–166
Li J, Tan Y (2018) The bare bones fireworks algorithm: a minimalist global optimizer. Appl Soft Comput 62:454–462
Li J, Tan Y (2017) Loser-out tournament based fireworks algorithm for multi-modal function optimization. IEEE Trans Evol Comput 22(5):679–691
Imran AM, Kowsalya M (2014) A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using Fireworks Algorithm. Int J Electr Power Energy Syst 62:312–322
Bouarara HA, Hamou RM, Amine A et al (2015) A fireworks algorithm for modern web information retrieval with visual results mining. Int J Swarm Intell Res (IJSIR) 6(3):1–23
Shi J, Xu B, Zhu P et al (2016) Multi-task firework algorithm for cell tracking and contour estimation. In: 2016 International conference on control, automation and information sciences (ICCAIS). IEEE, pp 27–31
Mnif M, Bouamama S (2017) Firework algorithm for multi-objective optimization of a multimodal transportation network problem. Procedia Comput Sci 112:1670–1682
Taidi Z, Benameur L, Chentoufi JA (2017) A fireworks algorithm for solving travelling salesman problem. Int J Comput Syst Eng 3(3):157–162
Janecek A, Tan Y (2011) Iterative improvement of the Multiplicative Update NMF algorithm using nature-inspired optimization. In: Seventh international conference on natural computation, Shanghai, China, pp 1668–1672
Gao HY, Diao M (2011) Clutural firework algorithm and its application for digital filters design. Int J Model Ident Control 14(4):324–331
Xue J, Wang W, Meng X et al (2017) Binary reverse learning fireworks algorithm for solving multi-dimensional Knapsack problem. Syst Eng Electron 39(2):451–458 (in Chinese)
Cao L, Ye C, Huang X (2016) Application of chaotic fireworks algorithm to solve the problem of replacement flow shop. Comput Appl Softw 33(11):188–192 (in Chinese)
Bao X, Ye C, Huang X (2017) Study on solving JSP problem by fireworks algorithm. Comput Eng Appl 53(3):247–252 (in Chinese)
Liu C (2016) Research on fuzzy modeling method based on fireworks algorithm. Zhengzhou University (in Chinese)
Ye Z, Yuan M, Cheng S et al (2013) A new firefly explosive new immune planning algorithm for mobile robots. Comput Simul 30(3):323–326 (in Chinese)
Zhu X, Liu C, Guo Y (2015) Design of fuzzy classification system based on fireworks algorithm and differential evolution algorithm. J Zhengzhou Univ (Eng Sci Ed) 36(6):47–51 (in Chinese)
Huang W, Guo F (2017) Cloud computing multi-objective task scheduling based on fireworks algorithm. Comput Appl Res 34(6):1718–1720 (in Chinese)
Wu Q (2016) Positive and negative quantitative association rule mining algorithm based on multi-target fireworks algorithm. Nanchang University (in Chinese)
Wu Q, Zeng Q (2017) Association rules mining based on multi-objective fireworks algorithm. Pattern Recogn Artif Intell 30(4):365–376 (in Chinese)
Chen H, Qi L, Ye Z (2018) Otsu multi-valued image segmentation method based on fireworks algorithm. J Hubei Univ Technol 33(1):55–58 (in Chinese)
Yan L (2017) Application of fireworks algorithm in image processing. Hubei University of Technology (in Chinese)
Li X, Cui Y (2016) Fireworks clustering algorithm based on binary code. Appl Technol 43(1):36–39 (in Chinese)
Zhang Y, Wu J, Zhao M et al (2016) Web service composition optimization based on improved fireworks algorithm. Comput Integr Manuf Syst 22(2):422–432 (in Chinese)
Ma W, Zhao Y, Zhang W et al (2018) Multi-UAV task assignment based on adaptive fireworks algorithm. Electro-Opt Control 1:37–43 (in Chinese)
Xu X, Liu Z, Wang Z et al (2015) The artificial bee colony algorithm paradigm of S-ABC-oriented service domain. Chin J Comput 38(11):2301–2317 (in Chinese)
Hong PN, Ahn CW (2016) Linkage artificial bee colony for solving linkage problems. Expert Syst Appl 61:378–385
Karaboga D, Kaya E (2016) An adaptive and hybrid artificial bee colony algorithm(a ABC) for ANFIS training. Appl Soft Comput 49:423–436
Wen S, Xia J, Gao R et al (2016) Improved artificial bee colony algorithm based optimal navigation path for mobile robot. In: 2016 12th World congress on intelligent control and automation, Guilin, China, pp 2928–2933
Ma R, Wu H, Ding L (2016) Optimization design of LQG/LTR control law for unmanned helicopter based on artificial bee colony algorithm. Control Decis Making 31:1–7 (in Chinese)
Hu R, Cheng T, Xu W et al (2016) Seismic reliability analysis of slope based on artificial bee colony algorithm. J Wuhan Univ (Eng Sci) 49(5):796–800
Kiran MS (2015) The continuous artificial bee colony algorithm for binary optimization. Appl Soft Comput 33(4):15–23
Zhuo T, Zhan Y (2014) Cloud computing resource scheduling model based on artificial bee colony algorithm. Microelectron Comput 31(7):147–151 (in Chinese)
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Project No. 51678375), Natural Science Foundation of Liaoning Province (Project No. 2015020603), and the basic scientific research project of Liaoning Higher Education (Project No. LJZ2017009).
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Chang, C., Wu, X. (2020). A Survey of Group Intelligence Optimization Algorithms. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_136
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DOI: https://doi.org/10.1007/978-3-030-15235-2_136
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