Abstract
For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.
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Abbreviations
- \( x_{i}\) :
-
The ith dimension of solution \(\mathbf {x}\)
- \(p_{\text {generation}}\) :
-
Pre-determined probability, which is used to determine a new individual being generated by one or two “old” individuals
- \(p_{\text {oneCluster}}\) :
-
Pre-determined probability, which is used to determine the cluster center or another normal individual will be chosen in one cluster generation case
- \(p_{\text {twoCluster}}\) :
-
Pre-determined probability, which is used to determine the cluster center or another normal individual will be chosen in two clusters generation case
- r :
-
Random value in the range [0, 1)
- \(\xi (t)\) :
-
Step size function
- \(f(\mathbf {x})\) :
-
Fitness value: objective function value of \(\mathbf {x}\)
- t :
-
Iteration number
- T :
-
Maximum number of iteration
- S :
-
Population size: the number of solutions in a population
- D :
-
Number of decision variables
References
Arsuaga-Ríos M, Vega-Rodríguez MA (2014a) Cost optimization based on brain storming for grid scheduling. In: Proceedings of the 2014 4th international conference on innovative computing technology (INTECH), pp 31–36
Arsuaga-Ríos M, Vega-Rodríguez MA (2014b) Multi-objective energy optimization in grid systems from a brain storming strategy. Soft computing pp. 1–14
Cao Z, Shi Y, Rong X, Liu B, Du Z, Yang B (2015) Random grou** brain storm optimization algorithm with a new dynamically changing step size. In: Tan Y, Shi Y, Buarque F, Gelbukh A, Das S, Engelbrecht A (eds) Advances in swarm and computational intelligence, lecture notes in computer science, vol 9140. Springer, New York, pp 357–364
Cao Z, Wang L, Hei X, Shi Y, Rong X (2015) An improved brain storm optimization with differential evolution strategy for applications of ANNs. Math Problems Eng 2015:1–18
Chen J, Cheng S, Chen Y, **e Y, Shi Y (2015) Enhanced brain storm optimization algorithm for wireless sensor networks deployment. In: Tan Y, Shi Y, Buarque F, Gelbukh A, Das A, Swagatamand Engelbrecht (eds) Advances in swarm and computational intelligence, lecture notes in computer science, vol 9140. Springer, Berlin, pp 373–381
Chen J, **e Y, Ni J (2014) Brain storm optimization model based on uncertainty information. In: 2014 10th International conference on computational intelligence and security, pp 99–103
Cheng S, Shi Y, Qin Q (2012) Dynamical exploitation space reduction in particle swarm optimization for solving large scale problems. In: Proceedings of 2012 IEEE congress on evolutionary computation. CEC 2012IEEE, Brisbane, Australia, pp 3030–3037
Cheng S, Shi Y, Qin Q (2012) Population diversity based study on search information propagation in particle swarm optimization. In: Proceedings of 2012 IEEE congress on evolutionary computation. CEC 2012IEEE, Brisbane, Australia, pp 1272–1279
Cheng S, Shi Y, Qin Q, Gao S (2013) Solution clustering analysis in brain storm optimization algorithm. In: Proceedings of the 2013 IEEE symposium on swarm intelligence., SIS 2013IEEE, Singapore, pp 111–118
Cheng S, Shi Y, Qin Q, Ting TO, Bai R (2014) Maintaining population diversity in brain storm optimization algorithm. In: Proceedings of 2014 IEEE congress on evolutionary computation. CEC 2014IEEE, Bei**g, China, pp 3230–3237
Cheng S, Shi Y, Qin Q, Zhang Q, Bai R (2014) Population diversity maintenance in brain storm optimization algorithm. J Artif Intell Soft Comput Res 4(2):83–97
Duan H, Li C (2015) Quantum-behaved brain storm optimization approach to solving loney’s solenoid problem. IEEE Trans Magn 51(1):1–7
Duan H, Li S, Shi Y (2013) Predator-prey brain storm optimization for DC brushless motor. IEEE Trans Magn 49(10):5336–5340
Guo X, Wu Y, **e L (2014) Modified brain storm optimization algorithm for multimodal optimization. In: Tan Y, Shi Y, Coello CAC (eds) Advances in swarm intelligence, lecture notes in computer science, vol 8795. Springer, New York, pp 340–351
Guo X, Wu Y, **e L, Cheng S, **n J (2015) An adaptive brain storm optimization algorithm for multiobjective optimization problems. In: Tan Y, Shi Y, Buarque F, Gelbukh A, Das S, Engelbrecht A (eds) Advances in swarm and computational intelligence, lecture notes in computer science, vol 9140. Springer, New York, pp 365–372
Jadhav H, Sharma U, Patel J, Roy R (2012) Brain storm optimization algorithm based economic dispatch considering wind power. In: Proceedings of the 2012 IEEE international conference on power and energy (PECon 2012). Kota Kinabalu, Malaysia, pp 588–593
Jia Z, Duan H, Shi Y (2015) Hybrid brain storm optimization and simulated annealing algorithm for continuous optimization problems. Int J Bio-Inspired Comput (in press)
Jordehi AR (2015) Brainstorm optimisation algorithm (BSOA): an efficient algorithm for finding optimal location and setting of facts devices in electric power systems. Electr Power Energy Syst 69:48–57
Krishnanand K, Hasani SMF, Panigrahi BK, Panda SK (2013) Optimal power flow solution using self-evolving brain-storming inclusive teaching-learning-based algorithm. In: Tan Y, Shi Y, Mo H (eds) Advances in swarm intelligence, vol 7928. Lecture Notes in Computer Science. Springer, Berlin, pp 338–345
Lenin K, Reddy BR, Kalavathi MS (2014) Brain storm optimization algorithm for solving optimal reactive power dispatch problem. Int J Res Electron Commun Technol 1(3):25–30
Li J, Duan H (2015) Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system. Aerosp Sci Technol 42:187–195
Li L, Tang K (2015) History-based topological speciation for multimodal optimization. IEEE Trans Evol Comput 19(1):136–150
Mafteiu-Scai LO (2015) A new approach for solving equations systems inspired from brainstorming. Int J New Comput Archit Appl 5(1):10–18
Martens D, Baesens B, Fawcett T (2011) Editorial survey: swarm intelligence for data mining. Mach Learn 82(1):1–42
Murphy KP (2012) Machine learning: a probabilistic perspective. Adaptive computation and machine learning series. The MIT Press, Cambridge, Massachusetts
Qiu H, Duan H (2014) Receding horizon control for multiple UAV formation flight based on modified brain storm optimization. Nonlinear Dyn 78(3):1973–1988
Qiu H, Duan H, Shi Y (2015) A decoupling receding horizon search approach to agent routing and optical sensor tasking based on brain storm optimization. Optik 126:690–696
Ramanand K, Krishnanand K, Panigrahi BK, Mallick MK (2012) Brain storming incorporated teaching-learning-based algorithm with application to electric power dispatch. In: Panigrahi BK, Das S, Suganthan PN, Nanda PK (eds) Swarm, evolutionary, and memetic computing, vol 7677. Lecture Notes in Computer Science. Springer, Berlin, pp 476–483
Shen L (2014) Research and application of v-SVR based on brain storm optimization algorithm. Master’s thesis, Lanzhou University
Shi Y (2011) Brain storm optimization algorithm. In: Tan Y, Shi Y, Chai Y, Wang G (eds) Advances in swarm intelligence, lecture notes in computer science, vol 6728. Springer, Berlin, pp 303–309
Shi Y (2011) An optimization algorithm based on brainstorming process. Int J Swarm Intell Res 2(4):35–62
Shi Y (2014) Developmental swarm intelligence: developmental learning perspective of swarm intelligence algorithms. Int J Swarm Intell Res 5(1):36–54
Shi Y (2015) Brain storm optimization algorithm in objective space. In: Proceedings of 2015 IEEE congress on evolutionary computation, (CEC 2015). IEEE, Sendai, Japan, pp 1227–1234
Shi Y, Xue J, Wu Y (2013) Multi-objective optimization based on brain storm optimization algorithm. Int J Swarm Intell Res 4(3):1–21
Sun C, Duan H, Shi Y (2013) Optimal satellite formation reconfiguration based on closed-loop brain storm optimization. IEEE Comput Intell Mag 8(4):39–51
Sun Y (2014) A hybrid approach by integrating brain storm optimization algorithm with grey neural network for stock index forecasting. Abstract Appl Anal 2014:1–10
Tan Y (2015) Fireworks algorithm: a novel swarm intelligence optimization method. Springer, New York
Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Tan Y, Shi Y, Tan KC (eds) Advances in swarm intelligence, vol 6145. Lecture Notes in Computer Science. Springer, Berlin, pp 355–364
Ting TO, Yang XS, Cheng S, Huang K (2015) Hybrid metaheuristic algorithms: past, present, and future. In: Yang XS (ed) Recent advances in swarm intelligence and evolutionary computation, studies in computational intelligence (SCI), vol 585. Springer, New York, pp 71–83
**e L, Wu Y (2014) A modified multi-objective optimization based on brain storm optimization algorithm. In: Tan Y, Shi Y, Coello C (eds) Advances in swarm intelligence. Lecture Notes in Computer Science, vol 8795. Springer, New York, pp 328–339
Xue J, Wu Y, Shi Y, Cheng S (2012) Brain storm optimization algorithm for multi-objective optimization problems. In: Tan Y, Shi Y, Ji Z (eds) Advances in swarm intelligence, vol 7331. Lecture Notes in Computer Science. Springer, Berlin, pp 513–519
Yang P, Tang K, Lu X (2015) Improving estimation of distribution algorithm on multimodal problems by detecting promising areas. IEEE Trans Cybern 45(8):1438–1449
Yang Y, Shi Y, **a S (2013) Discussion mechanism based brain storm optimization algorithm. J Zhejiang Univ (Eng Sci) 47:1705–1711
Yang Y, Shi Y, **a S (2014) Advanced discussion mechanism-based brain storm optimization algorithm. Soft computing, pp 1–11
Yang Z, Shi Y (2015) Brain storm optimization with chaotic operation. In: Proceedings of the 7th international conference on advanced computational intelligence (ICACI 2015), pp 111–115. IEEE
Zhan ZH, Chen WN, Lin Y, Gong YJ, long Li, Y, Zhang J (2013) Parameter investigation in brain storm optimization. In: Proceedings of the 2013 IEEE symposium on swarm intelligence (SIS 2013), pp 103–110
Zhan Zh, Zhang J, Shi Yh, Liu Hl (2012) A modified brain storm optimization. In: Proceedings of the 2012 IEEE congress on evolutionary computation (CEC), pp 1–8
Zhang GW, Zhan ZH, Du KJ (2014) Chen WN (2014) Normalization group brain storm optimization for power electronic circuit optimization. In: Proceedings of the 2014 conference companion on genetic and evolutionary computation companion. GECCO Comp ’14ACM, New York, NY, USA, pp 183–184
Zhao X (2013) Research and application of brain storm optimization algorithm. Master’s thesis, **’an University of Technology
Zhou D, Shi Y, Cheng S (2012) Brain storm optimization algorithm with modified step-size and individual generation. In: Tan Y, Shi Y, Ji Z (eds) Advances in swarm intelligence, vol 7331. Lecture Notes in Computer ScienceSpringer, Berlin, pp 243–252
Zhou H, Jiang M, Ben X (2014) Niche brain storm optimization algorithm for multi-peak function optimization. Adv Mater Res 989–994:1626–1630
Zhu, H., Shi, Y.: Brain storm optimization algorithms with \(k\)-medians clustering algorithm. In: Proceedings of the 7th international conference on advanced computational intelligence (ICACI 2015), pp 107–110. IEEE
Acknowledgments
This work is partially supported by National Natural Science Foundation of China under Grant Number 61403121, 71402103, 61273367, 71240015; the PAPD and CICAEET project; the Foundation for Distinguished Young Talents in Higher Education of Guangdong, China, under Grant 2012WYM_0116; and the MOE Youth Foundation Project of Humanities and Social Sciences at Universities in China under grant 13YJC630123; China Postdoctoral Science Foundation Funded Project (No. 2015M580053); and The Youth Foundation Project of Humanities and Social Sciences in Shenzhen University under grant 14QNFC28; and by Ningbo Science & Technology Bureau (Science and Technology Project Number 2012B10055).
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Cheng, S., Qin, Q., Chen, J. et al. Brain storm optimization algorithm: a review. Artif Intell Rev 46, 445–458 (2016). https://doi.org/10.1007/s10462-016-9471-0
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DOI: https://doi.org/10.1007/s10462-016-9471-0