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Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization

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Brain Storm Optimization Algorithms

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 23))

Abstract

With the development of computational intelligence, many intelligence algorithms have attracted the attention of the scientific community, and a great deal of work on optimizing these algorithms is in full swing. One of the optimization techniques that we focus on is the hybridization of algorithms. Brain storm optimization algorithm (BSO), belonging to the swarm intelligence algorithms, is proposed by taking inspiration of human brain storming behavior. Meanwhile, the covariance matrix adaptive evolutionary strategy algorithm (CMA-ES) which belongs to the field of evolutionary strategy is also concerned. The purpose of this paper is to combine the search capability of BSO with the search efficiency of CMA-ES to achieve a relatively balanced and effective solution.

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References

  1. Boussaid, I., Chatterjee, A., Siarry, P., Ahmed-Nacer, M.: Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks. IEEE Trans. Veh. Technol. 60(5), 2347–2353 (2011)

    Article  Google Scholar 

  2. Cao, Z., Hei, X., Wang, L., Shi, Y., Rong, X.: An improved brain storm optimization with differential evolution strategy for applications of ANNs. Math. Probl. Eng. (2015)

    Google Scholar 

  3. Chang, L., Liao, C., Lin, W., Chen, L.L., Zheng, X.: A hybrid method based on differential evolution and continuous ant colony optimization and its application on wideband antenna design. Prog. Electromagnet. Res. 122, 105–118 (2012)

    Article  Google Scholar 

  4. Cheng, J., Cheng, J., Zhou, M., Liu, F., Gao, S., Liu, C.: Routing in internet of vehicles: a review. IEEE Trans. Intell. Transp. Syst. 16(5), 2339–2352 (2015)

    Article  Google Scholar 

  5. Cheng, J., Mi, H., Huang, Z., Gao, S., Zang, D., Liu, C.: Connectivity modeling and analysis for internet of vehicles in urban road scene. IEEE Access 6, 2692–2702 (2018)

    Article  Google Scholar 

  6. Cheng, J., Wu, X., Zhou, M., Gao, S., Huang, Z., Liu, C.: A novel method for detecting new overlapping community in complex evolving networks. IEEE Trans. Syst. Man Cybern. Syst. (2018). https://doi.org/10.1109/TSMC.2017.2779138

  7. Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. 46(4), 445–458 (2016)

    Article  Google Scholar 

  8. Deng, W., Chen, R., Gao, J., Song, Y., Xu, J.: A novel parallel hybrid intelligence optimization algorithm for a function approximation problem. Computers Math. Appl. 63(1), 325–336 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Deng, W., Chen, R., He, B., Liu, Y., Yin, L., Guo, J.: A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput. 16(10), 1707–1722 (2012)

    Article  Google Scholar 

  10. Deng, W., Li, W., Yang, X.H.: A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction. Expert Syst. Appl. 38(4), 4198–4205 (2011)

    Article  Google Scholar 

  11. Gao, S., Song, S., Cheng, J., Todo, Y., Zhou, M.: Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(4), 1365–1378 (2018)

    Article  Google Scholar 

  12. Gao, S., Vairappan, C., Wang, Y., Cao, Q., Tang, Z.: Gravitational search algorithm combined with chaos for unconstrained numerical optimization. Appl. Math. Comput. 231, 48–62 (2014)

    MathSciNet  MATH  Google Scholar 

  13. Gao, S., Wang, W., Dai, H., Li, F., Tang, Z.: Improved clonal selection algorithm combined with ant colony optimization. IEICE Trans. Inf. Syst. 91(6), 1813–1823 (2008)

    Article  Google Scholar 

  14. Gao, S., Wang, Y., Cheng, J., Inazumi, Y., Tang, Z.: Ant colony optimization with clustering for solving the dynamic location routing problem. Appl. Math. Comput. 285, 149–173 (2016)

    MathSciNet  MATH  Google Scholar 

  15. Gao, S., Wang, Y., Wang, J., Cheng, J.: Understanding differential evolution: a Poisson law derived from population interaction network. J. Comput. Sci. 21, 140–149 (2017)

    Article  Google Scholar 

  16. Gao, S., Zhang, J., Wang, X., Tang, Z.: Multi-layer neural network learning algorithm based on random pattern search method. Int. J. Innov. Comput. Inf. Control 5(2), 489–502 (2009)

    Google Scholar 

  17. Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., Wang, J.: Dendritic neural model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans. Neural Network. Learn. Syst. (2018). https://doi.org/10.1109/TNNLS.2018.2846646

    Article  Google Scholar 

  18. Gao, W., Liu, S., Huang, L.: Enhancing artificial bee colony algorithm using more information-based search equations. Inform. Sci. 270, 112–133 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inform. Sci. 180(10), 2044–2064 (2010)

    Article  Google Scholar 

  20. Garg, H.: A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274, 292–305 (2016)

    MathSciNet  MATH  Google Scholar 

  21. Guvenc, U., Duman, S., Saracoglu, B., Ozturk, A.: A hybrid GA-PSO approach based on similarity for various types of economic dispatch problems. Elektronika ir Elektrotechnika 108(2), 109–114 (2011)

    Article  Google Scholar 

  22. Hansen, N.: The CMA evolution strategy: a comparing review. In: Towards a New Evolutionary Computation, pp. 75–102. Springer (2006)

    Google Scholar 

  23. Ji, J., Gao, S., Wang, S., Tang, Y., Yu, H., Todo, Y.: Self-adaptive gravitational search algorithm with a modified chaotic local search. IEEE Access 5, 17881–17895 (2017)

    Article  Google Scholar 

  24. Ji, J., Song, S., Tang, C., Gao, S., Tang, Z., Todo, Y.: An artificial bee colony algorithm search guided by scale-free networks. Inform. Sci. 473, 142–165 (2019)

    Article  Google Scholar 

  25. Jiao, L., Wang, L.: A novel genetic algorithm based on immunity. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 30(5), 552–561 (2000)

    Article  Google Scholar 

  26. Juang, C.F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 34(2), 997–1006 (2004)

    Article  Google Scholar 

  27. Kämpf, J.H., Robinson, D.: A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential. Appl. Soft Comput. 9(2), 738–745 (2009)

    Article  Google Scholar 

  28. Kao, Y.T., Zahara, E.: A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Comput. 8(2), 849–857 (2008)

    Article  Google Scholar 

  29. Kaveh, A., Ghazaan, M.I.: Hybridized optimization algorithms for design of trusses with multiple natural frequency constraints. Adv. Eng. Softw. 79, 137–147 (2015)

    Article  Google Scholar 

  30. Kaveh, A., Talatahari, S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87(5–6), 267–283 (2009)

    Article  Google Scholar 

  31. Lazzús, J.A., Rivera, M., López-Caraballo, C.H.: Parameter estimation of lorenz chaotic system using a hybrid swarm intelligence algorithm. Phys. Lett. A 380(11–12), 1164–1171 (2016)

    Article  MathSciNet  Google Scholar 

  32. Lee, Z.J., Lee, C.Y., Su, S.F.: An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem. Appl. Soft Comput. 2(1), 39–47 (2002)

    Article  Google Scholar 

  33. Lee, Z.J., Su, S.F., Chuang, C.C., Liu, K.H.: Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl. Soft Comput. 8(1), 55–78 (2008)

    Article  Google Scholar 

  34. Li, Z., Wang, W., Yan, Y., Li, Z.: PS-ABC: a hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Expert Syst. Appl. 42(22), 8881–8895 (2015)

    Article  Google Scholar 

  35. Lien, L.C., Cheng, M.Y.: A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization. Expert Syst. Appl. 39(10), 9642–9650 (2012)

    Article  Google Scholar 

  36. Lieu, Q.X., Do, D.T., Lee, J.: An adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraints. Comput. Struct. 195, 99–112 (2018)

    Article  Google Scholar 

  37. Lin, Q., Chen, J., Zhan, Z.H., Chen, W.N., Coello, C.A.C., Yin, Y., Lin, C.M., Zhang, J.: A hybrid evolutionary immune algorithm for multiobjective optimization problems. IEEE Trans. Evol. Comput. 20(5), 711–729 (2016)

    Google Scholar 

  38. Liu, C., Duan, H., Qingtian, Z., Zhou, M., Lu, F., Cheng, J.: Towards comprehensive support for privacy preservation cross-organization business process mining. IEEE Trans. Serv. Comput. (2016). https://doi.org/10.1109/TSC.2016.2617331

  39. Liu, C., Zeng, Q., Duan, H., Zhou, M., Lu, F., Cheng, J.: E-net modeling and analysis of emergency response processes constrained by resources and uncertain durations. IEEE Trans.Syst. Man Cybern. Syst. 45(1), 84–96 (2015)

    Article  Google Scholar 

  40. Liu, C., Zhang, J., Li, G., Gao, S., Zeng, Q.: A two-layered framework for the discovery of software behavior: a case study. IEICE Trans. Inform. Syst. 101(8), 2005–2014 (2018)

    Article  Google Scholar 

  41. Liu, Y., Cheng, D., Wang, Y., Cheng, J., Gao, S.: A novel method for predicting vehicle state in internet of vehicles. Mob. Inform. Syst. (2018)

    Google Scholar 

  42. Luengo, J., García, S., Herrera, F.: A study on the use of statistical tests for experimentation with neural networks: analysis of parametric test conditions and non-parametric tests. Expert Syst. Appl. 36(4), 7798–7808 (2009)

    Article  Google Scholar 

  43. Marinakis, Y., Marinaki, M.: A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem. Comput. Oper. Res. 37(3), 432–442 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  44. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  45. Nguyen, T.T., Li, Z., Zhang, S., Truong, T.K.: A hybrid algorithm based on particle swarm and chemical reaction optimization. Expert Syst. Appl. 41(5), 2134–2143 (2014)

    Article  Google Scholar 

  46. Niknam, T., Amiri, B.: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl. Soft Comput. 10(1), 183–197 (2010)

    Article  Google Scholar 

  47. Nwankwor, E., Nagar, A.K., Reid, D.: Hybrid differential evolution and particle swarm optimization for optimal well placement. Comput. Geosci. 17(2), 249–268 (2013)

    Article  MATH  Google Scholar 

  48. Pan, G., Li, K., Ouyang, A., Li, K.: Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving tsp. Soft Comput. 20(2), 555–566 (2016)

    Article  Google Scholar 

  49. Panda, S., Kiran, S.H., Dash, S.S., Subramani, C.: A PD-type multi input single output sssc damping controller design employing hybrid improved differential evolution-pattern search approach. Appl. Soft Comput. 32, 532–543 (2015)

    Article  Google Scholar 

  50. Panda, S., Yegireddy, N.K.: Multi-input single output SSSC based damping controller design by a hybrid improved differential evolution-pattern search approach. ISA Trans. 58, 173–185 (2015)

    Article  Google Scholar 

  51. Shelokar, P., Siarry, P., Jayaraman, V.K., Kulkarni, B.D.: Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl. Math. Comput. 188(1), 129–142 (2007)

    MathSciNet  MATH  Google Scholar 

  52. Shi, Y.: Brain storm optimization algorithm. In: International Conference in Swarm Intelligence, pp. 303–309. Springer (2011)

    Google Scholar 

  53. Shuang, B., Chen, J., Li, Z.: Study on hybrid PS-ACO algorithm. Appl. Intell. 34(1), 64–73 (2011)

    Article  Google Scholar 

  54. Soleimani, H., Kannan, G.: A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Modell. 39(14), 3990–4012 (2015)

    Article  MathSciNet  Google Scholar 

  55. Song, S., Ji, J., Chen, X., Gao, S., Tang, Z., Todo, Y.: Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction. Appl. Soft Comput. (2018). https://doi.org/10.1016/j.asoc.2018.07.042

    Article  Google Scholar 

  56. Song, Z., Gao, S., Yu, Y., Sun, J., Todo, Y.: Multiple chaos embedded gravitational search algorithm. IEICE Trans. Inform. Syst. 100(4), 888–900 (2017)

    Article  Google Scholar 

  57. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optimization 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  58. Sun, J., Gao, S., Dai, H., Cheng, J., Zhou, M., Wang, J.: Bi-objective elite differential evolution for multivalued logic networks. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/TCYB.2018.2868493

  59. Sun, Y.: A hybrid approach by integrating brain storm optimization algorithm with grey neural network for stock index forecasting. Abstract Appl. Anal. 2014. Hindawi (2014)

    Google Scholar 

  60. Tran, D.H., Cheng, M.Y., Cao, M.T.: Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem. Knowl. Based Syst. 74, 176–186 (2015)

    Article  Google Scholar 

  61. Trivedi, A., Srinivasan, D., Biswas, S., Reindl, T.: Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem. Swarm Evol. Comput. 23, 50–64 (2015)

    Article  Google Scholar 

  62. Wang, J., Cen, B., Gao, S., Zhang, Z., Zhou, Y.: Cooperative evolutionary framework with focused search for many-objective optimization. IEEE Trans. Emerg. Top. Comput. Intell. (2018). https://doi.org/10.1109/TETCI.2018.2849380

  63. Wang, Y., Gao, S., Yu, Y., Xu, Z.: The discovery of population interaction with a power law distribution in brain storm optimization. Memetic Comput. (2017). https://doi.org/10.1007/s12293-017-0248-z

    Article  Google Scholar 

  64. Xin, B., Chen, J., Zhang, J., Fang, H., Peng, Z.H.: Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: a review and taxonomy. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(5), 744–767 (2012)

    Article  Google Scholar 

  65. Xu, Z., Wang, Y., Li, S., Liu, Y., Todo, Y., Gao, S.: Immune algorithm combined with estimation of distribution for traveling salesman problem. IEEJ Trans. Electr. Electron. Eng. 11, S142–S154 (2016)

    Article  Google Scholar 

  66. Yu, Y., Gao, S., Cheng, S., Wang, Y., Song, S., Yuan, F.: CBSO: a memetic brain storm optimization with chaotic local search. Memetic Comput. (2017). https://doi.org/10.1007/s12293-017-0247-0

    Article  Google Scholar 

  67. Yu, Y., Gao, S., Wang, Y., Cheng, J., Todo, Y.: ASBSO: an improved brain storm optimization with flexible search length and memory-based selection. IEEE Access 6, 36977–36994 (2018)

    Article  Google Scholar 

  68. Zheng, Y.J., Xu, X.L., Ling, H.F., Chen, S.Y.: A hybrid fireworks optimization method with differential evolution operators. Neurocomputing 148, 75–82 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan

    Yang Yu, Lin Yang, Yirui Wang & Shangce Gao

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  1. Yang Yu
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  2. Lin Yang
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  3. Yirui Wang
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  4. Shangce Gao
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Correspondence to Shangce Gao .

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Editors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  2. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Yu, Y., Yang, L., Wang, Y., Gao, S. (2019). Brain Storm Algorithm Combined with Covariance Matrix Adaptation Evolution Strategy for Optimization. In: Cheng, S., Shi, Y. (eds) Brain Storm Optimization Algorithms. Adaptation, Learning, and Optimization, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-15070-9_6

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