Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment

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As a novel swarm intelligence optimization algorithm, brain storm optimization (BSO) has its own unique capabilities in solving optimization problems. However, the performance of traditional BSO strategy in balancing exploitation and exploration is inadequate, which reduces the convergence performance of BSO. To overcome these problems, a multi-strategy BSO with dynamic parameters adjustment (MSBSO) is presented in this paper. In MSBSO, four competitive strategies based on improved individual selection rules are designed to adapt to different search scopes, thus obtaining more diverse and effective individuals. In addition, a simple adaptive parameter that can dynamically regulate search scopes is designed as the basis for selecting strategies. The proposed MSBSO algorithm and other state-of-the-art algorithms are tested on CEC 2013 benchmark functions and CEC 2015 large scale global optimization (LSGO) benchmark functions, and the experimental results prove that the MSBSO algorithm is more competitive than other related algorithms.

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  1. 1.

    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol 4, pp 1942–1948

  2. 2.

    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization technical report - tr06. Technical Report, Erciyes University

  3. 3.

    Peng H, Deng C, Wu Z (2019) Best neighbor-guided artificial bee colony algorithm for continuous optimization problems. Soft Comput 23(18):8723–8740

  4. 4.

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

  5. 5.

    Dorigo M, Caro GD (1999) Ant colony optimization: a new meta-heuristic. In: Congress on evolutionary computation, vol, 2, pp 1470–1477

  6. 6.

    Cai X, Xz Gao, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-Inspired Comput 8(4):205–214

  7. 7.

    Cui Z, Zhang J, Wang Y, Cao Y, Cai X, Zhang W, Chen J (2019) A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci China Inf Sci 62(7):70212

  8. 8.

    Shi Y (2011) Brain storm optimization algorithm. In: International conference in swarm intelligence. Springer, pp 303–309

  9. 9.

    Duan H, Li S, Shi Y (2013) Predator–prey brain storm optimization for dc brushless motor. IEEE Trans Magn 49(10):5336–5340

  10. 10.

    Guo X, Wu Y, Xie L (2014) Modified brain storm optimization algorithm for multimodal optimization. In: International Conference in Swarm Intelligence. Springer, pp 340–351

  11. 11.

    Jordehi AR (2015) Brainstorm optimisation algorithm (bsoa): an efficient algorithm for finding optimal location and setting of facts devices in electric power systems. Int J Electr Power Energy Syst 69:48–57

  12. 12.

    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

  13. 13.

    Cheng S, Sun Y, Chen J, Qin Q, Chu X, Lei X, Shi Y (2017) A comprehensive survey of brain storm optimization algorithms. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp 1637–1644

  14. 14.

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

  15. 15.

    Zhan Z h, Zhang J, Shi Y h, Liu Hl (2012) A modified brain storm optimization. In: 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1–8

  16. 16.

    Yang YT, Shi YH, Xia SR (2013) Discussion mechanism based brain storm optimization algorithm. J ZheJiang Univ (Eng Sci) 47(10):1705–1711

  17. 17.

    Chu X, Chen J, Cai F, Chen C, Niu B (2017) Augmented brain storm optimization with mutation strategies. In: Asia-Pacific Conference on Simulated Evolution and Learning. Springer, pp 949–959

  18. 18.

    Peng H, Deng C, Wu Z (2019) Spbso: self-adaptive brain storm optimization algorithm with pbest guided step-size. J Intell Fuzzy Syst 36(6):5423–5434

  19. 19.

    Nama S, Saha AK (2018) A new hybrid differential evolution algorithm with self-adaptation for function optimization. Appl Intell 48(7):1657–1671

  20. 20.

    Cheng J, Wang L, Jiang Q, Xiong Y (2018) A novel cuckoo search algorithm with multiple update rules. Appl Intell 48(11):4192–4211

  21. 21.

    Guo J, Sato Y (2019) A fission-fusion hybrid bare bones particle swarm optimization algorithm for single-objective optimization problems. Appl Intell 49(10):3641–3651

  22. 22.

    Wang F, Zhang H, Li K, Lin Z, Yang J, Shen XL (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf Sci 436:162–177

  23. 23.

    Cheng S, Lu H, Lei X, Shi Y (2019) Brain storm optimization algorithms: More questions than answers. In: Brain Storm Optimization Algorithms. Springer, pp 3–32

  24. 24.

    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. Springer, Berlin, pp 243–252

  25. 25.

    Yang Y, Duan D, Zhang H, Xia S (2015) Motion recognition based on hidden markov model with improved brain storm optimization. Space Med Med Eng 28(06):403–407

  26. 26.

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

  27. 27.

    Zhu H, Shi Y (2015) Brain storm optimization algorithms with k-medians clustering algorithms. In: Proceedings of the 7th international conference on advanced computational intelligence (ICACI). IEEE, pp 107–110

  28. 28.

    Cao Z, Rong X, Du Z (2017) An improved brain storm optimization with dynamic clustering strategy. In: MATEC Web of conferences, EDP sciences, vol, 95, pp 19002

  29. 29.

    Cao Z, Hei X, Wang L, Shi Y, Rong X (2015) An improved brain storm optimization with differential evolution strategy for applications of anns. Math Probl Eng 2015(10):1–18

  30. 30.

    Hua Z, Chen J, Xie Y (2016) Brain storm optimization with discrete particle swarm optimization for tsp. In: International conference on computational intelligence and security (CIS). IEEE, pp 190–193

  31. 31.

    Clerc M (2004) Discrete particle swarm optimization, illustrated by the traveling salesman problem. In: New optimization techniques in engineering. Springer, pp 219–239

  32. 32.

    Wang H, Liu J, Yi W, Niu B, Baek J (2017) An improved brain storm optimization with learning strategy. In: International Conference in Swarm Intelligence. Springer, pp 511–518

  33. 33.

    Chen J, Shi C, Yang C, Xie Y, Shi Y (2015) Enhanced brain storm optimization algorithm for wireless sensor networks deployment. Adv Swarm Comput Intell Lect Notes Comput Sci 9140:373–381

  34. 34.

    Liang Zhigang GJ (2018) Medical image registration by integrating modified brain storm optimization algorithm and powell algorithm. J Comput Appl 38(9):2683–2688

  35. 35.

    Lei Y, Zhang Y (2013) An improved 2d-3d medical image registration algorithm based on modified mutual information and expanded powell method. In: 2013 IEEE International conference on medical imaging physics and engineering (ICMIPE). IEEE, pp 24–29

  36. 36.

    Cheng S, Shi Y, Qin Q, Ting TO, Bai R (2014) Maintaining population diversity in brain storm optimization algorithm. Proc IEEE Congr Evol Comput (CEC) 4(3):3230–3237

  37. 37.

    Tang XW, Tang J, Wan S, Tang B (2013) Adaptive differential evolution algorithm with modified mutation strategy and its application. J Astron 34(7):1001–1007

  38. 38.

    Mühlenbein H, Schomisch M, Born J (1991) The parallel genetic algorithm as function optimizer. Parallel Comput 17(6-7):619–632

  39. 39.

    Liang J, Qu B, Suganthan P, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the cec 2013 special session on real-parameter optimization. Comput Intell Lab Zhengzhou Univ Zhengzhou, China Nanyang Technol Univ Singapore Techn Rep 201212(34):281–295

  40. 40.

    Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10 (6):646–657

  41. 41.

    Yong W, Zhang Q (2012) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 185(1):153–177

  42. 42.

    Peng H, Wu Z (2015) Heterozygous differential evolution with taguchi local search. Soft Comput 19 (11):3273–3291

  43. 43.

    Liao T, Stuetzle T (2013) Benchmark results for a simple hybrid algorithm on the cec 2013 benchmark set for real-parameter optimization. In: 2013 IEEE Congress on Evolutionary Computation. IEEE, pp 1938–1944

  44. 44.

    Auger A, Hansen N (2005) A restart cma evolution strategy with increasing population size. In: 2005 IEEE Congress on evolutionary computation. IEEE, vol, 2, pp 1769–1776

  45. 45.

    Lourenço H, Martin O, Stützle T (2010) Iterated local search: Framework and applications. In: Handbook of metaheuristics, vol, 146, pp 363–397

  46. 46.

    Hansen N, Müller S D, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evol Comput 11(1):1–18

  47. 47.

    Peng H, Wu Z, Deng C (2017) Enhancing differential evolution with commensal learning and uniform local search. Chin J Electron 26(4):725–733

  48. 48.

    Guo Z, Liu G, Li D, Wang S (2017) Self-adaptive differential evolution with global neighborhood search. Soft Comput 21(13):3759–3768

  49. 49.

    Bonyadi MR, Michalewicz Z (2017) Particle swarm optimization for single objective continuous space problems: a review. Evol Comput 25:1–54

  50. 50.

    Gonzalez-Fernandez Y, Chen S (2015) Leaders and followers—a new metaheuristic to avoid the bias of accumulated information. In: 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 776–783

  51. 51.

    Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2006) A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, pp 485– 492

  52. 52.

    Li X, Tang K, Omidvar MN, Yang Z, Qin K, China H (2013) Benchmark functions for the cec 2013 special session and competition on large-scale global optimization. Gene 7(33):8

  53. 53.

    Molina D, LaTorre A, Herrera F (2018) An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions. Cogn Comput 10(4):517–544

  54. 54.

    LaTorre A, Muelas S, Peña JM (2013) Large scale global optimization: Experimental results with mos-based hybrid algorithms. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 2742–2749

  55. 55.

    Molina D, Herrera F (2015) Iterative hybridization of de with local search for the cec’2015 special session on large scale global optimization. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 1974–1978

  56. 56.

    Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985– 2999

  57. 57.

    Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

  58. 58.

    LaTorre A (2009) A framework for hybrid dynamic evolutionary algorithms: multiple offspring sampling (mos). Universidad Politécnica de Madrid

  59. 59.

    Cano A, García-Martínez C, Ventura S (2017) Extremely high-dimensional optimization with mapreduce: scaling functions and algorithm. Inf Sci 415:110–127

  60. 60.

    Cano A, García-martínez C (2016) 100 million dimensions large-scale global optimization using distributed gpu computing. In: 2016 IEEE Congress on evolutionary computation (CEC). IEEE, pp 3566–3573

  61. 61.

    Mohamed AW, Suganthan PN (2018) Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput 22(10):3215–3235

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This work is supported by the National Natural Science Foundation of China(61763019), the Science and Technology Plan Projects of Jiangxi Provincial Education Department (GJJ1610 76,GJJ170953,GJJ180891).

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Correspondence to Hu Peng.

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Liu, J., Peng, H., Wu, Z. et al. Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment. Appl Intell (2020).

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  • Brain storm optimization
  • Multi-strategy
  • Individual selection rules
  • Dynamic parameters adjustment