Skip to main content
SpringerLink
Log in

A novel multi-population coevolution strategy for single objective immune optimization algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A novel multi-population coevolution strategy for single objective immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world from the inspiration that how neuro-endocrine system affects T cells and B cells in immune system eliminate the danger. The main idea of MCIA is to promote three populations to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity, where several operators have the capability of broadening the elites search space, boosting the global and local search around elites in search space. The MCIA is population B, population T, and assistant population A carrying on parallel evolution in nature, which simulates the immune system more comprehensively and unique in the aspects: clone operator and selected elite elements in the memory population enable the search space be broadened and compressed, and with the help of the cloud model characterized with randomness and stable topotaxis and local search technique, the global and local search is integrated to find the global optima with high population diversity. The performance comparisons of MCIA with three known immune algorithms and three optimization algorithms in optimizing 12 benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Hui L, Liu J, Niu R, Zhu Z (2014) Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem. Soft Comput 18:2385–2396

    Article  Google Scholar 

  2. Karthikeyan P, Baskar S (2015) Genetic algorithm with ensemble of immigrant strategies for multicast routing in ad hoc networks. Soft Comput 19:489–498

    Article  Google Scholar 

  3. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, vol 4, pp 1942–1948

  4. Rezaee Jordehi A (2014) Chaotic bat swarm optimisation (CBSO). Appl Soft Comput 26:523–530

    Article  Google Scholar 

  5. Rezaee Jordehi A, Jasni J, Abd Wahab N, Kadir MZ, Javadi MS (2015) Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput 26:401–417

    Article  Google Scholar 

  6. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperating learning approach to the travelling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  7. Terzi S, Serin S (2014) Planning maintenance works on pavements through ant colony optimization. Neural Comput Appl 25:143–153

    Article  Google Scholar 

  8. Viswanathan V, Krishnamurthi I (2015) Finding relevant semantic association paths using semantic ant colony optimization algorithm. Soft Comput 19:251–260

    Article  Google Scholar 

  9. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67

    Article  Google Scholar 

  10. Panda S, Swain SC, Mahapatra S (2014) Design and analysis of bacteria foraging optimised TCSC-based controller for power system stability improvement. Int J Data Anal Tech Strateg 6(4):384–406

    Article  Google Scholar 

  11. Xinbin Li L, Liu L, Li G, Guan X (2015) Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm. Soft Comput 19:597–607

    Article  Google Scholar 

  12. Yan X, Zhu Y, Chen H, Zhang H (2015) A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization. Nat Comput 14:169–184

    Article  MathSciNet  Google Scholar 

  13. Brajevic I (2015) Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput Appl. doi:10.1007/s00521-015-1826-y

    Google Scholar 

  14. Črepinšek M, Liu S-H, Mernik L (2012) A note on teaching-learning-based optimization algorithm. Inf Sci 212:79–93

    Article  Google Scholar 

  15. Rao RV, Waghmare GG (2014) Complex constrained design optimisation using an elitist teaching-learning-based optimisation algorithm. Int J Metaheuristics 3(1):81–102

    Article  Google Scholar 

  16. Cheng Y-H (2014) Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimization. IET Nanobiotechnol 8(4):238–246

    Article  Google Scholar 

  17. Rezaee Jordehi A (2014) A chaotic-based big bang–big crunch algorithm for solving global optimisation problems. Neural Comput Appl 25:1329–1335

    Article  Google Scholar 

  18. Dasgupta D (1999) Artificial immune systems and their applications. ISBN: 978-3-642-64174-9 (print) 978-3-642-59901-9 (online)

  19. Rezaee Jordehi A (2014) A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems. Neural Comput Appl. doi:10.1007/s00521-014-1751-5

    Google Scholar 

  20. Shang R, Li Y, Jiao L (2015) Co-evolution-based immune clonal algorithm for clustering. Soft Comput. doi:10.1007/s00500-015-1602-z

    Google Scholar 

  21. Karimi-Majd A-M, Fathian M, Amiri B (2014) A hybrid artificial immune network for detecting communities in complex networks. Computing. doi:10.1007/s00607-014-0433-6

    MATH  Google Scholar 

  22. Ettefagh MM, Javash MS (2014) Optimal synthesis of four-bar steering mechanism using AIS and genetic algorithms. J Mech Sci Technol 28(6):2351–2362

    Article  Google Scholar 

  23. Xiao X, Li T, Zhang R (2015) An immune optimization based real-valued negative selection algorithm. Appl Intell 42:289–302

    Article  Google Scholar 

  24. Van Peteghem V, Vanhoucke M (2013) An artificial immune system algorithm for the resource availability cost problem. Flex Serv Manuf J 25(1–2):122–144

    Article  Google Scholar 

  25. Salmon HM, de Farias CM, Loureiro P, Pirmez L, Rossetto S, Rodrigues PHA, Pirmez R, Delicato FC, da Costa Carmo LFR (2013) Intrusion detection system for wireless sensor networks using danger theory immune-inspired techniques. Int J Wireless Inf Netw 20(1):39–66

    Article  Google Scholar 

  26. Muhamad AS, Deris S (2013) An artificial immune system for solving production scheduling problems: a review. Artif Intell Rev 9(2):97–108

    Article  Google Scholar 

  27. Yang P, Zeng K, Li C, Yang J, Wang S (2014) An improved hybrid immune algorithm for mechanism kinematic chain isomorphism identification in intelligent design. Soft Comput 19(1):217–223

    Article  Google Scholar 

  28. Jamshidi R, Esfahani MMS (2013) A novel hybrid method for supply chain optimization with capacity constraint and shipping option. Int J Adv Manuf Technol 67:1563–1575

    Article  Google Scholar 

  29. Deepak BBVL, Parhi D (2013) Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment. Intell Serv Robot 6:155–162

    Article  Google Scholar 

  30. Mohammadi M, Akbari A, Raahemi B, Nassersharif B, Asgharian H (2013) A fast anomaly detection system using probabilistic artificial immune algorithm capable of learning new attacks. Evol Intell 6:135–156

    Article  Google Scholar 

  31. Liang C, Peng L (2013) An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J Med Syst 37:9932

    Article  Google Scholar 

  32. Qu G, Lou Z (2013) Application of particle swarm algorithm in the optimal allocation of regional water resources based on immune evolutionary algorithm. J Shanghai Jiaotong Univ (Sci) 18(5):634–640

    Article  Google Scholar 

  33. Pan G, Li K, Ouyang A, Li K (2014) Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP. Soft Comput. doi:10.1007/s00500-014-1522-3

    Google Scholar 

  34. Abdi K, Fathian M, Safari E (2012) A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem. Int J Adv Manuf Technol 60:723–732

    Article  Google Scholar 

  35. Kuo RJ, Chen SS, Cheng WC, Tsai CY (2014) Integration of artificial immune network and K-means for cluster analysis. Knowl Inf Syst 40:541–557

    Article  Google Scholar 

  36. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, Berlin

    MATH  Google Scholar 

  37. Chen M-H, Chang P-C, Lin C-H (2013) A self-evolving artificial immune system II with T-cell and B-cell for permutation flow-shop problem. J Intell Manuf 25(6):1257–1270

    Article  Google Scholar 

  38. Ataser Z, Alpaslan FN (2013) Self-adaptive negative selection using local outlier factor. In: Computer and information sciences III, pp 161–169

  39. Aickelin U, Bentley P, Cayzer S, Kim J (2003) Danger theory: the link between AIS and IDS. Lecture notes in computer sciences, vol 2787, pp 147–155

  40. Janosky M, Sabado RL, Cruz C, Vengco I, Hasan F, Winer A, Moy L, Adams S (2014) MAGE-specific T cells detected directly ex vivo correlate with complete remission in metastatic breast cancer patients after sequential immune-endocrine therapy. J Immunother Cancer 2:32. http://www.immunotherapyofcancer.org/content/2/1/32

  41. Prall SP, Muehlenbein MP (2014) Testosterone and immune function in primates: a brief summary with methodological considerations. Int J Primatol 35:805–824

    Article  Google Scholar 

  42. Farhy LS, Straume M et al (2011) A construct of interactive control of the GH axis in the male. Am J Physiol Regul Infest Comp Physiol 281(I):38–51

    Google Scholar 

  43. Keenan DM, Licinio J, Veldhuis JD (2001) A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenalaxis. PNAS 98(7):4028–4033

    Article  Google Scholar 

  44. Bao L, Yongsheng D (2006) A two-level controller based on the modulation principle of testosterone release. J Shanghai Jiaotong Univ 40(5):822–824

    Google Scholar 

  45. Bao L, Yongsheng D, Junhong W (2008) An intelligent controller based on ultra-short feedback of neuroendocrine system. Comput Simul 25(1):188–191

    Google Scholar 

  46. Bao L, Yongsheng D (2006) A novel intelligent controller based on hormone modulation of neuralendocrine system. Comput Simul 23(2):129–132

    Google Scholar 

  47. Huan H, Yongsheng D, Kuangrong H et al (2008) A neuroendocrine-based intelligent controller of parallel robot. Mach Des Res 24(6):35–38, 31

  48. Yizhou X, Kuangrong H, Yongsheng D (2007) Predictive PI controller for moisture of tobacco leaves based on the neuroendocrine feedback. Microcomput Appl 23(1):211–214

    Google Scholar 

  49. Ding YS, Liu B, Ren LH (2007) Intelligent decoupling control system inspired from modulation of the growth hormone in neuroendocrine system. Dyn Contin Discrete Impulsive Syst Ser B Appl Algorithms 14(5):679–693

    MATH  Google Scholar 

  50. Bao L, Zhongwei Z, Yongsheng D (2006) Decoupling control based on bi-directional regulation principle of growth hormone. J Southeast Univ (Nat Sci Ed) 36(Sup I):5–8

    Google Scholar 

  51. Liang X, Ding YS, Hao KR et al (2010) A neuroendocrine regulation principle-based intelligent cooperative decoupling controller for PANCF coagulation bath. In: Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA 2010), Jinan, China

  52. Liu B, Ding YS, Wang YH (2009) Intelligent network control system inspired from neuroendocrine–immune. In: Proceedings of the 6th international conference on fuzzy systems and knowledge discovery, Tianjin, China

  53. Zhang XF, Liang ZX, Ding YS (2009) A study on distributed collaborative control scheme based on multi-immune agent. In: Proceedings of the 2009 IEEE international joint conference on computational sciences and optimization, Sanya, China

  54. Liu B, Ding Y S, Wang J H (2009) A Collaborative optimized genetic algorithm based on regulation mechanism of neuroendocrine–immune system. In: Proceedings of the 2009 world summit on genetic and evolution and computation (GEC2 009), Shanghai, China

  55. Ding Y (2010) Research development of bio-network based intelligent control and optimization. Control Eng China 17(4):416–421

    Google Scholar 

  56. Casanova-Acebes M, A-Gonzalez N, Weiss LA, Hidalgo A (2014) Innate immune cells as homeostatic regulators of the hematopoietic niche. Int J Hematol 99:685–694

    Article  Google Scholar 

  57. Liu J, Zhao D, Liu C, Ding T, Yang L, Yin X, Zhou X (2015) Prion protein participates in the protection of mice from lipopolysaccharide infection by regulating the inflammatory process. J Mol Neurosci 55:279–287

    Article  Google Scholar 

  58. Greensmith J, Aickelin U, Tedesco G (2010) Information fusion for anomaly detection with the dendritic cell algorithm. Inf Fusion 11(1):21–34

    Article  Google Scholar 

  59. Hornung T, Wenzel J (2014) Innate immune-response mechanisms in dermatomyositis: an update on pathogenesis, diagnosis and treatment. Drugs 74:981–998

    Article  Google Scholar 

  60. Charles JF, Nakamura MC (2014) Bone and the innate immune system. Curr Osteoporos Rep 12:1–8

    Article  Google Scholar 

  61. Castro LN, Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6:239–251

    Article  Google Scholar 

  62. HYK Lau, WWP Tsang (2008) A parallel immune optimization algorithm for numeric function optimization. Evol Intell 1(3):171–185

    Article  Google Scholar 

  63. Gong M, Jiao L, Liu F, Ma W (2010) Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization. Knowl Inf Syst 25:523–549

    Article  Google Scholar 

  64. Pham HA (2014) Reduction of function evaluation in differential evolution using nearest neighbor comparison. Vietnam J Comput Sci. doi:10.1007/s40595-014-0037-2

    Google Scholar 

  65. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    MathSciNet  MATH  Google Scholar 

  66. Zou W, Zhu Y, Chen H, Sui X (2010) A clustering approach using cooperative artificial bee colony algorithm. DDNS 2010(2):1038–1045

    Google Scholar 

  67. Yan X, Zhu Y, Chen H, Zhang H (2013) A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization. Nat Comput. doi:10.1007/s11047-013-9405-6

    Google Scholar 

  68. Cuevas E, Gonzalez M (2013) An optimization algorithm for multimodal functions inspired by collective animal behavior. Soft Comput 17:489–502

    Article  Google Scholar 

  69. Wu H, Zhang F, Wu L (2013) New swarm intelligence algorithm-wolf pack algorithm. Syst Eng Elertron 35(11):2430–2438

    MathSciNet  MATH  Google Scholar 

  70. McGill R, Tukey J, Larsen W (1978) Variations of boxplots. Am Stat 32:12–16

    Google Scholar 

Download references

Acknowledgments

This work was supported by following Foundation Items: the National High Technology Research and Development Program (863 Program) of China (Nos. 2011AA7013038 and 2012AA7013038), the National Natural Science Foundation of China (Nos. 61102109, 61473309 and 61472443), the 2014 Annual Aviation Science Funds (Nos. 20140196003 and 20141996018), Scientific Plan Projects Foundation of Shanxi Province of China (Nos. 2014JQ8331 and 2014JM8308).

Author information

Authors and Affiliations

  1. Air and Missile Defense College, Air Force Engineering University, Changle East Road Jiazi No. 1, Xi’an, 710051, China

    Jinke Xiao, Weimin Li, Bin Liu & Peng Ni

Authors
  1. Jinke Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weimin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinke Xiao.

Appendix

Appendix

  1. (A)

    Rosenbrock function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n - 1} {\left( {100\left( {x_{l + 1} - x_{l}^{2} } \right)^{2} + \left( {x_{l} - 1} \right)^{2} } \right)} \\ & \quad - 30 \le x_{l} \le 30\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {1 \cdots 1} \right] \\ \end{aligned}$$
  2. (B)

    Step function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {\left\lfloor {\left( {x_{l} + 0.5} \right)^{2} } \right\rfloor } \right)} \\ & \quad - 100 \le x_{l} \le 100\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\text{ }\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {0 \cdots 0} \right] \\ \end{aligned}$$
  3. (C)

    Quadric function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {\sum\limits_{j = 1}^{l} {x_{j} } } \right)^{2} } \\ & \quad - 10 \le x_{l} \le 10\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {0 \cdots 0} \right] \\ \end{aligned}$$
  4. (D)

    Schwefels function

    $$\begin{aligned} \hbox{min} f(x) & = 418.9829 - \sum\limits_{l = 1}^{n} {\left( {x_{l} \sin \sqrt {\left| {x_{l} } \right|} } \right)} \\ & \quad - 500 \le x_{l} \le 500\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0 \\ & \quad {\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {420.9687 \cdots 420.9687} \right] \\ \end{aligned}$$
  5. (E)

    Rastrigin function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {x_{l}^{2} - 10\cos \left( {2\pi x_{l} } \right) + 10} \right)} \\ & \quad - 5.12 \le x_{l} \le 5.12\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;\text{ }f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {0 \cdots 0} \right] \\ \end{aligned}$$
  6. (F)

    Ackley’s function

    $$\begin{aligned} \hbox{min} f(x) & = - 20e^{{ - 0.2\left( {\sqrt {\frac{{\sum\nolimits_{l = 1}^{n} {x_{l}^{n} } }}{n}} } \right)}} - e^{{\frac{{\sum\nolimits_{l = 1}^{n} {\cos \left( {2\pi x_{l} } \right)} }}{n}}} + 20 + e \\ & \quad - 32 \le x_{l} \le 32\quad {\text{for}}\text{ }{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {0 \cdots 0} \right] \\ \end{aligned}$$
  7. (G)

    Griewank function

    $$\begin{aligned} \hbox{min} f( x ) & = \frac{{\sum\nolimits_{l = 1}^{n} {x_{l}^{2} } }}{4000} - \prod\limits_{l = 1}^{n} {\cos \left( {\frac{{x_{l} }}{\sqrt l }} \right)} + 1 \\ & \quad - 600 \le x_{l} \le 600\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {0 \cdots 0} \right] \\ \end{aligned}$$
  8. (H)

    Rotate hyper-ellipsoid function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {\sum\limits_{ll = 1}^{l} {x_{ll} } } \right)}^{2} \\ & \quad - 100 \le x_{l} \le 100\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {0 \cdots 0} \right] \\ \end{aligned}$$
  9. (I)

    Shift sphere function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {x_{l} - 1} \right)^{n} } \\ & \quad - 100 \le x_{l} \le 100\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {1 \cdots 1} \right] \\ \end{aligned}$$
  10. (J)

    Shift Schwefel’s function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {\sum\limits_{ll = 1}^{l} {\left( {x_{ll} - 1} \right)} } \right)}^{2} \\ & \quad - 100 \le x_{l} \le 100\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {1 \cdots 1} \right] \\ \end{aligned}$$
  11. (K)

    Shift Rosenbrock function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n - 1} {\left( {100\left( {x_{l + 1} - 1 - \left( {x_{l} - 1} \right)^{2} } \right)^{2} + \left( {x_{l} - 1} \right)^{2} } \right)} \\ & \quad - 100 \le x_{l} \le 100\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {1 \cdots 1} \right] \\ \end{aligned}$$
  12. (L)

    Shift Rastrigin function

    $$\begin{aligned} \hbox{min} f(x) & = \sum\limits_{l = 1}^{n} {\left( {\left( {x_{l} - 1} \right)^{2} - 10\cos \left( {2\pi \left( {x_{l} - 1} \right)} \right) + 10} \right)} \\ & \quad - 5 \le x_{l} \le 5\quad {\text{for}}\;{\text{all}}\;l = 1,2, \ldots ,n \\ & \quad {\text{where}}\;f(x^{*} ) = 0\;\;{\text{with}}\;\left[ {x_{1} \cdots x_{n} } \right] = \left[ {1 \cdots 1} \right] \\ \end{aligned}$$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, J., Li, W., Liu, B. et al. A novel multi-population coevolution strategy for single objective immune optimization algorithm. Neural Comput & Applic 29, 1115–1128 (2018). https://doi.org/10.1007/s00521-016-2507-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2507-1

Keywords

Search

Navigation