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Review on the Improvement and Application of Ant Colony Algorithm

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

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Abstract

The ant colony optimization algorithm is an approximation algorithm and it is also a probabilistic algorithm for finding optimized paths. Many combinatorial optimization problems have been solved by the ant colony optimization algorithm. Firstly, the basic principles of ant colony algorithm are first introduced by this reference. Secondly, it briefs several improvements method of ant colony algorithm and the application in solving practical problems, including the improvement of ant colony algorithm, parameter combination tuning and the application of ant colony algorithm in combination optimization problem. Finally, the problems existing in the ant colony algorithm are summarized and forecasted in this article.

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References

  1. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 1–13 (1996)

    Article  Google Scholar 

  2. Dorigo, M., Ganbardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  3. Gong, Y.: Research and application of ant colony algorithm. Anhui University of Technology (2014)

    Google Scholar 

  4. Li, Z.: Research and application prospect of intelligent optimization algorithm. J. Wuhan Univ. Light Ind. 35(04), 1–9 131 (2016)

    Google Scholar 

  5. Liang, X., Huang, M.: Modern Intelligent Optimization Hybrid Algorithm and its Application. Electronic Industry Press, Beijing (2011)

    Google Scholar 

  6. Huang, M.: Ant colony optimization algorithm and its application. Nanchang University (2007)

    Google Scholar 

  7. Liang, X., Huang, M., Ning, T.: Modern Intelligent Optimization Hybrid Algorithm and Its Application, 2nd edn. Publishing House of Electronics Industry, Beijing (2014)

    Google Scholar 

  8. Li, S., Chen, Y., Li, Y.: Ant Colony Algorithm and its Application. Harbin Institute of Technology Press, Harbin (2004)

    Google Scholar 

  9. Chen, S., Ma, L.: Basic principles and overview of ant colony algorithm. Sci. Technol. Innov. Appl. (31), 41 (2016)

    Google Scholar 

  10. Zhao, X., Tian, E.: The Ant Colony System (ACS) and its convergence proof. Comput. Eng. Appl. 43(5), 67–70 (2007)

    Google Scholar 

  11. Zhao, X.: MAX-MIN ant colony system and its convergence proof. Comput. Eng. Appl. 08, 70–72 (2006)

    Google Scholar 

  12. Jia, R., Ma, W.: Improved maximum and minimum ant colony algorithm based on neighborhood search. Comput. Simul. 31(12), 261–264 (2014)

    Google Scholar 

  13. Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the ant system-a computational study. Cent. Eur. J. Oper. Res. Econ. 7, 25–38 (1999)

    MathSciNet  MATH  Google Scholar 

  14. Li, X., He, Q., Li, Y., Zhu, Z.: Short-wave frequency optimization assignment based on mutual information diffusion ant colony algorithm. J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.) 44(04), 6–11 (2016)

    MATH  Google Scholar 

  15. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–56 (1997)

    Article  Google Scholar 

  16. Zhou, N., Ge, G., Su, S.: An pheromone-based adaptive continuous-domain hybrid mosquito swarm algorithm. Comput. Eng. Appl. 53(6), 156–161 (2017)

    Google Scholar 

  17. Chen, Y., Han, W., Cui, H.: Improved fusion of genetic algorithm and ant colony algorithm. Chin. J. Agric. Mechanization 35(04), 246–249 (2014)

    Google Scholar 

  18. Monarche, N., Venturini, G., Slimane, M.: On how pachycondyllan apicalis ants suggests a new algorithm. Future Gener. Comput. Syst. 16(8), 937–946 (2000)

    Article  Google Scholar 

  19. Xie, J., Su, D., Lu, S., Jia, W., Sun, M., Guo, J.: Key technology of transmission line path planning based on improved ant colony algorithm. Electr. Meas. Instrum., 1–7 (2019)

    Google Scholar 

  20. Wang, Y., Huang, L.: An ant colony algorithm based on disjoint search strategy. J. Chongqing Univ. Technol. (Nat. Sci.) 25(04), 65–69 (2011)

    Google Scholar 

  21. Wang, L., Wang, L., Zheng, C.: Identification of tag SNPs using set coverage ant colony algorithm with random disturbance characteristics. J. Yibin Univ. 15(06), 81–85 (2015)

    Google Scholar 

  22. Bao, W., Zhu, X., Zhao, J., Xu, H.: Weighted-valued polymorphic ant colony algorithm. Softw. Eng. 19(04), 1–4 (2016)

    Google Scholar 

  23. You, X., Liu, S., Lv, J.: An ant colony algorithm for dynamic search strategy and its application in robot path planning. Control Decis. 32(03), 552–556 (2017)

    Google Scholar 

  24. Mou, L.: Ant colony algorithm based on parameter dynamic change and variation. Comput. Eng. 36(19), 185–187 (2010)

    Google Scholar 

  25. You, H., Lu, Z.: Fast ant colony algorithm for adaptive adjustment of parameters \(\upalpha \), \(\upbeta \) and \(\uprho \). Manuf. Autom. 4040(06), 99–102–112(2018)

    Google Scholar 

  26. Wei, X., Li, Y.: Parameter optimization and simulation research in ant colony algorithm. Manuf. Autom. 37(10), 33–35 (2015)

    Google Scholar 

  27. Yang, S., Zhang, S.: Research on improved ant colony algorithm and parameter optimization. Electron. Technol. Softw. Eng. 13, 186–188 (2016)

    Google Scholar 

  28. Huang, P., Chen, Y.: Study on parameter optimization of improved ant colony algorithm. Comput. Age 06, 53–55 (2014)

    Google Scholar 

  29. Gan, Y., Li, S.: Study on parameter optimization configuration of ant colony algorithm. Manuf. Autom. 33(05), 66–69 (2011)

    Google Scholar 

  30. Wang, S., Jiang, H.: Application of genetic-ant colony algorithm in post-disaster emergency material path planning. Comput. Appl. Softw. 35(09), 99–103+131 (2018)

    Google Scholar 

  31. Lin, F., Chen, J., Ding, K., Li, Z.: Optimization of DV-Hop localization algorithm based on genetic algorithm and binary ant colony algorithm. Instr. Tech. Sens. (01), 86–90+96 (2019)

    Google Scholar 

  32. Meng, X., Pian, Z., Shen, Z., et al.: Ant colony algorithm based on force pheromone coordination. Control Decis. Making 5, 782–786 (2013)

    Google Scholar 

  33. Zhang, Y., Hou, Y., Li, C.: Path planning for handling robot ant colony based on artificial immune improvement. Comput. Meas. Control 23(12), 4124–4127 (2015)

    Google Scholar 

  34. Wu, D., Shao, J., Zhu, Y.: Research on fault diagnosis technology of CNC machine tools based on ant colony algorithm and neural network. Mech. Des. Manuf. 1, 165–167 (2013)

    Google Scholar 

  35. Zhang, C., Li, Q., Chen, P., Yang, S., Yin, Y.: An improved ant colony algorithm based on particle swarm optimization and its application. J. Univ. Sci. Technol. Beijing 35(07), 955–960 (2013)

    MATH  Google Scholar 

  36. Yin, J., Shuai, J., Wen, B.: Research on logistics distribution optimization based on ant colony algorithm and tabu search algorithm. Light Ind. Technol. 3434(08), 96–99 (2018)

    Google Scholar 

  37. Liu, K., Zhang, M.: Path planning based on simulated annealing ant colony algorithm. In: International Symposium on Computational Intelligence and Design, pp. 461–466 (2016)

    Google Scholar 

  38. Qin, D., Wang, C.: A hybrid ant colony algorithm based on 2-opt algorithm. Ind. Control Comput. 31(01), 98–100 (2018)

    Google Scholar 

  39. Lv, S., Ma, K.: Application of mosquito-fish group hybrid algorithm in batch scheduling of differential workpieces. Comput. Syst. Appl. 27(01), 162–167 (2018)

    Google Scholar 

  40. Huang, R.H., Yu, T.H.: An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting. Appl. Soft Comput. 57, 642–656 (2017)

    Article  Google Scholar 

  41. Ji, Y., Dang, P., Guo, X.: Study on job shop scheduling problem based on ant colony algorithm. Comput. Digit. Eng. 39(01), 4–6+52 (2011)

    Google Scholar 

  42. Tian, S., Chen, D., Wang, T., Liu, X.: An asynchronous ant colony algorithm for solving flexible job shop scheduling problems. J. Tianjin Univ. (Nat. Sci. Eng. Technol.) 49(09), 920–928 (2016)

    Google Scholar 

  43. Lu, H., Lu, Y.: Study on flexible job shop scheduling method based on distribution estimation and ant colony hybrid algorithm. Mech. Electr. Eng. 36(06), 568–573 (2019)

    Google Scholar 

  44. Fan, H., Xiong, H., Jiang, G., et al.: A review of scheduling rules algorithms in dynamic job shop scheduling problems. Comput. Appl. Res. 33(3), 648–653 (2016)

    Google Scholar 

  45. Khoukhi, F.E., Boukachour, J., Hilali Alaoui, A.E.: The “Dual-Ants Colony": a novel hybrid approach for the flexible job shop scheduling problem with preventive maintenance. Comput. Ind. Eng. 106, 236–255 (2016)

    Article  Google Scholar 

  46. Zhang, J., Zhang, J., Song, X.: Application of simulated annealing mosquito swarm algorithm in VRP problem. J. Xihua Univ. (Nat. Sci. Ed.) 36(06), 6–12 (2017)

    MathSciNet  Google Scholar 

  47. He, W., Ni, Y., Wang, T.: Vehicle routing problem based on mixed behavior ant colony algorithm. J. Hefei Univ. Technol. Nat. Sci. Ed. (7), 883–887 (2014)

    Google Scholar 

  48. Li, W., Dong, Y., Li, X., Zhang, W.: Application and convergence analysis of improved ant colony algorithm in emergency VRP. J. Comput. Appl. 31(12), 3557–3559+3567 (2014)

    Google Scholar 

  49. Zhu, H., He, H., Fang, Q., Dai, Y., Jiang, D.: Peak clustering of mosquito population density for medical image segmentation. J. Nanjing Norm. Univ. (Nat. Sci. Ed.) 42(02), 1–8 (2019)

    Google Scholar 

  50. Dai, T., Li, W.: Optimization of wireless sensor network routing based on improved ant colony algorithm. Comput. Meas. Control 24(02), 321–324 (2016)

    Google Scholar 

  51. Wang, M., Wan, Y., Ye, Z., et al.: Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Inf. Sci. 402, 50–68 (2017)

    Article  Google Scholar 

  52. Shen, C.: Design of PID controller based on adaptive ant colony algorithm. Instrum. Tech. Sens. (12), 126–128+156 (2016)

    Google Scholar 

  53. Yu, Y.: Research on path planning of mobile robot based on improved ant colony algorithm. J. Mech. Trans. 7, 58–61 (2016)

    Google Scholar 

  54. Zhao, F., Yang, C., Chen, F., Huang, L., Tan C.: Adaptive search radius ant colony dynamic path planning algorithm. Comput. Eng. Appl. 54(19), 56–61+87 (2018)

    Google Scholar 

  55. Zhao, H., Guo, J., Xu, W., Yan, S.: Research on trajectory planning of mobile robot based on fuzzy ant colony algorithm. Comput. Simul. 35(05), 318–321 (2018)

    Google Scholar 

  56. Lin, W., Deng, S., et al.: Research on path planning of mobile robot based on ant colony algorithm. Mech. Res. Appl. 31(04), 144–155+148 (2018)

    Google Scholar 

  57. You, X.-M., Liu, S., Lv, J.-Q.: Ant colony algorithm based on dynamic search strategy and its application on path planning of robot. Juece/Control Decis. 32(3), 552–556 (2017)

    Google Scholar 

  58. Wang, Y., Ma, J., Wang, Y.: Path planning of robot based on modified ant colony algorithm. Tec./Tech. Bull. 55(3), 1–6 (2017)

    Google Scholar 

  59. Bai, J., Chen, L., Jin, H., Chen, R., Mao, H.: Robot path planning based on random expansion of ant colony optimization. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds.) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol. 125, pp. 141–146. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-25789-6_21

    Chapter  Google Scholar 

  60. Lee, J.: Heterogeneous-ants-based path planner for global path planning of mobile robot applications. Int. J. Control Autom. Syst. 15(4), 1754–1756 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Dongping Qiao, Wentong Bai, Kanghong Wang & Yajing Wang

Authors
  1. Dongping Qiao
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  2. Wentong Bai
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  3. Kanghong Wang
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  4. Yajing Wang
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Corresponding author

Correspondence to Wentong Bai .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Qiao, D., Bai, W., Wang, K., Wang, Y. (2020). Review on the Improvement and Application of Ant Colony Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_1

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

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