Skip to main content
SpringerLink
Log in

An improved CACO algorithm based on adaptive method and multi-variant strategies

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Chaotic ant colony optimization (CACO) algorithm is an effective optimization algorithm that simulates the self-organization and chaotic behavior of ants. However, in the research and application of the CACO algorithm for solving complex optimization problems, the CACO algorithm presents some disadvantages. In order to resolve these disadvantages, an improved CACO algorithm based on adaptive multi-variant strategies (CACOAMS) is proposed in this paper. The CACOAMS algorithm takes full advantage of multi-population strategy, the neighborhood comprehensive learning strategy, the fine search strategy, the chaotic optimization strategy, the super excellent ant strategy, the punishment strategy and min–max ant strategy in order to avoid the local optimization solution and stagnation, guarantee learning rate of the different dimensions for each ant and the diversity of the search, eliminate the self-locking trap between environmental boundary and obstacles, improve the search efficiency, search accuracy and robustness of the algorithm. In order to testify to the performance of the CACOAMS algorithm, the CACOAMS algorithm is applied to test the benchmark functions and dynamically adjust the values of PID parameters. The simulation results show that the CACOAMS algorithm takes on the strong flexibility, adaptability and robustness. It can effectively improve system control precision and guarantee feasibility and effectiveness.

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
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Acampora G, Cadenas JM, Loia V, Ballester EM (2011a) A multi-agent memetic system for human-based knowledge selection. IEEE Trans Syst Man Cybern Part A 41(5):946–960

  • Acampora G, Cadenas JM, Loia V, Ballester EM (2011b) Achieving memetic adaptability by means of agent-based machine learning. IEEE Trans Ind Inform 7(4):557–569

  • Acampora G, Loia V, Salerno S (2012) A hybrid evolutionary approach for solving the ontology alignment problem. Int J Intell Syst 27(3):189–216

    Article  Google Scholar 

  • Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699

    Article  MATH  Google Scholar 

  • Cai JJ, Li Q, Li LY, Peng HP, Yang YX (2012) A fuzzy adaptive chaotic ant swarm optimization for economic dispatch. Electr Power Energy Syst 34(1):154–160

  • Cai JJ, Ma XQ, Li Q, Li LY, Peng HP (2010) A multi-objective chaotic ant swarm optimization for environmental/economic dispatch. Electr Power Energy Syst 32(5):337–344

  • Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13(8):811–831

    Article  Google Scholar 

  • Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for off-line and on-line control design of PMSM drives. IEEE Tran Syst Man Cybern Part B 37(1):28–41

  • Chang WD (2007) A multi-crossover genetic approach to multivariable PID controllers tuning. Expert Syst Appl 33(3):620–626

    Article  Google Scholar 

  • Cole BJ (1991) Is animal behavior chaotic?Evidence from the activity of ants. Proc R Soc Lond Ser B Biol Sci 244(1311):253–259

  • Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of the 1st European conference of artificial life, Paris, pp 134–142

  • Davendr D, Zelinka I, Senkerik R (2010) Chaos driven evolutionary algorithms for the task of PID control. Comput Math Appl 60(4):1088–1104

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

    Article  Google Scholar 

  • Deng W, Chen R, Gao J et al (2012) A novel parallel hybrid intelligence optimization algorithm for function approximation problem. Comput Math Appl 63(1):325–336

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • E JQ, Wang CH, Wang YN, Gong JK (2008) A new adaptive mutative scale chaos optimization algorithm and its application. J Control Theory Appl 6(2):140–145

  • Gaing Z L (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391

  • Horst R, Tuy H (1996) Global optimization—deterministic approaches. Springer, New York

    MATH  Google Scholar 

  • Ko YC, Alouini MS, Simon MK (2000) Analysis and optimization of switched diversity systems. IEEE Trans Veh Technol 49(5):1813–1831

    Article  Google Scholar 

  • Li HX, Zhang L, Cai KY, Chen G (2005) An improved robust fuzzy-PID controller with optimal fuzzy reasoning. IEEE Trans Syst Man Cybern Part B Cybern 35:1283–1294

    Article  Google Scholar 

  • Li LY, Yang YX, Peng HP, Wang XD (2006) Parameters identification of chaotic systems via chaotic ant swarm. Chaos Solitons Fractals 28(12):1204–1211

    Article  MATH  Google Scholar 

  • Li LX, Peng HP, Wang XD et al (2006) An optimization method inspired by chaotic ant behavior. Int J Bifurc Chaos 16(8):2351–2364

    Article  MATH  MathSciNet  Google Scholar 

  • Li LY, Wen QY, Li LX, Peng HP (2009) Hybrid chaotic ant swarm optimization. Chaos Solitons Fractals 42(2):880–889

    Article  Google Scholar 

  • Lim KK, Ong YS, Lim MH, Chen XS, Agarwal A (2008) Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput 12(10):981–994

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  • Mann GKI, Hu BG, Gosine RG (1999) Analysis of direct action fuzzy PID controllersstructures. IEEE Trans Syst Man Cybern Part B Cybern 29(3):371–388

    Article  Google Scholar 

  • Neri F, Cascella GL, Salvatore N, Kononova AV, Acciani G (2007) Prudent-daring vs tolerant survivor selection schemes in control design of electric drives. Lect Notes Comput Sci 3907:805–809

  • Neri F, Garcia XD, Cascella GL, Salvatore N (2008) Surrogate assisted local search in PMSM drive design.COMPEL Int J Comput Math Electr Electron Eng 27(3):573–592

  • Niknam T, Ranjbar AM, Shirani AR (2005) A new approach for distribution state estimation based on ant colony algorithm with regard to distributed generation. J Intell Fuzzy Syst 16(2):119–131

  • Ong YS, Lim MH, Neri F, Ishibuchi H (2009) Special issue on emerging trends in soft computing: memetic algorithms. Soft Comput 13(8–9):739–740

    Article  Google Scholar 

  • Savran A (2013) A multivariable predictive fuzzy PID control system. Appl Soft Comput 13(5):2658–2667

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    Article  MATH  Google Scholar 

  • Tang YG, Cui MY, Hua CC, Li LX, Yang YX (2012) Optimum design of fractional order PI\(\lambda \)D\(\mu \) controller for AVR system using chaotic ant swarm. Expert Syst Appl 39(8):6887–6896

  • Willjuice Iruthayarajan M, Baskar S (2009) Evolutionary algorithms based design of multivariable PID controller. Expert Syst Appl 36(5):9159–9167

  • Yuan XF, Wang YN (2008) Parameter selection of support vector machine for function approximation based on chaos optimization. J Syst Eng Electron 19(1):191–197

    Article  Google Scholar 

  • Zhao YM, Xie WF, Tu XW (2012) Performance-based parameter tuning method of model-driven PID control systems. ISA Trans 51(3):393–399

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank all the reviewers for their constructive comments. This research was supported by the Open Project Program of State Key Laboratory of Software Engineering (SKLSE) (SKLSE2012-09-27), the National Natural Science Foundation of China (51175054), the Project Program of Provincial Key Laboratory for Computer Information Processing Technology (Soochow University) (KJS1326), the Open Project Program of Sichuan Provincial Key Lab of Process Equipment and Control (GK201202), the Higher Growth Plans of Liaoning Province for Distinguished Young Scholars (LJQ2013049), Open Project Program of Guangxi Key laboratory of hybrid computation and IC design analysis (2012HCIC06), the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province (Sichuan University of Science and Engineering) (2014RYJ02, 2014RYJ01), the Open Project Program of Chongqing Key Laboratory of Computational Intelligence (Chongqing University of Posts and Telecommunications) (CQ-LCI-2013-05). The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2009a produced by the Math-Works, Inc.

Author information

Authors and Affiliations

  1. Software Institute, Dalian Jiaotong University, Dalian , 116028, China

    Wu Deng, Huimin Zhao, Jingjing Liu, Xiaolin Yan, Yuanyuan Li, Lifeng Yin & Chuanhua Ding

  2. State Key Laboratory of Software Engineering, Wuhan University, Wuhan , 430072, China

    Wu Deng

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou , 215006, China

    Wu Deng

  4. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, 64300, China

    Wu Deng

  5. Sichuan Provincial Key Lab of Process Equipment and Control, Sichuan University of Science and Engineering, Zigong , 64300, China

    Wu Deng

Authors
  1. Wu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huimin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingjing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuanyuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lifeng Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chuanhua Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wu Deng.

Additional information

Communicated by P.-C. Chung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, W., Zhao, H., Liu, J. et al. An improved CACO algorithm based on adaptive method and multi-variant strategies. Soft Comput 19, 701–713 (2015). https://doi.org/10.1007/s00500-014-1294-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1294-9

Keywords

Search

Navigation