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

Soft Computing

Volumn 19, Issue 3, 2015, Pages 701-713

  Deng, Wu ^a,b,c,d   Zhao, Huimin ^a   Liu, Jingjing ^a   Yan, Xiaolin ^a   Li, Yuanyuan ^a   Yin, Lifeng ^a   Ding, Chuanhua ^a  

^a DALIAN JIAOTONG UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

^c SOOCHOW UNIVERSITY   (China)

^d SICHUAN UNIVERSITY OF SCIENCE AND ENGINEERING   (China)

Author keywords

Adaptive multi variant strategies; Chaotic ant colony algorithm; Parameter optimization; Pheromone; PID control

Indexed keywords

ANT COLONY OPTIMIZATION; BENCHMARKING; ROBUSTNESS (CONTROL SYSTEMS); THREE TERM CONTROL SYSTEMS;

ADAPTIVE MULTI-VARIANT STRATEGIES; CHAOTIC ANT COLONY ALGORITHMS; CHAOTIC ANT COLONY OPTIMIZATIONS; COMPLEX OPTIMIZATION PROBLEMS; ENVIRONMENTAL BOUNDARIES; PARAMETER OPTIMIZATION; PHEROMONE; RESEARCH AND APPLICATION;

PARAMETER ESTIMATION;

EID: 84922836282     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-014-1294-9     Document Type: Article

References (36)

1. 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
2. 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
3. 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
4. Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699
5. 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
6. 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
7. Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13(8):811–831
8. 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
9. Chang WD (2007) A multi-crossover genetic approach to multivariable PID controllers tuning. Expert Syst Appl 33(3):620–626
10. 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
11. 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
12. Davendr D, Zelinka I, Senkerik R (2010) Chaos driven evolutionary algorithms for the task of PID control. Comput Math Appl 60(4):1088–1104
13. 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
14. 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
15. 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
16. 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
17. 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
18. Horst R, Tuy H (1996) Global optimization—deterministic approaches. Springer, New York
19. Ko YC, Alouini MS, Simon MK (2000) Analysis and optimization of switched diversity systems. IEEE Trans Veh Technol 49(5):1813–1831
20. 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
21. 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
22. 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
23. Li LY, Wen QY, Li LX, Peng HP (2009) Hybrid chaotic ant swarm optimization. Chaos Solitons Fractals 42(2):880–889
24. 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
25. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13
26. 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
27. 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
28. 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
29. 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
30. 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
31. Savran A (2013) A multivariable predictive fuzzy PID control system. Appl Soft Comput 13(5):2658–2667
32. 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
33. Tang YG, Cui MY, Hua CC, Li LX, Yang YX (2012) Optimum design of fractional order PI$$\lambda $$λD$$\mu $$μ controller for AVR system using chaotic ant swarm. Expert Syst Appl 39(8):6887–6896
34. Willjuice Iruthayarajan M, Baskar S (2009) Evolutionary algorithms based design of multivariable PID controller. Expert Syst Appl 36(5):9159–9167
35. 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
36. Zhao YM, Xie WF, Tu XW (2012) Performance-based parameter tuning method of model-driven PID control systems. ISA Trans 51(3):393–399