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Super-fit control adaptation in memetic differential evolution frameworks

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Abstract

This paper proposes the super-fit memetic differential evolution (SFMDE). This algorithm employs a differential evolution (DE) framework hybridized with three meta-heuristics, each having different roles and features. Particle Swarm Optimization assists the DE in the beginning of the optimization process by helping to generate a super-fit individual. The two other meta-heuristics are local searchers adaptively coordinated by means of an index measuring quality of the super-fit individual with respect to the rest of the population. The choice of the local searcher and its application is then executed by means of a probabilistic scheme which makes use of the generalized beta distribution. These two local searchers are the Nelder mead algorithm and the Rosenbrock Algorithm. The SFMDE has been tested on two engineering problems; the first application is the optimal control drive design for a direct current (DC) motor, the second is the design of a digital filter for image processing purposes. Numerical results show that the SFMDE is a flexible and promising approach which has a high performance standard in terms of both final solutions detected and convergence speed.

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References

  • Birkho G, Lane SM (1953) A survey of modern algebra. Macmillan, New York

    Google Scholar 

  • Brest BBMMJ, Greiner S, Žumer V. (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6): 646–657

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Cascella MSGL, Salvatore N, Salvatore L (2005) On-line simplex-genetic algorithm for self-commissioning of electric drives. In: Proceedings 11th EPE, pp 277–283

  • Chiou J-P, Wang F-S (1998) A hybrid method of differential evolution with application to optimal control problems of a bioprocess system. In: The 1998 IEEE international conference on evolutionary computation proceedings, pp 627–632

  • Chiou J-P, Wang F-S (1999) Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process. Comp Chem Eng 23: 1277– 1291

    Article  Google Scholar 

  • Chiou J-P, Chang C-F, Su C-T (2004) Ant direction hybrid differential evolution for solving large capacitor placement problems. IEEE Trans Power Syst 19: 1794–1800

    Article  Google Scholar 

  • Daugman J (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am 2(7): 1160–1169

    Article  Google Scholar 

  • del Toro Garcia X, Neri F, Cascella GL, Salvatore N (2006) A surrogate assisted hooke-jeeves algorithm to optimize the control system of a pmsm drive. In: Proceedings of IEEE international symposium on industrial electronics, vol 1, pp 347–352

  • Dunn D, Higgins W (1995) Optimal Gabor filters for texture segmentation. IEEE Trans Image Process 4: 947–964

    Article  Google Scholar 

  • Dury W (2001) Control techniques drives & controls handbook. Institution Electrical Engineers

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, pp 39–43

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computation. Springer, Berlin

    Google Scholar 

  • Gabor D (1946) Theory of communication. J IEE (London) 93: 429– 457

    Google Scholar 

  • Hart WE, Krasnogor N, Smith JE (2004) Memetic evolutionary algorithms. In: Hart WE, Krasnogor N, Smith JE(eds) Recent advances in memetic algorithms. Springer, Berlin, pp 3–27

    Google Scholar 

  • Hendtlass T (2001) A combined swarm differential evolution algorithm for optimization problems. In: Lecture Notes in Computer Science, vol 2070. Springer, Heidelberg, pp 11–18

  • Iivarinen J, Pakkanen J, Rauhamaa J (2004) A som-based system for web surface inspection. In: Machine vision applications in industrial inspection XII, vol 5303, pp 178–187, SPIE

  • Jain LC, de Silva CW (1998) Intelligent adaptive control: industrial applications. CRC, Boca Raton

    Google Scholar 

  • Kaelo P, Ali MM (2007) Differential evolution algorithms using hybrid mutation. Comput Optim Appl 37: 231–246

    Article  MATH  MathSciNet  Google Scholar 

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

  • Krishnan R (2001) Electronic motor drives: modeling, analysis and control. Prentice-Hall, New Jersey

    Google Scholar 

  • Kirkpatrick S, Gelatt CDJ, Vecchi MP (1983) Optimization by simulated annealing. Science 220: 671–680

    Article  MathSciNet  Google Scholar 

  • Khorrami HMF, Krishnamurthy P (2003) Modeling and adaptive nonlinear control of electric motors. Springer, Heidelberg

    Google Scholar 

  • Krasnogor N (2004) Toward robust memetic algorithms. In: Hart WE, Krasnogor N, Smith JE(eds) Recent advances in memetic algorithms. Springer, Berlin, pp 185–207

    Google Scholar 

  • Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optim 9: 112–147

    Article  MATH  MathSciNet  Google Scholar 

  • Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Oŝmera P (ed) Proceedings of 6th international mendel conference on soft computing, pp 76–83

  • Leonhard W (2001) Control of electrical drives, 2nd edn. Springer, Heidelberg

    Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3): 281–295

    Article  Google Scholar 

  • Lin Y-C, Wang F-S, Hwang K-S (1999) A hybrid method of evolutionary algorithms for mixed-integer nonlinear optimization problems. In: Proceedings of the IEEE congress on evolutionary computation, vol 3, July

  • Lin Y-C, Hwang K-S, Wang F-S (2001) Co-evolutionary hybrid differential evolution for mixed-integer optimization problems. Eng Optim 33(6): 663–682

    Article  Google Scholar 

  • Lin Y-C, Hwang K-S, Wang F-S (2002) Hybrid differential evolution with multiplier updating method for nonlinear constrained optimization. In: Proceedings of the IEEE congress on evolutionary computation, May

  • Liu J, Lampinen J (2002a) On setting the control parameter of the differential evolution algorithm. In: Proceedings of the 8th international Mendel conference on soft computing, pp 11–18

  • Liu J, Lampinen J (2002b) Adaptive parameter control of differential evolution. In: Proceedings of the 8th international Mendel conference on soft computing, pp 19–26

  • Liu JLJ (2002) A fuzzy adaptive differential evolution algorithm. In: Proceedings of the 17th IEEE region 10 international conference on computer, communications, control and power engineering, vol I, p 606–611

  • Liu JLJ (2005) A fuzzy adaptive differential evolution algorithm. Soft computing—a fusion of foundations, methodologies and applications, vol 9. Springer, Heidelberg, pp 448–462

    Google Scholar 

  • Lopez Cruz IL, Van Willigenburg L, Van Straten G (2001) Parameter control strategy in differential evolution algorithm for optimal control. In: Hamza M (ed) Proceedings of the IASTED international conference artificial intelligence and soft computing (ASC 2001), May 21–24. ACTA Press, Calgary, pp 211–216

  • Kumar A, Pang G (2002) Defect detection in textured materials using gabor filters. IEEE Trans Ind Appl 38(2): 425–440

    Article  Google Scholar 

  • Manjunath WYMBS, Haley GM (2000) Handbook of image & video processing, Chap. Multiband techinques for texture classification and segmentation. Academic Press, New York, pp 367–381

  • Mydur R (2000) Application of evolutionary algorithms and neural networks to electromagnetic inverse problems. Master’s Thesis, Texas A and M University, Texas

  • Neri F, Toivanen J, Cascella GL, Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinform (Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics) 4(2): 264–278

    Google Scholar 

  • Nelder A, Mead R (1965) A simplex method for function optimization. Comput J 7: 308–313

    MATH  Google Scholar 

  • Neumann D, de Araujo HX: Hybrid differential evolution method for the mixed h2/h robust control problem under pole assignment. In: Proceedings of the 44th IEEE conference on decision and control, 2005 and 2005 European control conference, pp 1319–1324

  • Ng H (2004) Automatic Thresholding for Defect Detection. In: Proceedings of the third international conference on image and graphics (ICIG’04), vol 00, pp 532–535

  • Ong YS, Keane AJ (2004) Meta-lamarkian learning in memetic algorithms. IEEE Trans Evolut Comput 8(2): 99–110

    Article  Google Scholar 

  • Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1): 141–152

    Article  Google Scholar 

  • Panagopoulos H, Åström KJ, Hägglund T. (2002) Design of PID controllers based on constrained optimisation. IEE Proc Control Theory Appl 149: 32–40

    Article  Google Scholar 

  • Parker S, Chan J (2002) Dirt counting in pulp: an approach using image analysis methods. In: Signal and Image Processing (SIP 2002)

  • Price KV, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Heidelberg

    MATH  Google Scholar 

  • Rogalsky T, Derksen RW (2000) Hybridization of differential evolution for aerodynamic design. In: Proceedings of the 8th annual conference of the computational fluid dynamics society of Canada, pp 729–736

  • Rechemberg I (1973) Evolutionstrategie: Optimierung Technisher Systeme nach prinzipien des Biologishen Evolution. Fromman-Hozlboog Verlag, Stuggart

    Google Scholar 

  • Rosenbrock HH (1960) An automatic method for findong the greatest or least value of a function. Comp J 3(3): 175–184

    Article  MathSciNet  Google Scholar 

  • Schwefel H (1981) Numerical optimization of computer models. Wiley, Chichester

    MATH  Google Scholar 

  • Slotine J-J, Li W (1990) Applied nonlinear control. Prentice Hall, New Jersey

    Google Scholar 

  • Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. TR-95-012, ICSI

  • Su C-T, Lee C-S (2003) Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution. IEEE Trans Power Delivery 18: 1022–1027

    Article  Google Scholar 

  • Sun Z, Bebis G, Miller R (2005) On-road vehicle detection using evolutionary gabor filter optimization. IEEE Trans Intell Transportation Syst 6: 125–137

    Article  Google Scholar 

  • Szklarski KJL, Horodecki A (1990) Electric drive systems dynamics. Elsevier, Amsterdam

    Google Scholar 

  • Szu H, Hartley R (1987) Fast simulated annealing. Phys Lett A 122: 157–162

    Article  Google Scholar 

  • Tirronen V, Neri F, Kärkkäinen T, Majava K, Rossi T (2007) A memetic differential evolution in filter design for defect detection in paper production. In: Applications of evolutionary computing. Lectures Notes in Computer Science, vol 4448. Springer, Berlin, pp 320–329

  • Tsa D, Wu S (2000) Automated surface inspection using Gabor filters. Int J Adv Manufact Technol 16(7): 474–482

    Google Scholar 

  • Tvrdík J (2006) Differential evolution: competitive setting of control parameters. In: Proceedings of the international multiconference on computer science and information technology, pp 207–213

  • Wang F-S, Jang H-J (2000) Parameter estimation of a bioreaction model by hybrid differential evolution. Proc the IEEE Congress Evolut Comput 1: 410–417

    Google Scholar 

  • Weldon TP, Higgins WE (1996) Design of multiple gabor filters for texture segmentation (Atlanta, GA), pp 2243–2246

  • Zaharie D (2002) Critical values for control parameters of differential evolution algorithm. In: Matuŝek R, Oŝmera P (eds) Proceedings of 8th international Mendel conference on soft computing, pp 62–67

  • Zanchetta FCMMP, Sumner M, Mininno E (1995) On-line and off-line control design in power electronics and drives using genetic algorithms. In: Proceedings 11th IEEE CAIA, pp 277–283

  • Zielinskiand K, Weitkemper P, Laur R, Kammeyer K-D (2006) Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proceedings of the IEEE congress on evolutionary computation, pp 1857–1864

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Authors and Affiliations

  1. Department of Electrotechnics and Electronics, Technical University of Bari, Via E. Orabona 4, 70124, Bari, Italy

    Andrea Caponio

  2. Department of Mathematical Information Technology, Agora, University of Jyväskylä, P.O. Box 35 (Agora), 40014, Jyväskylä, Finland

    Ferrante Neri & Ville Tirronen

Authors
  1. Andrea Caponio
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  2. Ferrante Neri
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  3. Ville Tirronen
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Correspondence to Ferrante Neri.

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Caponio, A., Neri, F. & Tirronen, V. Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13, 811–831 (2009). https://doi.org/10.1007/s00500-008-0357-1

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