A modified differential evolution algorithm for unconstrained optimization problems

Neurocomputing

Volumn 120, Issue , 2013, Pages 469-481

  Zou, Dexuan ^a   Wu, Jianhua ^b   Gao, Liqun ^b   Li, Steven ^c  

^a JIANGSU NORMAL UNIVERSITY   (China)

^b NORTHEASTERN UNIVERSITY   (China)

^c UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

Author keywords

Central solution; External archive; Gauss distribution; Modified differential evolution algorithm; Mutation; Uniform distribution

Indexed keywords

EXTERNAL ARCHIVE; GAUSS DISTRIBUTION; MODIFIED DIFFERENTIAL EVOLUTION ALGORITHMS; MUTATION; UNIFORM DISTRIBUTION;

COMPUTER APPLICATIONS; NEURAL NETWORKS;

EVOLUTIONARY ALGORITHMS;

ADAPTIVE DIFFERENTIAL EVOLUTION BASED ON NOVEL MUTATION AND CROSSOVER STRATEGY; ADAPTIVE DIFFERENTIAL EVOLUTION WITH OPTIONAL EXTERNAL ARCHIVE; ALGORITHM; ARTICLE; CALCULATION; CONTROLLED STUDY; CONVERGENT EVOLUTION; DIFFERENTIAL EVOLUTION ALGORITHM; MATHEMATICAL ANALYSIS; MATHEMATICAL AND STATISTICAL PROCEDURES; MATHEMATICAL COMPUTING; MODIFIED DIFFERENTIAL EVOLUTION ALGORITHM; NEW DIFFERENTIAL EVOLUTION ALGORITHM; NORMAL DISTRIBUTION; OPPOSITION BASED DIFFERENTIAL EVOLUTION; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; RANDOMIZATION; SELF ADAPTING CONTROL PARAMETER; STATISTICAL DISTRIBUTION; UNIFORM DISTRIBUTION;

EID: 84882886621     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.04.036     Document Type: Article

References (52)

1. Holland J.H. Adaptive in Natural and Artificial Systems 1975, Ann Arbor, Univ. Mich. Press, MI.
2. J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.
3. Shi Y.H., Eberhart R.C. Empirical study of particle swarm optimization. Proc. IEEE Congress Evol. Comput. 1999, 1945-1950.
4. Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science 1983, 220.
5. Dorigo M., Maniezzo V., Colorni A. Ant system. optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 1996, 26(1):29-41.
6. Dai C.N., Yao M., Xie Z.J., Chen C.H., Liu J.G. Parameter optimization for growth model of greenhouse crop using genetic algorithms. Appl. Soft Comput. 2009, 9(1):13-19.
7. Luh G.C., Lin C.Y. Structural topology optimization using ant colony optimization algorithm. Appl. Soft Comput. 2009, 9(4):1343-1353.
8. Karasulu B., Korukoglu S. A simulated annealing-based optimal threshold determining method in edge-based segmentation of grayscale images. Appl. Soft Comput. 2011, 11(2):2246-2259.
9. Gomes H.M. Truss optimization with dynamic constraints using a particle swarm algorithm. Expert Syst. Appl. 2011, 38(1):957-968.
10. R. Storn, K. Price, Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, Berkeley, USA, International Computer Science Institute, 1995.
11. Price K.V., Storn R.M., Lampinen J.A. Differential evolution. a practical approach to global optimization. Nat. Comput. Series 2005.
12. J. Vesterstrøm, R. Thomsen, A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, in: Proceedings of the Congress on Evolutionary Computation, vol. 2, 2004, pp. 1980-1987.
13. Hrstka O., Kučerová A. Improvement of real coded genetic algorithm based on differential operators preventing premature convergence. Adv. Eng. Software 2004, 35(3-4):237-246.
14. Yang G.Y., Dong Z.Y., Wong K.P. A modified differential evolution algorithm with fitness sharing for power system planning. IEEE Trans. Power Syst. 2008, 23(2):514-522.
15. Cai H.R., Chung C.Y., Wong K.P. Application of differential evolution algorithm for transient stability constrained optimal power flow. IEEE Trans. Power Syst. 2008, 23(2):719-728.
16. Angira R., Santosh A. Optimization of dynamic systems. a trigonometric differential evolution approach. Comput. Chem. Eng. 2007, 31(9):1055-1063.
17. Semnani A., Rekanos I.T., Kamyab M., Papadopoulos T.G. Two-dimensional microwave imaging based on hybrid scatterer representation and differential evolution. IEEE Trans. Antennas Propag. 2010, 58(10):3289-3298.
18. Su C.T., Lee C.S. Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution. IEEE Trans. Power Delivery 2003, 18(3):1022-1027.
19. Cai Z.H., Gong W.Y., Ling C.X., Zhang H. A clustering-based differential evolution for global optimization. Appl. Soft Comput. 2011, 11(1):1363-1379.
20. Jain A., Murty M., Flynn P. Data clustering. a review. ACM Comput. Surv. 1999, 31(3):264-323.
21. Chiang C.W., Lee W.P., Heh J.S. A 2-Opt based differential evolution for global optimization. Appl. Soft Comput. 2010, 10(4):1200-1207.
22. Croes G.A. A method for solving traveling salesman problems. Oper. Res. 1958, 6(6):791-812.
23. Liu Y., Sun F. A fast differential evolution algorithm using k-nearest neighbour predictor. Expert Syst. Appl. 2011, 38(4):4254-4258.
24. Bishop C.M. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford, UK.
25. Vapnik V. Statistical Learning Theory 1998, Wiley, NY.
26. Omran M.G.H., Engelbrecht A.P., Salman A. Bare bones differential evolution. Eur. J. Oper. Res. 2009, 196(1):128-139.
27. Zhang J.Q., Sanderson A.C. JADE. adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 2009, 13(5):945-958.
28. Deng C.S., Zhao B.Y., Deng A.Y., Hu R.X. New differential evolution algorithm with a second enhanced mutation operator. Int. Workshop Intell. Syst. Appl. 2009, 1-4.
29. Neri F., Tirronen V. Recent advances in differential evolution. a survey and experimental analysis. Artif. Intell. Rev. 2010, 33(1):61-106.
30. Rahnamayan S., Tizhoosh H.R., Salama M.M.A. Opposition-based differential evolution. IEEE Trans. Evol. Comput. 2008, 12(1):64-79.
31. H.R. Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, in: Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, Austria, 2005, pp. 695-701.
32. U.K. Chakraborty, S. Das, A. Konar, Differential evolution with local neighborhood, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2006, pp. 2042-2049.
33. Das S., Abraham A., Chakraborty U.K., Konar A. Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 2009, 13(3):526-553.
34. A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, 2005, pp. 1785-1791.
35. Brest J., Greiner S., Bošković B., Mernik M., Žumer V. Self-adapting control parameters in differential evolution. a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 2006, 10(6):646-657.
36. Weber M., Tirronen V., Neri F. Scale factor inheritance mechanism in distributed differential evolution, soft computing-a fusion of foundations. Method. Appl. 2010, 14(11):1187-1207.
37. Neri F., Iacca G., Mininno E. Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf. Sci. 2011, 181(12):2469-2487.
38. Weber M., Neri F., Tirronen V. A study on scale factor in distributed differential evolution. Inf. Sci. 2011, 181(12):2488-2511.
39. Mininno E., Neri F., Cupertino F., Naso D. Compact differential evolution. IEEE Trans. Evol. Comput. 2011, 15(1):32-54.
40. Cody W.J. Rational Chebyshev approximations for the error function. Math. Comput. 1969, 23(107):631-637.
41. Neri F., Mininno E. Memetic compact differential evolution for Cartesian Robot control. IEEE Comput. Intell. Mag. 2010, 5(2):54-65.
42. Das S., Konar A., Chakraborty U.K. Two improved differential evolution schemes for faster global search. GECCO 2005, 991-998.
43. Islam S.M., Das S., Ghosh S., Roy S., Suganthan P.N. An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B 2012, 42(2):482-500.
44. M. Weber, F. Neri, V. Tirronen, Shuffle or update parallel differential evolution for large scale optimization, soft computing-a fusion of foundations, Methodologies and Applications, Springer, vol. 15(11), 2011, pp. 2089-2107.
45. Zou D.X., Q Gao L., Wu J.H., Li S. Novel global harmony search algorithm for unconstrained problems. Neurocomputing 2010, 73(16-18):3308-3318.
46. Zou D.X., Liu H.K., Gao L.Q., Li S. An improved differential evolution algorithm for the task assignment problem. Eng. Appl. Artif. Intell. 2011, 24(4):616-624.
47. Zou D.X., Liu H.K., Gao L.Q., Li S. A novel modified differential evolution algorithm for constrained optimization problems. Comput. Math. Appl. 2011, 61(6):1608-1623.
48. Liu Y., Qin Z., Shi Z.W., Lu J. Center particle swarm optimization. Neurocomputing 2007, 70(4-6):672-679.
49. P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y. P. Chen, A. Auger, S. Tiwari, Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, Technical Report, Nanyang Technological University, Singapore, 2005.
50. Wilcoxon F. Individual comparisons by ranking methods. Biometrics 1945, 1(6):80-83.
51. Derrac J., Garca S., Molina D., Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 2011, 1(1):3-18.
52. Iacca G., Neri F., Mininno E., Ong Y.S., Lim M.H. Ockham's Razor in memetic computing. three stage optimal memetic exploration. Inf. Sci. 2012, 188:17-43.