A self-adaptive differential evolution algorithm based on ant system with application to estimate kinetic parameters

References

• H. Abbass, The Self-adaptive Pareto Differential Evolution Algorithm, Proceedings of the IEEE Congress on Evolutionary Computation, Vol. 1, IEEE Press, Piscataway, NJ, 2002, pp. 831–836
   Google Scholar
• Ahuja , A , Das , S and Pahwa , A . 2007 . An AIS–ACO hybrid approach for multi-objective distribution system reconfiguration . IEEE Trans. Power Syst. , 22 : 1101 – 1111 .
   Web of Science ®Google Scholar
• Aluffi-Pentini , F , Parisi , V and Zirilli , F . 1985 . Global optimization and stochastic differential equations . J. Optim. Theory Appl. , 47 : 1 – 16 .
   Web of Science ®Google Scholar
• Azimi , ZN . 2005 . Hybrid heuristics for examination timetabling problem . Appl. Math. Comput. , 163 : 705 – 733 .
   Web of Science ®Google Scholar
• B. Babu and M. Jehan, Differential Evolution for Multi-objective Optimization, Proceedings of the IEEE Congress on Evolutionary Computation, Vol. 4, IEEE Press, Piscataway, NJ, 2003, pp. 2696–2703
   Google Scholar
• A.G. Bemis, J.A. Dindorf, B. Horwood, and C. Samans, Phthalic acids and other benzenepolycarboxylic acids, in Kirk Othmer Encyclopedia of Chemical Technology, Vol. 17, H.F. Mark, D.F. Othmer, and C.G. Overberg, eds., John Wiley & Sons, New York, 1982, pp. 732–777
   Google Scholar
• Brest , J , Bošković , B , Greiner , S , Žumer , V and Maučec , M . 2007 . Performance comparison of self-adaptive and adaptive differential evolution algorithms . Soft Comput. , 11 : 617 – 629 .
   Web of Science ®Google Scholar
• Brest , J , Greiner , S , Bošković , B , Mernik , M and Žumer , V . 2006 . Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems . IEEE Trans. Evol. Comput. , 10 : 646 – 657 .
   Web of Science ®Google Scholar
• Chang , YP and Low , C . 2008 . An ant direction hybrid differential evolution heuristic for the large-scale passive harmonic filters planning problem . Expert Syst. Appl. , 35 : 894 – 904 .
   Web of Science ®Google Scholar
• Dorigo , M , Maniezzo , V and Colorni , A . 1996 . The ant system: Optimization by a colony of cooperating agents . IEEE Trans. Syst. Man Cybern. Part B , 26 : 1 – 13 .
   PubMed Web of Science ®Google Scholar
• Dréo , J and Siarry , P . 2006 . An ant colony algorithm aimed at dynamic continuous optimization . Appl. Math. Comput. , 181 : 457 – 467 .
   Web of Science ®Google Scholar
• R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micromachine and Human Science, IEEE Service Center, Nagoya, Japan 1995, pp. 39–43
   Google Scholar
• Eiben , A , Hinterding , R and Michalewicz , Z . 1999 . Parameter control in evolutionary algorithms . IEEE Trans. Evol. Comput. , 3 : 124 – 141 .
   Web of Science ®Google Scholar
• H.G. Franck and J.W. Stadelhofer, p-Xylene and its derivates: Terephthalic acid, in Industrial Aromatic Chemistry: Raw Materials Process Products, H.G. Franck, ed., Springer-Verlag, Berlin, Heidelberg, 1988, pp. 283–290
   Google Scholar
• A.R. Hedar, Test functions for unconstrained global optimization, 2009. Available at http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO_files/Page364.htm (accessed 30 September 2009)
   Google Scholar
• Holland , JH . 1975 . Adaptation in Natural and Artificial Systems , Ann Harbor : University of Michigan Press .
   Google Scholar
• C.P. Hu and X.F. Yan. Novel macrokinetic model for industrial hydrogenation purifying of terephthalic acid, Chem. Prod. Process Model. 3 (2008), pp. 1–17
   Google Scholar
• Ingber , L . 1993 . Simulated annealing: Practice versus theory . J. Math. Comput. Model. , 18 : 29 – 57 .
   Web of Science ®Google Scholar
• L. Ji, Study on hydrogenation of 4-CBA in purification of terephthalic acid, M.S. thesis, Nanjing University of Technology, Nanjing, China, 2002
   Google Scholar
• Kaveh , A and Shahrouzi , M . 2007 . A hybrid ant strategy and genetic algorithm to tune the population size for efficient structural optimization . Int. J. Comput. Aided Eng. Software , 24 : 237 – 254 .
   Web of Science ®Google Scholar
• Kleerbezem , R , Mortier , J , Hulshoff Pol , LW and Lettinga , G . 1997 . Anaerobic pretreatment of petrochemical effluents: Terephthalic acid wastewater . Water Sci. Technol. , 36 : 237 – 248 .
   Web of Science ®Google Scholar
• Krink , T , Paterlini , S and Resti , A . 2007 . Using differential evolution to improve the accuracy of bank rating systems . Comput. Stat. Data Anal. , 52 : 68 – 87 .
   Web of Science ®Google Scholar
• J. Liu and J. Lampinen, A fuzzy adaptive differential revolution algorithm, Proceedings of the IEEE International Region 10 Conference, IEEE Press, Beijing, China, 2002, pp. 606–611
   Google Scholar
• Mathur , M , Karale , SB , Priye , S , Jayaraman , VK and Kulkarni , BD . 2000 . Ant colony approach to continuous function optimization . Ind. Eng. Chem. Res. , 39 : 3814 – 3822 .
   Web of Science ®Google Scholar
• Mu , SJ , Su , HY , Gu , Y and Chu , J . 2003 . Multi-objective optimization of industrial purified terephthalic acid (PTA) oxidation process . Chin. J. Chem. Eng. , 11 : 536 – 541 .
   Web of Science ®Google Scholar
• Muehlenbein , H and Schlierkamp , V . 1993 . Predictive models for the breeder genetic algorithm: I. Continuous parameter optimizations . Evol. Comput. , 1 : 25 – 49 .
   Web of Science ®Google Scholar
• Nobakhti , A and Wang , H . 2008 . A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier . Appl. Soft Comput. , 8 : 350 – 370 .
   Web of Science ®Google Scholar
• Press , WH , Teukolsky , SA , Vetterling , WT and Flannery , BP . 1992 . Numerical Recipes in C , Cambridge : Cambridge University Press .
   Google Scholar
• K. Price and R. Storn, 2009. DE Web site. Available at http://www.ICSI.Berkeley.edu/~storn/code.html (accessed 1 March 2009)
   Google Scholar
• Salman , A , Engelbrecht , A and Omran , M . 2007 . Empirical analysis of self-adaptive differential evolution . Eur. J. Oper. Res. , 183 : 785 – 804 .
   Web of Science ®Google Scholar
• A. Slowik and M. Bialko, Adaptive selection of control parameters in differential evolution algorithms, in Computational Intelligence: Methods and Applications, L. Zadeh, L. Rutkowski, R. Tadeusiewicz, and J. Zurada, eds., Polish Neural Network Society, IEEE Computational Intelligence Society, Poland, 2008, pp. 244–253
   Google Scholar
• A. Slowik and M. Bialko, Design and Optimization of IIR Digital Filters with Non-standard Characteristics Using Continuous Ant Colony Optimization Algorithm, Lecture Notes in Computer Science, Vol. 5138/2008, Springer-Verlag, Berlin, 2008
   Google Scholar
• A. Slowik and M. Bialko, Training of artificial neural networks using differential evolution algorithm, IEEE Conference on Human System Interaction, Cracow, 25–27 May, 2008, pp. 60–65
   Google Scholar
• Socha , K and Dorigo , M . 2008 . Ant colony optimization for continuous domains . Eur. J. Oper. Res. , 185 : 1155 – 1173 .
   Web of Science ®Google Scholar
• R. Storn, On the usage of differential evolution for function optimization, Biennial Conference of North American Fuzzy Information Processing Society, 1996, pp. 519–523
   Google Scholar
• Storn , R and Price , K . 1997 . Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces . J. Global Optim. , 11 : 341 – 359 .
   Web of Science ®Google Scholar
• J. Vesterstrom and R. Thomsen, A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, Proceedings of the Sixth Congress on Evolutionary Computation, Vol. 2, Piscataway, NJ, USA, 2004, pp. 1980–1987
   Google Scholar
• H.M. Voigt, Soft genetic operators in evolutionary algorithms, in Evolution and Biocomputation: Computational Models of Evolution, W. Banzhaf and F.H. Eeckman, eds., Springer-Verlag, Berlin, Vol. 899, 1995, pp. 123–141
   Google Scholar
• D. Whitley, D. Garrett, and J.P. Watson, Quad search and hybrid genetic algorithms, Proceedings of Genetic and Evolutionary Computation-GECCO 2003, Genetic and Evolutionary Computation Conference, Part II, Vol. 2724, Chicago, IL, USA, 2003, pp. 1468–1480
   Google Scholar
• Yao , X , Liu , Y and Lin , G . 1999 . Evolutionary programming made faster . IEEE Trans. Evol. Comput. , 3 : 82 – 102 .
   Web of Science ®Google Scholar
• Yasutaka , T , Takahiro , I , Satoshi , S and Ishii , Y . 2001 . A new strategy for the preparation of terephthalic acid by the aerobic oxidation of p-xylene using N-hydroxyphthalimide as a catalyst . Adv. Synth. Catal. , 343 : 220 – 225 .
   Web of Science ®Google Scholar
• X. Yuan, Y. Zhang, L. Wang, and Y. Yuan, An enhanced differential evolution algorithm for daily optimal hydro generation scheduling, Comput. Math. Appl. (2007), 55 (2008), pp. 2458–2468
   Google Scholar