Optimization of Non-Linear Chemical Processes using Modified Differential Evolution

Differential Evolution (DE) is an evolutionary optimization technique that is exceptionally simple, fast, and robust at numerical optimization. However, the convergence rate of DE in optimizing a computationally expensive objective function still does not meet our requirements, and an attempt to speed up DE is considered necessary. This paper introduces a Modified Differential Evolution (MDE) that enhances the convergence rate without compromising the robustness. MDE algorithm utilizes only one set of population array as against two sets in original DE at any given generation. This modification improves the convergence rate of DE and at the same time maintains the robustness. The MDE is applied to two benchmark test functions followed by non-linear chemical processes. The simulation results show empirical evidences on the efficiency and effectiveness of the proposed MDE.

[1]  B. Babu,et al.  Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation , 1999 .

[2]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[3]  Jhy‐Chang Lu,et al.  Optimization of low pressure chemical vapour deposition reactors using hybrid differential evolution , 2001 .

[4]  Gade Pandu Rangaiah,et al.  Tabu search for global optimization of continuous functions with application to phase equilibrium calculations , 2003, Comput. Chem. Eng..

[5]  Cliff T. Ragsdale,et al.  Modified differential evolution: a greedy random strategy for genetic recombination , 2005 .

[6]  Christodoulos A. Floudas,et al.  Nonlinear and Mixed-Integer Optimization , 1995 .

[7]  Rainer Storn,et al.  Differential Evolution Design of an IIR-Filter with Requirements for Magnitude and Group Delay , 1995 .

[8]  Feng-Sheng Wang,et al.  Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process , 1999 .

[9]  M. Willis,et al.  ADVANCED PROCESS CONTROL , 2005 .

[10]  Rainer Storn,et al.  Differential Evolution-A simple evolution strategy for fast optimization , 1997 .

[11]  O. A. Asbjornsen,et al.  Simultaneous optimization and solution of systems described by differential/algebraic equations , 1987 .

[12]  S. A. Da Eeo,et al.  DYNAMIC OPTIMIZATION OF CONSTRAINED CHEMICAL ENGINEERING PROBLEMS USING DYNAMIC PROGRAMMING , 1995 .

[13]  D. Himmelblau,et al.  Optimization of Chemical Processes , 1987 .

[14]  B. Babu,et al.  CHM-049 New Strategies Of Differential Evolution For Optimization Of Extraction Process , 2003 .

[15]  Ron S. Dembo,et al.  A set of geometric programming test problems and their solutions , 1976, Math. Program..

[16]  C. Floudas,et al.  Global Optimization in Generalized Geometric Programming , 1997, Encyclopedia of Optimization.

[17]  A. Neumaier,et al.  A global optimization method, αBB, for general twice-differentiable constrained NLPs — I. Theoretical advances , 1998 .

[18]  Kalyanmoy Deb,et al.  Optimization for Engineering Design: Algorithms and Examples , 2004 .

[19]  Costas J. Spanos,et al.  Advanced process control , 1989 .

[20]  B. Babu,et al.  A DIFFERENTIAL EVOLUTION APPROACH FOR GLOBAL OPTIMIZATION OF MINLP PROBLEMS , 2002 .

[21]  Ji-Pyng Chiou,et al.  Optimal control and optimal time location problems of differential-algebraic systems by differential evolution , 1997 .

[22]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[23]  G. McCormick,et al.  Selected applications of nonlinear programming , 1968 .