Multiobjective Optimization Using Differential Evolution

Real world engineering design problems are usually characterized by the presence of many conflicting objectives. The optimal solution to such problems results in a number of trade-off solutions, rather than a unique solution. The use of evolutionary algorithms for solving such problems has attracted much attention recently, as they can find multiple optimal solutions in one single simulation run due to their population based search approach. In this study some of the limitations of Non-dominated Sorting Genetic Algorithm (NSGA), a multiobjective evolutionary algorithm are addressed using Differential Evolution (DE), which is an improved version of Genetic Algorithm (GA). The solutions provided by Multiobjective Differential Evolution (MODE) for three standard test problems and an engineering problem are compared with those obtained using NSGA, and the proposed algorithm is found to give encouraging results.

[1]  Feng-Sheng Wang,et al.  Simultaneous Optimization of Feeding Rate and Operation Parameters for Fed‐Batch Fermentation Processes , 1999, Biotechnology progress.

[2]  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 .

[3]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms in Engineering Applications , 1997, Springer Berlin Heidelberg.

[4]  Ajay K. Ray,et al.  Multi-objective optimization of industrial hydrogen plants , 2001 .

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

[6]  Ajay K. Ray,et al.  Multiobjective optimization of an industrial wiped film poly(ethylene terephthalate) reactor: some further insights , 2001 .

[7]  Ajay K. Ray,et al.  Multiobjective optimization of steam reformer performance using genetic algorithm , 2000 .

[8]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  B. Babu,et al.  EVOLUTIONARY COMPUTATION STRATEGY FOR OPTIMIZATION OF AN ALKYLATION REACTION , 2002 .

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

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

[12]  Ajay K. Ray,et al.  Multiobjective optimization of an industrial styrene reactor , 2003, Comput. Chem. Eng..

[13]  G. P. Rangaiah,et al.  Simulation and Multiobjective Optimization of an Industrial Hydrogen Plant Based on Refinery Off-Gas , 2002 .

[14]  Ajay K. Ray,et al.  Multiobjective optimization of an industrial wiped-film pet reactor , 2000 .

[15]  Rakesh Angira,et al.  Optimization Of Water Pumping System Using Differential Evolution Strategies , 2003 .

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

[17]  Rakesh Angira,et al.  OPTIMIZATION OF NON-LINEAR FUNCTIONS USING EVOLUTIONARY COMPUTATION , 2002 .

[18]  Optimal Design of Shell-and-Tube Heat Exchangers by Different Strategies of Differential Evolution , 2001 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  B. V. Babu,et al.  Optimal design of an auto-thermal ammonia synthesis reactor , 2005, Comput. Chem. Eng..

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

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

[23]  B. Babu,et al.  Optimization of Pyrolysis of Biomass Using Differential Evolution Approach , 2003 .