Nature-Inspired Evolutionary Computation For Optimization

B.V. Babu*
Former Vice Chancellor, Graphic Era University, Dehradun, India & Galgotias University, Uttar Pradesh, India and Former Professor & Dean, BITS-Pilani, Rajastan, India.


As and when the conventional analytical and empirical approaches fail to optimize a given system, we need to look for alternatives. The emerging trend is to get inspiration from nature to handle such complex situations and systems. Traditionally, the gradient based optimization techniques are used for finding an optimal solution to a given problem. However, due to the inability of these techniques in terms of handling multi-variable multi-constrained complex systems, the nature inspired evolutionary algorithms (population based search algorithms) have been developed over the past few years. This paper focuses on nature- or bio-inspired evolutionary computation technique called Differential Evolution (DE) (an improved version of Generic Algorithm), its working principle, and demonstration with a numerical example using step-by-step procedure. Various DE strategies are discussed and applied to many engineering and management problems. DE is extended to multi-objective optimization problems as Multi-Objective Differential Evolution (MODE) and its variants, that can handle the limitations of traditional optimization techniques in addressing complex engineering problems in terms of constraints, objectives, etc. are demonstrated. The working principles of these Evolutionary Algorithms are demonstrated with examples and industrial applications.


Evolutionary Algorithms, Evolutionary Computation, Differential Evolution, Genetic Algorithms, Multi-Objective Differential Evolution.

How to Cite this Article?

Babu, B.V. (2017). Nature-Inspired Evolutionary Computation for Optimization. i-manager’s Journal on Software Engineering, 12(2), 25-36.


[1]. Angira, R., & Santosh, A. (2007). Optimization of dynamic systems: A trigonometric differential evolution approach. Computers & Chemical Engineering, 31(9), 1055-1063.
[2]. Angira, R., & Babu, B. V. (2006a). Optimization of process synthesis and design problems: A modified differential evolution approach. Chemical Engineering Science, 61(14), 4707-4721.
[3]. Angira, R., & Babu, B. V. (2006b). Performance of modified differential evolution for optimal design of complex and non-linear chemical processes. Journal of Experimental & Theoretical Artificial Intelligence, 18(4), 501-512.
[4]. Angira, R., & Babu, B. V. (2006c). Multi-Objective optimization using Modified Differential Evolution (MDE). International Journal of Mathematical Sciences: Special Issue on Recent Trends in Computational Mathematics and Its Applications, 5(2), 371-387.
[5]. Babu, B. V. (2004). Process Plant Simulation. Oxford University Press, USA.
[6]. Babu, B. V., & Angira, R. (2005). Optimal design of an auto-thermal ammonia synthesis reactor. Computers & Chemical Engineering, 29(5), 1041-1045.
[7]. Babu, B. V., & Angira, R. (2006). Modified Differential Evolution (MDE) for optimization of non-linear chemical processes. Computers & Chemical Engineering, 30(6-7), 989-1002.
[8]. Babu, B. V., Chakole, P. G., & Mubeen, J. S. (2005a). Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor. Chemical Engineering Science, 60(17), 4822-4837.
[9]. Babu, B. V., Chakole, P. G., & Syed Mubeen, J. H. (2005b). Differential evolution strategy for optimal design of gas transmission network. Multidiscipline Modeling in Materials and Structures, 1(4), 315-328.
[10]. Babu, B. V. (2007). Improved differential evolution for single and multiobjective optimization: MDE, MODE, NSDE, and MNSDE. In Deb, K., Chakroborty, P., Iyengar, N. G. R., & Gupta, S. K. (Ed.), Advances in Computational Optimization and its Applications (pp. 24-30). Universities Press, Hyderabad.
[11]. Babu, B. V., & Angira, R. (2006). Modified differential evolution (MDE) for optimization of non-linear chemical processes. Computers & Chemical Engineering, 30(6-7), 989-1002.
[12]. Babu, B. V., & Gujarathi, A. M. (2007a). Elitist-Multiobjective differential evolution (E-MODE) algorithm for rd multi-objective optimization. In Proc. of 3 Indian International Conference on Artificial Intelligence (IICAI- 2007) (pp. 441-449).
[13]. Babu, B. V., & Gujarathil, A. M. (2007b). Multiobjective differential evolution (MODE) for optimization of supply chain planning and management. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (pp. 2732-2739). IEEE.
[14]. Babu, B. V., & Gujarathi, A. M. (2007c). Multiobjective differential evolution (MODE) algorithm for multiobjective optimization: Parametric study on benchmark test problems. Journal on Future Engineering and Technology, 3(1), 47-59.
[15]. Babu, B. V., & Gujarathi, A. M. (2008). Hybrid multiobjective differential evolution (H-MODE) for multiobjective optimization. In Computational Intelligence in Expensive Optimization Problems, Edited by Chi-Keong Yoel and Chi-Keong G. O. H. Springer-Verlag, Germany, Communicated.
[16]. Babu, B. V., Gujarathi, A. M., Katla, P., & Laxmi, V. B. (2007a). Strategies of multi-objective differential evolution (MODE) for optimization of adiabatic styrene reactor. In Proceedings of the International Conference on Emerging Mechanical Technology: Macro to Nano (EMTMN-2007) (p. 243).
[17]. Babu, B. V., Mubeen, J. S., & Chakole, P. G. (2007b). Simulation and optimization of wiped-film poly-ethylene terephthalate (PET) reactor using multiobjective differential evolution ( M O D E ) . Materials and Manufacturing Processes, 22(5), 541-552.
[18]. Babu, B. V., & Jehan, M. M. L. (2003). Differential evolution for multi-objective optimization. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 4, pp. 2696-2703). IEEE.
[19]. Babu, B. V., & Khan, M. (2007). Optimization of reactive distillation processes using differential evolution strategies. Asia?Pacific Journal of Chemical Engineering, 2(4), 322-335.
[20]. Babu, B. V., & Munawar, S. A. (2007). Differential evolution strategies for optimal design of shell-and-tube heat exchangers. Chemical Engineering Science, 62(14), 3720-3739.
[21]. Babu, B. V., & Sastry, K. K. N. (1999). Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation. Computers & Chemical Engineering, 23(3), 327-339.
[22]. Bhaskar, V., Gupta, S. K., & Ray, A. K. (2000). Multiobjective optimization of an industrial wiped?film pet reactor. AIChE Journal, 46(5), 1046-1058.
[23]. Bhaskar, V., Gupta, S. K., & Ray, A. K. (2001). Multiobjective optimization of an industrial wiped film poly (ethylene terephthalate) reactor: Some further insights. Computers & Chemical Engineering, 25(2-3), 391-407.
[24]. Chiou, J. P., & Wang, F. S. (1999). Hybrid method of evolutionar y algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process. Computers & Chemical Engineering, 23(9), 1277-1291.
[25]. Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., & Price, K. V. (1999). New ideas in Optimization. McGraw-Hill Ltd., UK.
[26]. Dasgupta, D., & Michalewicz, Z. (1997). Evolutionary Algorithms in Engineering Applications Springer, Germany.
[27]. Deb, K. (1996). Optimization for engineering design: Algorithms and examples. Prentice-Hall, India.
[28]. Deb, K. (2001). Multi-objective Optimization using Evolutionary Algorithms. New York: Wiley.
[29]. Deb, K., Mitra, K., Dewri, & R., Majumdar, S. (2004). Towards a better understanding of epoxy polymerization process using multi-objective evolutionary computation. Chemical Engineering Science, 59(20), 4261-4277.
[30]. Fan, H. Y., & Lampinen, J. (2003). A trigonometric mutation operation to differential evolution. Journal of Global Optimization, 27, 105-129.
[31]. Fonseca, C. M., & Fleming, P. J. (1995). An overview of evolutionary algorithms in multi-objective optimization. Evolutionary Computation Journal, 3(1), 1-16.
[32]. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA: Addison-Wesley.
[33]. Gujarathi, A. M., & Babu, B. V. (2009). Optimization of adiabatic styrene reactor: A hybrid multiobjective differential evolution (H-MODE) approach. Industrial & Engineering Chemistry Research, 48(24), 11115-11132
[34]. Gujarathi, A. M., & Babu, B. V. (2010a). Multiobjective Optimization of Industrial Styrene Reactor: Adiabatic and Pseudo-isothermal Operation. Chemical Engineering Science, 65(6), 2009-2026.
[35]. Gujarathi, A. M., & Babu, B. V. (2010b). Hybrid Multiobjective Differential Evolution (H-MODE) for optimization of Polyethylene Terephthalate (PET) reactor. International Journal of Bio-inspired Computation, 2(3/4), 213-221.
[36]. Gujarathi, A. M., & Babu, B. V. (2011a). Multiobjective optimization of industrial processes using elitist multiobjective differential evolution (Elitist-MODE). Materials and Manufacturing Processes, 26(3), 455-463.
[37]. Gujarathi, A. M., & Babu, B. V. (2011b). Hybrid multiobjective differential evolution for multi-objective optimization of industrial polymeric materials. Computer Methods in Materials Science, 11(3), 463-468.
[38]. Gujarathi, A. M., & Babu, B. V. (2012). Differential evolution strategies for multi-objective optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) (pp. 63- 71). Springer, India.
[39]. Gujarathi, A. M., & Babu, B. V. (2013a). Hybrid Strategy of Multi-objective Differential Evolution (H-MODE) for Multi-objective optimization. International Journal of Computational Intelligence Studies, 2(2), 157-185.
[40]. Gujarathi, A. M., & Babu, B. V. (2013b). Multiobjective Optimization of Industrial Naphtha Cracker for Production of Ethylene and Propylene. Materials and Manufacturing Processes, 28(7), 803-810.
[41]. Hawkins, D. S., Allen, D. M., & Stromberg, A. J. (2001). Determining the Number of Components in Mixtures of Linear Models. Computational Statistics & Data Analysis, 38, 15-48.
[42]. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
[43]. Joshi, R., & Sanderson, A. C. (1999). Minimal Representation Multi-Sensor Fusion using Differential Evolution. IEEE Transactions on Systems, Man and Cybernetics, Part A, 29, 63-76.
[44]. Kirkpatrick, S., Gelatt, C. D., & Vechhi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4568), 671-680.
[45]. Kumar, S., Datta, D., & Babu, B. V. (2010). Experimental data and Theoretical (Chemodel using the Differential Evolution Approach and Linear Solvation Energy Relationship Model) Predictions on Reactive Extraction of Monocarboxylic Acids using Tri-noctylamine. Journal of Chemical & Engineering Data, 55(10), 4290-4300.
[46]. Kumar, S., Datta, D., & Babu, B. V. (2011a). Estimation of Equilibrium Parameters using Differential Evolution in Reactive Extraction of Propionic Acid by Tri-n-Butyl Phosphate. Chemical Engineering and Processing: Process Intensification, 50(7), 614-622.
[47]. Kumar, S., Datta, D., & Babu, B. V. (2011b). Differential Evolution Approach for Reactive Extraction of Propionic Acid using Tri-n-Butyl Phosphate (TBP) in Kerosene and 1-Decanol. Material and Manufacturing Processes, 26(9), pp. 1222-1228.
[48]. Kyprianou, A., Worden, K., & Panet, M. (2001). Identification of Hysteretic Systems using the Differential Evolution Algorithm. Journal of Sound and Vibration, 248(2), 289-314.
[49]. Lampinen, J. (2003). A Bibliography on Differential Evolution. Lappeenranta University of Technology, Finland.
[50]. Laubriet, C., LeCorre, B., & Choi K.Y. (1991). Twophase model for continuous final stage melt polycondensation of poly ethylene terephthalate: 1. steadystate analysis. Industrial & Engineering Chemistry Research, 30(1), 2-12.
[51]. Lee, M. H., Han, C., & Chang, K. S. (1999). Dynamic Optimization of a Continuous Polymer Reactor using a Modified Differential Evolution. Industrial & Engineering Chemistry Research, 38(12), 4825-4831.
[52]. Lu, J. C., & Wang, F. S. (2001). Optimization of Low Pressure Chemical Vapour Deposition Reactors Using Hybrid Differential Evolution. Canadian Journal of Chemical Engineering, 79(2), 246-254.
[53]. Martin, H. C. S., & Choi, K. Y. (1991). Two phase m o d e l f o r c o n t i n u o u s f i n a l s t a g e m e l t polycondenstaionpoly (ethylene terephthalate): 2. analysis of dynamic behavior. Industrial & Engineering Chemistry Research, 30, 1712-1718.
[54]. Martinez, S. Z., & Coello, C. A. C. (2008). Hybridizing An evolutionary algorithm with mathematical programming technique for multi-objective optimization, In Proceedings of genetic and evolutionary Computation Conference.
[55]. Mitra, K., Deb, K., & Gupta, S. K. (1998). Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. Journal of Applied Polymer Science, 69(1), pp. 69-87.
[56]. Nelder, J. M., & Mead, R. (1965). A simplex method for function minimization. The Computational Journal, 7, 308-313.
[57]. Onwubolu, G. C., & Babu, B. V. (2004). New Optimization Techniques in Engineering. Springer-Verlag, Heidelberg, Germany.
[58]. Poloni, C., Giurgevich A., Onesti, L., & Pediroda, V. (2000). Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problems fluid dynamics. Computational Methods in Applied Mechanical Engineering, 186, 403-420.
[59]. Price, K., & Storn, R. (1997). Differential Evolution – A Simple Evolution Strategy for Fast Optimization. Dr. Dobb's Journal, 2 (4), 18-24 & 78.
[60]. Rao, S. S. (1991). Optimization Theory and nd Applications, 2 Ed. Wiley Eastern: New Delhi.
[61]. Ravindranath, K., & Mashelkar, R. A. (1984). Finishing stages of PET synthesis: a comprehensive model. American Institute of Chemical Engineers Journal, 30(3), 415-422.
[62]. Ravindranath, K., & Mashelkar, R. A. (1986a). Polyethyleneterephthalate - I : chemistry , thermodynamics and transport properties. Chemical Engineering Science, 41(9), 2197-2214.
[63]. Ravindranath, K., & Mashelkar, R. A. (1986b). Polyethylene terephthalate-II: engineering analysis. Chemical Engineering Science, 41(12), 2969-2987.
[64]. Sastry, K. K. N., Behra, L., & Nagrath, I. J. (1999). Differential evolution based fuzzy logic controller for nonlinear process control. Fundamenta Informaticae: Special Issue on Soft Computation, 37(1–2), 121–136.
[65]. Sheth, P. N., & Babu, B. V. (2009). Differential Evolution Approach for obtaining Kinetic Parameters in Non isotherma l Pyrolysis of Bioma s s. Materials and Manufacturing Processes, 24(1), 47-52.
[66]. Storn, R. (1995). Differential evolution design of an IIRfilter with requirements for magnitude and group delay. International Computer Science Institute, TR-95-018.
[67]. Wang, F. S., Su, T. L., & Jang, H. J. (2001). Hybrid Differential Evolution for Problems of Kinetic Parameter Estimation and Dynamic Optimization of an Ethanol Fermentation Process. Industrial & Engineering Chemistry Research, 40(13), 2876-2885.
[68]. Wang, F. S., & Cheng, W. M. (1999). Simultaneous optimization of feeding rate and operation parameters for fed-batch fermentation processes. Biotechnology Progress, 15(5), 949-952.
[69]. Wang, F. S., Jing, C. H., & Tsao, G. T. (1998). Fuzzydecision- making problems of fuel ethanol production using genetically engineered yeast. Industrial & Engineering Chemistry Research, 37(8), 3434-3443.
[70]. Yee, A. K. Y., Ray, A. K., & Rangiah, G. P. (2003). Multiobjective optimization of industrial styrene reactor. Computers & Chemical Engineering, 27, 111-130
If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.

Purchase Instant Access

Single Article

Print 35 35 200
Online 35 35 200
Print & Online 35 35 400