Abstract
Systematic and simultaneous optimization of a collection of objectives is called multiobjective or multicriteria optimization. These sorts of optimization procedures are becoming commonplace in fields involving engineering design, process and system optimization. In this work, the multiobjective (MO) optimization of the bioactive compound extraction process was carried out. Using the Normal Boundary Intersection (NBI) approach the MO optimization problem is transformed into a weighted form called the beta-subproblem. This subproblem is then solved using two evolutionary strategies (differential evolution (DE) and genetic algorithm (GA)). Using these evolutionary strategies, the solutions to the extraction process which form the efficient Pareto frontier was generated. The Hypervolume Indicator (HVI) was applied to the solutions to rank the strategies based on the solution quality. Critical analyses and comparative studies were then carried out on the strategies employed in this work and that from the previous work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Deep, S.K., Katiyar, V.K.: Extraction optimization of bioactive compounds from gardenia using particle swarm optimization. In: Proceedings of Global Conference on Power Control and Optimization (2010)
Li, X., Branke, J., Kieley, M.: On performance metrics and particle swarm methods for dynamic multiobjective optimization problems. In: IEEE Congress on Evolutionary Computation, pp 576–583 (2007)
Li, X.: Better spread and convergence: particle swarm multiobjective optimization using the maximin fitness function. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 117–128. Springer, Heidelberg (2004)
Stanikov, R.B., Matusov, J.B.: Multicriteria Optimization and Engineering. Chapman and Hall, New York (1995)
Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point. In: Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms, pp 87–102 (2009)
Grosan, C.: Performance metrics for multiobjective optimization evolutionary algorithms. In: Proceedings of Conference on Applied and Industrial Mathematics (CAIM), Oradea (2003)
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Conference on Parallel Problem Solving from Nature (PPSN V), pp 292–301 (1998)
Knowles, J., Corne, D.: Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors. IEEE Transactions on Evolutionary Computation 7(2), 100–116 (2003)
Sandgren, E.: Multicriteria design optimization by goal programming. In: Adeli, H. (ed.) Advances in Design Optimization, pp. 225–265. Chapman & Hall, London (1994)
Igel, C., Hansen, N., Roth, S.: Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation 15(1), 1–28 (2007)
Storn, R., Price, K.V.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces, ICSI, Technical Report TR-95-012 (1995)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, USA (1992)
Das, I., Dennis, J.E.: Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM Journal of Optimization 8(3), 631–657 (1998)
Babu, B.V., Munawar, S.A.: Differential evolution for the optimal design of heat exchangers. In: Proceedings of All-India seminar on Chemical Engineering Progress on Resource Development: A Vision 2010 and Beyond, Bhuvaneshwar (2000)
Babu, B.V., Singh, R.P.: Synthesis & optimization of heat integrated distillation systems using differential evolution. In: Proceedings of All- India seminar on Chemical Engineering Progress on Resource Development: A Vision 2010 and Beyond, Bhuvaneshwar (2000)
Angira, R., Babu, B.V.: Optimization of non-linear chemical processes using modified differential evolution (MDE). In: Proceedings of the 2nd Indian International Conference on Artificial Intelligence, Pune, India, pp. 911–923 (2005)
Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.: An Algorithmic Framework for Multiobjective Optimization. The Scientific World Journal 2013 (2013)
Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.: Multiobjective Optimization of Green Sand Mould System Using Chaotic Differential Evolution. In: Gavrilova, M.L., Tan, C., Abraham, A. (eds.) Transactions on Computational Science XXI. LNCS, vol. 8160, pp. 145–163. Springer, Heidelberg (2013)
Elamvazuthi, I., Ganesan, T., Vasant, P.: A comparative study of HNN and Hybrid HNN-PSO techniques in the optimization of distributed generation (DG) power systems. In: 2011 International Conference on Advanced Computer Science and Information System (ICACSIS), pp 195–200. IEEE (2011)
Mirjalili, S., Lewis, A.: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization. Swarm and Evolutionary Computation 9, 1–14 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ganesan, T., Elamvazuthi, I., Vasant, P., Shaari, K.Z.K. (2015). Multiobjective Optimization of Bioactive Compound Extraction Process via Evolutionary Strategies. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-15705-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15704-7
Online ISBN: 978-3-319-15705-4
eBook Packages: Computer ScienceComputer Science (R0)