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Evaluation of Lipase Production by Genetic Algorithm and Particle Swarm Optimization and Their Comparative Study

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Abstract

This paper presents the nature-inspired genetic algorithm (GA) and particle swarm optimization (PSO) approaches for optimization of fermentation conditions of lipase production for enhanced lipase activity. The central composite non-linear regression model of lipase production served as the optimization problem for PSO and GA approaches. The overall optimized fermentation conditions obtained thereby, when verified experimentally, have brought about a significant improvement (more than 15 U/gds (gram dry substrate)) in the lipase titer value. The performance of both optimization approaches in terms of computational time and convergence rate has been compared. The results show that the PSO approach (96.18 U/gds in 46 generations) has slightly better performance and possesses better convergence and computational efficiency than the GA approach (95.34 U/gds in 337 generations). Hence, the proposed PSO approach with the minimal parameter tuning is a viable tool for optimization of fermentation conditions of enzyme production.

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Acknowledgment

The financial support from the Ministry of Human Resource Development, Government of India is acknowledged gratefully.

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Authors and Affiliations

  1. Microbial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India

    Vijay Kumar Garlapati & Rintu Banerjee

  2. Mechanical Engineering Department, Rungta College of Engineering and Technology, Bhilai, CG-490 024, India

    Pandu Ranga Vundavilli

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  1. Vijay Kumar Garlapati
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  2. Pandu Ranga Vundavilli
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  3. Rintu Banerjee
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Correspondence to Rintu Banerjee.

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Garlapati, V.K., Vundavilli, P.R. & Banerjee, R. Evaluation of Lipase Production by Genetic Algorithm and Particle Swarm Optimization and Their Comparative Study. Appl Biochem Biotechnol 162, 1350–1361 (2010). https://doi.org/10.1007/s12010-009-8895-2

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  • DOI: https://doi.org/10.1007/s12010-009-8895-2

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