Genetic algorithms with filters for optimal control problems in fed-batch bioreactors

Sarkar, Debasis and Modak, Jayant M (2004) Genetic algorithms with filters for optimal control problems in fed-batch bioreactors. In: Bioprocess and Biosystems Engineering, 26 (5). pp. 295-306.

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Abstract

When using a genetic algorithm (GA) to solve optimal control problems that can arise in a fed-batch bioreactor, the most obvious direct approach is to rely on a finite dimensional discretization of the optimal control problem into a nonlinear programming problem.Usually only the control function is discretized, and the continuous control function is approximated by a series of piecewise constant functions. Even though the piecewise discretized controls that the GA produces for the optimal control problem may give good performances, the control policies often show very high activity and differ considerably from those obtained using a continuous optimization strategy. The present study introduces a few filters into a realcoded genetic algorithm as additional operators and investigates the smoothing capabilities of the filters employed. It is observed that inclusion of a filter Significantly smoothens the optimal control profile and often encourages the convergence of the algorithm. The applicability of the technique is illustrated by solving two previously reported optimal control problems in fed-batch bioreactors that are known to have singular arcs.

Item Type:	Journal Article
Publication:	Bioprocess and Biosystems Engineering
Publisher:	Springer
Additional Information:	The copyright belongs to Springer.
Keywords:	Genetic algorithm;Smoothing operator;Filter;Fed-batch bioreactor;Optimization
Department/Centre:	Division of Mechanical Sciences > Chemical Engineering
Date Deposited:	06 Jun 2006
Last Modified:	19 Sep 2010 04:29
URI:	http://eprints.iisc.ac.in/id/eprint/7539

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