Multi-objective optimal control of dynamic bioprocesses using ACADO Toolkit

被引:27
作者
Logist, Filip [1 ]
Telen, Dries [1 ]
Houska, Boris [2 ]
Diehl, Moritz [2 ]
Van Impe, Jan [1 ]
机构
[1] Katholieke Univ Leuven, Dept Chem Engn, BioTeC & Optimizat Engn Ctr OPTEC, B-3001 Louvain, Belgium
[2] Katholieke Univ Leuven, SCD & Optimizat Engn Ctr OPTEC, Dept Elect Engn, B-3001 Louvain, Belgium
关键词
Multi-objective optimisation; Dynamic optimisation; Bioprocess; NORMAL-BOUNDARY INTERSECTION; NORMAL CONSTRAINT METHOD; REDUCED SQP STRATEGY; MULTICRITERIA OPTIMIZATION; SYSTEMS;
D O I
10.1007/s00449-012-0770-9
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The optimal design and operation of dynamic bioprocesses gives in practice often rise to optimisation problems with multiple and conflicting objectives. As a result typically not a single optimal solution but a set of Pareto optimal solutions exist. From this set of Pareto optimal solutions, one has to be chosen by the decision maker. Hence, efficient approaches are required for a fast and accurate generation of the Pareto set such that the decision maker can easily and systematically evaluate optimal alternatives. In the current paper the multi-objective optimisation of several dynamic bioprocess examples is performed using the freely available ACADO Multi-Objective Toolkit (http://www.acadotoolkit.org). This toolkit integrates efficient multiple objective scalarisation strategies (e.g., Normal Boundary Intersection and (Enhanced) Normalised Normal Constraint) with fast deterministic approaches for dynamic optimisation (e.g., single and multiple shooting). It has been found that the toolkit is able to efficiently and accurately produce the Pareto sets for all bioprocess examples. The resulting Pareto sets are added as supplementary material to this paper.
引用
收藏
页码:151 / 164
页数:14
相关论文
共 38 条
[1]   Dynamic optimization of chemical and biochemical processes using restricted second-order information [J].
Balsa-Canto, E ;
Banga, JR ;
Alonso, AA ;
Vassiliadis, VS .
COMPUTERS & CHEMICAL ENGINEERING, 2001, 25 (4-6) :539-546
[2]   SOLUTION OF DYNAMIC OPTIMIZATION PROBLEMS BY SUCCESSIVE QUADRATIC-PROGRAMMING AND ORTHOGONAL COLLOCATION [J].
BIEGLER, LT .
COMPUTERS & CHEMICAL ENGINEERING, 1984, 8 (3-4) :243-247
[3]  
Bock HG., 1983, NUMERICAL TREATMENT, P95, DOI [10. 1007/978-1-4684-7324-7_7, DOI 10.1007/978-1-4684-7324-7_7, DOI 10.1007/978-1-4684-7324-77]
[4]   OPTIMAL-CONTROL COMPUTATION FOR DIFFERENTIAL-ALGEBRAIC PROCESS SYSTEMS WITH GENERAL CONSTRAINTS [J].
CHEN, CT ;
HWANG, C .
CHEMICAL ENGINEERING COMMUNICATIONS, 1990, 97 :9-26
[5]   Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems [J].
Das, I ;
Dennis, JE .
SIAM JOURNAL ON OPTIMIZATION, 1998, 8 (03) :631-657
[6]   A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems [J].
Das, I ;
Dennis, JE .
STRUCTURAL OPTIMIZATION, 1997, 14 (01) :63-69
[7]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[8]  
Deb K., 2010, MULTIOBJECTIVE OPTIM
[9]  
Eichfelder G, 2008, VECTOR OPTIM, P1, DOI 10.1007/978-3-540-79159-1
[10]  
Griewank A., 1989, MATH PROGRAMMING REC, V6, P83