Optimization of biogas production with computational intelligence a comparative study

Biogas plants are reliable sources of energy based on renewable materials including organic waste. There is a high demand from industry to run these plants efficiently, which leads to a highly complex simulation and optimization problem. A comparison of several algorithms from computational intelligence to solve this problem is presented in this study. The sequential parameter optimization was used to determine improved parameter settings for these algorithms in an automated manner. We demonstrate that genetic algorithm and particle swarm optimization based approaches were outperformed by differential evolution and covariance matrix adaptation evolution strategy. Compared to previously presented results, our approach required only one tenth of the number of function evaluations.

[1]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[2]  Brian Birge,et al.  PSOt - a particle swarm optimization toolbox for use with Matlab , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[3]  Thomas Bartz-Beielstein,et al.  Sequential parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[4]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[5]  Thomas Bartz-Beielstein,et al.  Experimental Research in Evolutionary Computation - The New Experimentalism , 2010, Natural Computing Series.

[6]  Günter Rudolph,et al.  Solving multimodal problems via multiobjective techniques with Application to phase equilibrium detection , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Eyke Hüllermeier,et al.  Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules , 2009, German Conference on Bioinformatics.

[8]  Günter Rudolph,et al.  Economic optimization of non-sharp separation sequences by means of evolutionary algorithms , 2008, Comput. Chem. Eng..

[9]  Nicola Beume,et al.  Intelligent anti-grouping in real-time strategy games , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

[10]  Seán McLoone,et al.  Biogas plant optimization using Genetic Algorithms and Particle Swarm Optimization , 2008 .

[11]  Michèle Sebag,et al.  Analysis of adaptive operator selection techniques on the royal road and long k-path problems , 2009, GECCO.

[12]  Thomas Bartz-Beielstein,et al.  A New Clustering-Based Evolutionary Algorithm for Real- Valued Multimodal Optimization , 2009 .

[13]  Klaus Lucas,et al.  Emergence, Analysis and Evolution of Structures: Concepts and Strategies Across Disciplines , 2009 .

[14]  Thomas Bartz-Beielstein,et al.  Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches , 2010, Experimental Methods for the Analysis of Optimization Algorithms.