A Bacterial Foraging Optimization Algorithm Applied to the Synthesis of Polyacrylamide Hydrogels
The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.
[1] J. M. Gonzalez-Saiz and C. Pizarro, “Polyacrylamide gels as support for enzyme immobilization by entrapment. Effect of polyelectrolyte carrier, pH and temperature on enzyme action and kinetics parameters”, European Polymer Journal, vol. 37, no. 3, 2001, pp. 435–444.
[2] H. Ghandehari, P. Kopeckova and J. Kopecek, “In vitro degradation of pH-sensitive hydrogels containing aromatic azo bonds”, Biomaterials, vol. 18, 1997, pp. 861–872.
[3] V. Compan, J. Guzmaun and E. Riade, “A potentiostatic study of oxygen transmissibility and permeability through hydrogel membranes”, Biomaterials, vol. 23, 1998, pp. 2139–2145.
[4] H. Park, Hydrogels in Bioapplications: Hydrogels and Biodegradable Polymers for Bioapplications, eds. R. M. Ottenbrite, S. J. Huang, K. Park, American Chemical Society, Washington D. C., 1996, pp. 2–10.
[5] L. H. Christensen, V. B. Breiting, A. Aasted, A. Jorgensen and I. Kebuladze, “Long-term effects of polyacrylamide hydrogel on human breast tissue”, Plastic and Reconstructive Surgery, vol. 111, no. 6, 2003, pp. 1883–1890.
[6] A. El-Hag Ali, H. A. Shawky, H. A. Abd El Rehim and E. A. Hegazy, “Synthesis and characterization of PVP/AAc copolymerhydrogel and its applications in the removalof heavy metals from aqueous solution”, European Polymer Journal, vol. 39, no. 12, 2003, pp. 23–37.
[7] J. Tirthankar, C. R. Bidhan and M. Sukumar, “Biodegradable film”, Polymer Degradation and Stability, vol. 37, 2001, pp. 861–864.
[8] S. Zlatkovic and L. Raskovic, “The effect of the polyacrylamide, polyvinylalcohol, and carboxymethylcellulose on the aggregation of the soil and on the growth of the plants”, Facta Universitatis, vol. 1, no. 3, 1998, pp. 17–23.
[9] J. P. Baker, L. H. Hong, H. W. Blanch and J. M. Prausnitz, “Effect of Initial Total Monomer Concentration on the Swelling Behavior of Cationic Acrylamide-Based Hydrogels”, Macromolecules, vol. 27, 1994, pp. 14–46.
[10] C. Mihăilescu, A. Dumitrescu, B. C. Simionescu and V. Bulacovschi, “Synthesis of polyacrylamide-based hydrogels by simultaneous polymerization/crosslinking”, Revue Roumaine de Chimie, vol 52, no. 11, 2007, pp. 1071–1076.
[11] S. Curteanu, A. Dumitrescu, C. Mihăilescu and B. C. Simionescu, “Neural network modeling applied to polyacrylamide based hydrogels synthetized by single step process”, Polymer-Plastics Technology and Engineering, vol. 47, 2008, pp. 1061–1071.
[12] S. Curteanu, A. Dumitrescu, C. Mihăilescu and B. C. Simionescu, “The synthesis of polyacrylamide-based multicomponent hydrogels. A neural network modeling”, Journal of Macromolecular Science, Part A Pure and Applied Chemistry, vol. A46, no. 4, 2009, pp. 368–380.
[13] J. Brownlee, Clever Algorithms: Nature-Inspired Programming Recipes, chapter: “Bacterial Foraging Optimization Algorithm”, http://www.cleveralgorithms.com/nature-inspired/swarm/bfoa.html, 2012 (last accessed: 17 April 2019).
[14] S. Das, A. Biswas, S. Dasgupta and A. Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications”, Foundations of Computational Intelligence, vol. 3: Global Optimization, 2009, pp. 23–55.
[15] Y. Liu and K. M. Passino, “Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors”, Journal of Optimization Theory and Applications, vol. 115, no. 3, 2002, pp. 603–628.
[16] S. D. Müller, J. Marchetto, S. Airaghi and P. Koumoutsakos, “Optimization Based on Bacterial Chemotaxis”, IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, 2002, pp. 16–29.
[17] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine, vol. 22, no. 3, 2002, pp. 52–67.
[18] K. M. Passino, “Bacterial Foraging Optimization”, International Journal of Swarm Intelligence Research, vol. 1, no. 1, 2010, pp. 1–16.
[1] J. M. Gonzalez-Saiz and C. Pizarro, “Polyacrylamide gels as support for enzyme immobilization by entrapment. Effect of polyelectrolyte carrier, pH and temperature on enzyme action and kinetics parameters”, European Polymer Journal, vol. 37, no. 3, 2001, pp. 435–444.
[2] H. Ghandehari, P. Kopeckova and J. Kopecek, “In vitro degradation of pH-sensitive hydrogels containing aromatic azo bonds”, Biomaterials, vol. 18, 1997, pp. 861–872.
[3] V. Compan, J. Guzmaun and E. Riade, “A potentiostatic study of oxygen transmissibility and permeability through hydrogel membranes”, Biomaterials, vol. 23, 1998, pp. 2139–2145.
[4] H. Park, Hydrogels in Bioapplications: Hydrogels and Biodegradable Polymers for Bioapplications, eds. R. M. Ottenbrite, S. J. Huang, K. Park, American Chemical Society, Washington D. C., 1996, pp. 2–10.
[5] L. H. Christensen, V. B. Breiting, A. Aasted, A. Jorgensen and I. Kebuladze, “Long-term effects of polyacrylamide hydrogel on human breast tissue”, Plastic and Reconstructive Surgery, vol. 111, no. 6, 2003, pp. 1883–1890.
[6] A. El-Hag Ali, H. A. Shawky, H. A. Abd El Rehim and E. A. Hegazy, “Synthesis and characterization of PVP/AAc copolymerhydrogel and its applications in the removalof heavy metals from aqueous solution”, European Polymer Journal, vol. 39, no. 12, 2003, pp. 23–37.
[7] J. Tirthankar, C. R. Bidhan and M. Sukumar, “Biodegradable film”, Polymer Degradation and Stability, vol. 37, 2001, pp. 861–864.
[8] S. Zlatkovic and L. Raskovic, “The effect of the polyacrylamide, polyvinylalcohol, and carboxymethylcellulose on the aggregation of the soil and on the growth of the plants”, Facta Universitatis, vol. 1, no. 3, 1998, pp. 17–23.
[9] J. P. Baker, L. H. Hong, H. W. Blanch and J. M. Prausnitz, “Effect of Initial Total Monomer Concentration on the Swelling Behavior of Cationic Acrylamide-Based Hydrogels”, Macromolecules, vol. 27, 1994, pp. 14–46.
[10] C. Mihăilescu, A. Dumitrescu, B. C. Simionescu and V. Bulacovschi, “Synthesis of polyacrylamide-based hydrogels by simultaneous polymerization/crosslinking”, Revue Roumaine de Chimie, vol 52, no. 11, 2007, pp. 1071–1076.
[11] S. Curteanu, A. Dumitrescu, C. Mihăilescu and B. C. Simionescu, “Neural network modeling applied to polyacrylamide based hydrogels synthetized by single step process”, Polymer-Plastics Technology and Engineering, vol. 47, 2008, pp. 1061–1071.
[12] S. Curteanu, A. Dumitrescu, C. Mihăilescu and B. C. Simionescu, “The synthesis of polyacrylamide-based multicomponent hydrogels. A neural network modeling”, Journal of Macromolecular Science, Part A Pure and Applied Chemistry, vol. A46, no. 4, 2009, pp. 368–380.
[13] J. Brownlee, Clever Algorithms: Nature-Inspired Programming Recipes, chapter: “Bacterial Foraging Optimization Algorithm”, http://www.cleveralgorithms.com/nature-inspired/swarm/bfoa.html, 2012 (last accessed: 17 April 2019).
[14] S. Das, A. Biswas, S. Dasgupta and A. Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications”, Foundations of Computational Intelligence, vol. 3: Global Optimization, 2009, pp. 23–55.
[15] Y. Liu and K. M. Passino, “Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors”, Journal of Optimization Theory and Applications, vol. 115, no. 3, 2002, pp. 603–628.
[16] S. D. Müller, J. Marchetto, S. Airaghi and P. Koumoutsakos, “Optimization Based on Bacterial Chemotaxis”, IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, 2002, pp. 16–29.
[17] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine, vol. 22, no. 3, 2002, pp. 52–67.
[18] K. M. Passino, “Bacterial Foraging Optimization”, International Journal of Swarm Intelligence Research, vol. 1, no. 1, 2010, pp. 1–16.
@article{"International Journal of Information, Control and Computer Sciences:79193", author = "Florin Leon and Silvia Curteanu", title = "A Bacterial Foraging Optimization Algorithm Applied to the Synthesis of Polyacrylamide Hydrogels", abstract = "The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.
", keywords = "Bacterial foraging optimization, hydrogels, neural networks, modeling. ", volume = "13", number = "10", pages = "520-8", }
