Documents disponibles écrits par cet auteur

Biomimetic adaptation of the evolutionary algorithm, NSGA-II-aJG, using the biogenetic law of embryology for intelligent optimization / Manojkumar Ramteke in Industrial & engineering chemistry research, Vol. 48 N° 17 (Septembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 17 (Septembre 2009) . - pp. 8054–8067 Titre : Biomimetic adaptation of the evolutionary algorithm, NSGA-II-aJG, using the biogenetic law of embryology for intelligent optimization Type de document : texte imprimé Auteurs : Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur Année de publication : 2009 Article en page(s) : pp. 8054–8067 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Elitist  nondominated  sorting  genetic  algorithm  Adapted  jumping  gene  operator Résumé : Several of the recent optimization techniques have been adapted from nature. The elitist nondominated sorting genetic algorithm with the adapted jumping gene operator (NSGA-II-aJG) is one such evolutionary technique inspired by genetics. This algorithm is quite useful for solving multiobjective optimization problems. The drawback of these techniques is the inordinately large amount of computational effort required for solving real-life problems, even though these techniques are quite robust as compared to conventional techniques. Their use for online optimization is particularly limited. Many industrial optimization problems require frequent changes in the objective functions as well as the decision variables, even though the system itself remains the same. Surprisingly, no algorithm has been developed which makes use of previous information for solving a different problem for the same system in a comparatively short (computational) time. The algorithm developed in this study, namely, B-NSGA-II-aJG, carries this out more intelligently using the biogenetic law of embryology. This algorithm increases the speed of convergence significantly. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801592c [article] Biomimetic adaptation of the evolutionary algorithm, NSGA-II-aJG, using the biogenetic law of embryology for intelligent optimization [texte imprimé] / Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur . - 2009 . - pp. 8054–8067. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 17 (Septembre 2009) . - pp. 8054–8067 Mots-clés : Elitist  nondominated  sorting  genetic  algorithm  Adapted  jumping  gene  operator Résumé : Several of the recent optimization techniques have been adapted from nature. The elitist nondominated sorting genetic algorithm with the adapted jumping gene operator (NSGA-II-aJG) is one such evolutionary technique inspired by genetics. This algorithm is quite useful for solving multiobjective optimization problems. The drawback of these techniques is the inordinately large amount of computational effort required for solving real-life problems, even though these techniques are quite robust as compared to conventional techniques. Their use for online optimization is particularly limited. Many industrial optimization problems require frequent changes in the objective functions as well as the decision variables, even though the system itself remains the same. Surprisingly, no algorithm has been developed which makes use of previous information for solving a different problem for the same system in a comparatively short (computational) time. The algorithm developed in this study, namely, B-NSGA-II-aJG, carries this out more intelligently using the biogenetic law of embryology. This algorithm increases the speed of convergence significantly. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801592c

Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm / Manojkumar Ramteke in Industrial & engineering chemistry research, Vol. 48 N° 21 (Novembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9671–9685 Titre : Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm Type de document : texte imprimé Auteurs : Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur Année de publication : 2010 Article en page(s) : pp. 9671–9685 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Honey  bees  Biomimicking  altruistic  behavior  Genetic  algorithm Résumé : The altruistic behavior of honey bees provides an interesting contrast to natural selection in evolutionary biology. This is biomimicked in the framework of a multiobjective optimization algorithm, namely, genetic algorithm, GA, by exploiting the concept of elitism (preserving good parents). The effects of altruism and natural selection on the total fitness of the colony are compared. This basic algorithm is used for studying the evolution process. It is then modified to enhance the convergence rates of optimization problems and to simulate the carcinogenesis of cells using multiple queens, unlike in honeycombs, mimicking other species of hymenopterans, e.g., ants, wasps, etc. This algorithm provides a new approach for studying three problems, bee evolution, optimization, and cancer, and is used to understand conflicts in animal behavior, increase the speed of convergence of optimization problems, and for an improved understanding of the causes of cancer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9004817 [article] Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm [texte imprimé] / Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur . - 2010 . - pp. 9671–9685. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9671–9685 Mots-clés : Honey  bees  Biomimicking  altruistic  behavior  Genetic  algorithm Résumé : The altruistic behavior of honey bees provides an interesting contrast to natural selection in evolutionary biology. This is biomimicked in the framework of a multiobjective optimization algorithm, namely, genetic algorithm, GA, by exploiting the concept of elitism (preserving good parents). The effects of altruism and natural selection on the total fitness of the colony are compared. This basic algorithm is used for studying the evolution process. It is then modified to enhance the convergence rates of optimization problems and to simulate the carcinogenesis of cells using multiple queens, unlike in honeycombs, mimicking other species of hymenopterans, e.g., ants, wasps, etc. This algorithm provides a new approach for studying three problems, bee evolution, optimization, and cancer, and is used to understand conflicts in animal behavior, increase the speed of convergence of optimization problems, and for an improved understanding of the causes of cancer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9004817

Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm / Manojkumar Ramteke in Industrial & engineering chemistry research, Vol. 48 N° 21 (Novembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9671–9685 Titre : Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm Type de document : texte imprimé Auteurs : Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur Année de publication : 2010 Article en page(s) : pp. 9671–9685 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Honey  bees  Biomimicking  altruistic  behavior  Genetic  algorithm Résumé : The altruistic behavior of honey bees provides an interesting contrast to natural selection in evolutionary biology. This is biomimicked in the framework of a multiobjective optimization algorithm, namely, genetic algorithm, GA, by exploiting the concept of elitism (preserving good parents). The effects of altruism and natural selection on the total fitness of the colony are compared. This basic algorithm is used for studying the evolution process. It is then modified to enhance the convergence rates of optimization problems and to simulate the carcinogenesis of cells using multiple queens, unlike in honeycombs, mimicking other species of hymenopterans, e.g., ants, wasps, etc. This algorithm provides a new approach for studying three problems, bee evolution, optimization, and cancer, and is used to understand conflicts in animal behavior, increase the speed of convergence of optimization problems, and for an improved understanding of the causes of cancer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9004817 [article] Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm [texte imprimé] / Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur . - 2010 . - pp. 9671–9685. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9671–9685 Mots-clés : Honey  bees  Biomimicking  altruistic  behavior  Genetic  algorithm Résumé : The altruistic behavior of honey bees provides an interesting contrast to natural selection in evolutionary biology. This is biomimicked in the framework of a multiobjective optimization algorithm, namely, genetic algorithm, GA, by exploiting the concept of elitism (preserving good parents). The effects of altruism and natural selection on the total fitness of the colony are compared. This basic algorithm is used for studying the evolution process. It is then modified to enhance the convergence rates of optimization problems and to simulate the carcinogenesis of cells using multiple queens, unlike in honeycombs, mimicking other species of hymenopterans, e.g., ants, wasps, etc. This algorithm provides a new approach for studying three problems, bee evolution, optimization, and cancer, and is used to understand conflicts in animal behavior, increase the speed of convergence of optimization problems, and for an improved understanding of the causes of cancer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9004817

Large - scale refinery crude oil scheduling by integrating graph representation and genetic algorithm / Manojkumar Ramteke in Industrial & engineering chemistry research, Vol. 51 N° 14 (Avril 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 14 (Avril 2012) . - pp. 5256-5272 Titre : Large - scale refinery crude oil scheduling by integrating graph representation and genetic algorithm Type de document : texte imprimé Auteurs : Manojkumar Ramteke, Auteur ; Rajagopalan Srinivasan, Auteur Année de publication : 2012 Article en page(s) : pp. 5256-5272 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Genetic  algorithm  Scheduling  Crude  oil  Refinery Résumé : Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25783435 [article] Large - scale refinery crude oil scheduling by integrating graph representation and genetic algorithm [texte imprimé] / Manojkumar Ramteke, Auteur ; Rajagopalan Srinivasan, Auteur . - 2012 . - pp. 5256-5272. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 14 (Avril 2012) . - pp. 5256-5272 Mots-clés : Genetic  algorithm  Scheduling  Crude  oil  Refinery Résumé : Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25783435

Polymerizations in the Presence of vaporization / Manojkumar Ramteke in Industrial & engineering chemistry research, Vol. 47 N° 23 (Décembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N° 23 (Décembre 2008) . - p. 9061–9071 Titre : Polymerizations in the Presence of vaporization : experimental results on nylon-6 Type de document : texte imprimé Auteurs : Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur Année de publication : 2009 Article en page(s) : p. 9061–9071 Note générale : Chemistry engineering Langues : Anglais (eng) Mots-clés : Polymerizations  Vaporization  Results  on  Nylon-6 Résumé : This study deals with the hydrolytic step-growth polymerization of ε-caprolactam to produce nylon-6 in a semibatch reactor at near industrial conditions. ε-caprolactam is polymerized in a 1.6 L stainless steel reactor at three different initial water concentrations, 4.43% (by mass), 2.52%, and 3.45%, respectively. During the polymerization, the values of the temperature and the pressure are controlled and recorded. Samples of the liquid reaction mass are taken from the reactor at different times and analyzed. The monomer conversions are obtained gravimetrically (in terms of water extractibles) as well as by using gas chromatography. The samples are also analyzed for the degree of polymerization using amide and acid end-group concentrations. The parameters are tuned using one set of data with genetic algorithm. The tuned parameters are then used to predict the second set of data. In the simulation, the poly-NRTL model is used to describe the vapor−liquid equilibria. The simulated values match well with the experimental values. The tuned model gives reasonably good results. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800287d [article] Polymerizations in the Presence of vaporization : experimental results on nylon-6 [texte imprimé] / Manojkumar Ramteke, Auteur ; Santosh K. Gupta, Auteur . - 2009 . - p. 9061–9071. Chemistry engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N° 23 (Décembre 2008) . - p. 9061–9071 Mots-clés : Polymerizations  Vaporization  Results  on  Nylon-6 Résumé : This study deals with the hydrolytic step-growth polymerization of ε-caprolactam to produce nylon-6 in a semibatch reactor at near industrial conditions. ε-caprolactam is polymerized in a 1.6 L stainless steel reactor at three different initial water concentrations, 4.43% (by mass), 2.52%, and 3.45%, respectively. During the polymerization, the values of the temperature and the pressure are controlled and recorded. Samples of the liquid reaction mass are taken from the reactor at different times and analyzed. The monomer conversions are obtained gravimetrically (in terms of water extractibles) as well as by using gas chromatography. The samples are also analyzed for the degree of polymerization using amide and acid end-group concentrations. The parameters are tuned using one set of data with genetic algorithm. The tuned parameters are then used to predict the second set of data. In the simulation, the poly-NRTL model is used to describe the vapor−liquid equilibria. The simulated values match well with the experimental values. The tuned model gives reasonably good results. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800287d