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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

Modeling the cytotoxicity of cisplatin / Vishnu Sresht in Industrial & engineering chemistry research, Vol. 50 N° 23 (Décembre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 23 (Décembre 2011) . - pp. 12872–12880 Titre : Modeling the cytotoxicity of cisplatin Type de document : texte imprimé Auteurs : Vishnu Sresht, Auteur ; Jayesh R. Bellare, Auteur ; Santosh K. Gupta, Auteur Année de publication : 2012 Article en page(s) : pp. 12872–12880 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Cytotoxicity  Cisplatin Résumé : Cisplatin has been the mainstay of chemotherapeutic efforts against several types of cancers for over 30 years. However, there is still much that is unknown regarding its mechanism of action. The several serious side effects of cisplatin therapy and the lack of consensus regarding the ideal dosage regimen necessitate the development of a quantitative model for cisplatin action. A first-level mathematical model is presented for the cytotoxicity of cisplatin which predicts the survival of cancer cells when subjected to a given dose of this antitumor drug. This model comprises of (i) an uptake model that describes the movement of cisplatin molecules from the extracellular medium to the cell nucleus and the formation (and repair) of cisplatin−DNA adducts and (ii) a tolerance model that relates the quantity of cisplatin−DNA adducts formed inside the cell nuclei in a tumor to the fraction of tumor cells that are killed. The predictions of the model are in good agreement with in vitro experimental data obtained for carcinoma cell lines. To the best of our knowledge, this is the first model combining the uptake of cisplatin (consistent with the biochemical mechanism of action) with cell death (based on cisplatin−DNA adducts). It predicts a Pareto-type set of dosage regimen (dosage, Cext, vs exposure time, texp) of cisplatin to obtain a desired chemotherapeutic efficacy. Some reported patient-data is consistent with this prediction DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie102360e [article] Modeling the cytotoxicity of cisplatin [texte imprimé] / Vishnu Sresht, Auteur ; Jayesh R. Bellare, Auteur ; Santosh K. Gupta, Auteur . - 2012 . - pp. 12872–12880. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 23 (Décembre 2011) . - pp. 12872–12880 Mots-clés : Cytotoxicity  Cisplatin Résumé : Cisplatin has been the mainstay of chemotherapeutic efforts against several types of cancers for over 30 years. However, there is still much that is unknown regarding its mechanism of action. The several serious side effects of cisplatin therapy and the lack of consensus regarding the ideal dosage regimen necessitate the development of a quantitative model for cisplatin action. A first-level mathematical model is presented for the cytotoxicity of cisplatin which predicts the survival of cancer cells when subjected to a given dose of this antitumor drug. This model comprises of (i) an uptake model that describes the movement of cisplatin molecules from the extracellular medium to the cell nucleus and the formation (and repair) of cisplatin−DNA adducts and (ii) a tolerance model that relates the quantity of cisplatin−DNA adducts formed inside the cell nuclei in a tumor to the fraction of tumor cells that are killed. The predictions of the model are in good agreement with in vitro experimental data obtained for carcinoma cell lines. To the best of our knowledge, this is the first model combining the uptake of cisplatin (consistent with the biochemical mechanism of action) with cell death (based on cisplatin−DNA adducts). It predicts a Pareto-type set of dosage regimen (dosage, Cext, vs exposure time, texp) of cisplatin to obtain a desired chemotherapeutic efficacy. Some reported patient-data is consistent with this prediction DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie102360e

Multiobjective optimal design of heat exchanger networks using new adaptations of the elitist nondominated sorting genetic algorithm, NSGA-II / Aaditya Agarwal in Industrial & engineering chemistry research, Vol. 47 N°10 (Mai 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°10 (Mai 2008) . - p. 3489–3501 Titre : Multiobjective optimal design of heat exchanger networks using new adaptations of the elitist nondominated sorting genetic algorithm, NSGA-II Type de document : texte imprimé Auteurs : Aaditya Agarwal, Auteur ; Santosh K. Gupta, Auteur Année de publication : 2008 Article en page(s) : p. 3489–3501 Note générale : Bibliogr. p. 3500-3501 Langues : Anglais (eng) Mots-clés : Heat  exchanger  networks;  NSGA-II-sJG Résumé : A new approach for generating optimal heat exchanger networks (HENs) is described that does not use any heuristics. This approach involves generating the number of intermediate temperatures in each of the hot and cold streams and their values, randomly, using the binary coded NSGA-II-sJG. The substreams so generated are then matched randomly. This procedure results in a variable number of decision variables in each solution (chromosome). Dummy decision variables are introduced so as to make the length of each chromosome the same. A new crossover strategy, crossA, as well as a few other adaptations, are described that enable faster convergence to the optimal solution(s). Three single-objective problems involving the minimization of the annualized cost are solved and the results compared with those reported in the literature. Thereafter, a few problems with two- and three-objective functions are solved. In these, the objective functions are selected from among the annualized cost, the amount of (hot + cold) utilities required (these are important due the environmental issues associated with them), the energy recovery, and the total number of units. To the best of our knowledge, such multiobjective optimization of HENs has not been reported in the open literature yet. A decision maker can choose any of the solutions from among the set of several nondominated (equally good) Pareto-optimal solutions generated. These are more meaningful than those obtained using single objective functions. Though the algorithm developed is specific to HENs, it can easily be applied to other similar optimization problems. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070805g [article] Multiobjective optimal design of heat exchanger networks using new adaptations of the elitist nondominated sorting genetic algorithm, NSGA-II [texte imprimé] / Aaditya Agarwal, Auteur ; Santosh K. Gupta, Auteur . - 2008 . - p. 3489–3501. Bibliogr. p. 3500-3501 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°10 (Mai 2008) . - p. 3489–3501 Mots-clés : Heat  exchanger  networks;  NSGA-II-sJG Résumé : A new approach for generating optimal heat exchanger networks (HENs) is described that does not use any heuristics. This approach involves generating the number of intermediate temperatures in each of the hot and cold streams and their values, randomly, using the binary coded NSGA-II-sJG. The substreams so generated are then matched randomly. This procedure results in a variable number of decision variables in each solution (chromosome). Dummy decision variables are introduced so as to make the length of each chromosome the same. A new crossover strategy, crossA, as well as a few other adaptations, are described that enable faster convergence to the optimal solution(s). Three single-objective problems involving the minimization of the annualized cost are solved and the results compared with those reported in the literature. Thereafter, a few problems with two- and three-objective functions are solved. In these, the objective functions are selected from among the annualized cost, the amount of (hot + cold) utilities required (these are important due the environmental issues associated with them), the energy recovery, and the total number of units. To the best of our knowledge, such multiobjective optimization of HENs has not been reported in the open literature yet. A decision maker can choose any of the solutions from among the set of several nondominated (equally good) Pareto-optimal solutions generated. These are more meaningful than those obtained using single objective functions. Though the algorithm developed is specific to HENs, it can easily be applied to other similar optimization problems. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070805g

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

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