On Multi-objective Optimization Based on Ant Colony Optimization: Developing an Ant Colony Optimization Algorithm for Engineering Applications - Softcover
Synopsis
Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications.
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About the Author
Rizk M. Rizk Allah obtained his Ph.D in Engineering Mathematics from Faculty of Engineering ,Menoufia University, Egypt. He has been with the Basic Engineering Sciences Department, Faculty of Engineering ,Menoufia University, since 2005. He is especially interested in swarm intelligence techniques,evolutionary algorithms and operations research.
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On Multi-objective Optimization Based on Ant Colony Optimization
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LAP LAMBERT Academic Publishing Jun 2014, 2014
ISBN 10: 3659553220
ISBN 13: 9783659553226
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications. 176 pp. Englisch. Seller Inventory # 9783659553226
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On Multi-objective Optimization Based on Ant Colony Optimization
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Masoud Rizk Allah RizkRizk M. Rizk Allah obtained his Ph.D in Engineering Mathematics from Faculty of Engineering ,Menoufia University, Egypt. He has been with the Basic Engineering Sciences Department, Faculty of Engineering ,Menouf. Seller Inventory # 5164378
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On Multi-objective Optimization Based on Ant Colony Optimization | Developing an Ant Colony Optimization Algorithm for Engineering Applications
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Taschenbuch. Condition: Neu. On Multi-objective Optimization Based on Ant Colony Optimization | Developing an Ant Colony Optimization Algorithm for Engineering Applications | Rizk Masoud Rizk Allah (u. a.) | Taschenbuch | 176 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659553226 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 105217323
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On Multi-objective Optimization Based on Ant Colony Optimization
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LAP LAMBERT Academic Publishing Jun 2014, 2014
ISBN 10: 3659553220
ISBN 13: 9783659553226
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch. Seller Inventory # 9783659553226
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On Multi-objective Optimization Based on Ant Colony Optimization : Developing an Ant Colony Optimization Algorithm for Engineering Applications
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ISBN 13: 9783659553226
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications. Seller Inventory # 9783659553226