Sinossi
In this book, we consider the benchmark quadratic assignment problem which is very difficult NP-hard problem that has several practical applications. Several exact and heuristic algorithms are developed for solving the problem. In general, large sized instances cannot easily be solved optimally by an exact algorithm, but there are some situations where only exact optimal solution is required. Hence, we first present a reformulation of the problem, and then we apply simple and data-guided lexisearch algorithm to obtain exact optimal solutions to the problem. We also develop simple and improved genetic algorithms using sequential constructive crossover operator to find heuristic solution to the problem. Finally, a hybrid algorithm that combines lexisearch and genetic algorithms is developed. The proposed algorithm uses lexisearch algorithm to generate initial population, self-adaptive three crossover operators, and randomly one of four mutation operators, restricted combined mutation operator as local search, and multi-parent sequential constructive crossover as immigration method. Experimental results on benchmark QAPLIB instances show the effectiveness of the developed algorithms.
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Algorithms for the Quadratic Assignment Problem
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LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139814634
ISBN 13: 9786139814633
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, we consider the benchmark quadratic assignment problem which is very difficult NP-hard problem that has several practical applications. Several exact and heuristic algorithms are developed for solving the problem. In general, large sized instances cannot easily be solved optimally by an exact algorithm, but there are some situations where only exact optimal solution is required. Hence, we first present a reformulation of the problem, and then we apply simple and data-guided lexisearch algorithm to obtain exact optimal solutions to the problem. We also develop simple and improved genetic algorithms using sequential constructive crossover operator to find heuristic solution to the problem. Finally, a hybrid algorithm that combines lexisearch and genetic algorithms is developed. The proposed algorithm uses lexisearch algorithm to generate initial population, self-adaptive three crossover operators, and randomly one of four mutation operators, restricted combined mutation operator as local search, and multi-parent sequential constructive crossover as immigration method. Experimental results on benchmark QAPLIB instances show the effectiveness of the developed algorithms. 104 pp. Englisch. Codice articolo 9786139814633
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahmed Zakir HussainDr. Zakir H. Ahmed is an Associate Professor in the Department of Computer Science at Al Imam Mohammad Ibn Saud Islamic University, Saudi Arabia. He obtained MSc in Mathematics (Gold Medalist), MTech in Information. Codice articolo 385872116
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Algorithms for the Quadratic Assignment Problem
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LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139814634
ISBN 13: 9786139814633
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -In this book, we consider the benchmark quadratic assignment problem which is very difficult NP-hard problem that has several practical applications. Several exact and heuristic algorithms are developed for solving the problem. In general, large sized instances cannot easily be solved optimally by an exact algorithm, but there are some situations where only exact optimal solution is required. Hence, we first present a reformulation of the problem, and then we apply simple and data-guided lexisearch algorithm to obtain exact optimal solutions to the problem. We also develop simple and improved genetic algorithms using sequential constructive crossover operator to find heuristic solution to the problem. Finally, a hybrid algorithm that combines lexisearch and genetic algorithms is developed. The proposed algorithm uses lexisearch algorithm to generate initial population, self-adaptive three crossover operators, and randomly one of four mutation operators, restricted combined mutation operator as local search, and multi-parent sequential constructive crossover as immigration method. Experimental results on benchmark QAPLIB instances show the effectiveness of the developed algorithms.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch. Codice articolo 9786139814633
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Algorithms for the Quadratic Assignment Problem
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Taschenbuch. Condizione: Neu. Algorithms for the Quadratic Assignment Problem | Zakir Hussain Ahmed | Taschenbuch | 104 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139814633 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 115847708
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Algorithms for the Quadratic Assignment Problem
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139814634
ISBN 13: 9786139814633
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, we consider the benchmark quadratic assignment problem which is very difficult NP-hard problem that has several practical applications. Several exact and heuristic algorithms are developed for solving the problem. In general, large sized instances cannot easily be solved optimally by an exact algorithm, but there are some situations where only exact optimal solution is required. Hence, we first present a reformulation of the problem, and then we apply simple and data-guided lexisearch algorithm to obtain exact optimal solutions to the problem. We also develop simple and improved genetic algorithms using sequential constructive crossover operator to find heuristic solution to the problem. Finally, a hybrid algorithm that combines lexisearch and genetic algorithms is developed. The proposed algorithm uses lexisearch algorithm to generate initial population, self-adaptive three crossover operators, and randomly one of four mutation operators, restricted combined mutation operator as local search, and multi-parent sequential constructive crossover as immigration method. Experimental results on benchmark QAPLIB instances show the effectiveness of the developed algorithms. Codice articolo 9786139814633
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Algorithms for the Quadratic Assignment Problem
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139814634
ISBN 13: 9786139814633
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paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Codice articolo ERICA82361398146346
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