improving bees algorithm complex de otri sameh (6 resultados)

Taschenbuch. Condición: Neu. Improving the Bees Algorithm for Complex Optimisation Problems | Sameh Otri | Taschenbuch | 228 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659913129 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condición: Brand New. 228 pages. 8.66x5.91x0.52 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -An improved swarm-based optimization algorithm from the Bees Algorithm family for solving complex optimization problems is proposed. The algorithm performs a form of exploitative local search combined with random exploratory global search. This thesis details the development and optimization of this algorithm and demonstrates its robustness. The development includes a new method of tuning the Bees Algorithm called Meta Bees Algorithm and the functionality of the proposed method is compared to the standard Bees Algorithm and to a range of state-of-the-art optimisation algorithms. A new fitness evaluation method has been developed to enable the Bees Algorithm to solve a stochastic optimisation problem. The new modified Bees Algorithm was tested on the optimisation of parameter values for the Ant Colony Optimisation algorithm when solving Travelling Salesman Problems. Finally, the Bees Algorithm has been adapted and employed to solve complex combinatorial problems. The algorithm has been combined with two neighbourhood operators to solve such problems. The performance of the proposed Bees Algorithm has been tested on a number of travelling salesman problems. 228 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Otri SamehDr. Otri is an assistant Professor in the College of Computer at Qassim University in the Kingdom of Saudi Arabia since 2013. Dr. Otri holds a PhD and an MSc from Cardiff University in Wales. His undergraduate degree is fro.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -An improved swarm-based optimization algorithm from the Bees Algorithm family for solving complex optimization problems is proposed. The algorithm performs a form of exploitative local search combined with random exploratory global search. This thesis details the development and optimization of this algorithm and demonstrates its robustness. The development includes a new method of tuning the Bees Algorithm called Meta Bees Algorithm and the functionality of the proposed method is compared to the standard Bees Algorithm and to a range of state-of-the-art optimisation algorithms. A new fitness evaluation method has been developed to enable the Bees Algorithm to solve a stochastic optimisation problem. The new modified Bees Algorithm was tested on the optimisation of parameter values for the Ant Colony Optimisation algorithm when solving Travelling Salesman Problems. Finally, the Bees Algorithm has been adapted and employed to solve complex combinatorial problems. The algorithm has been combined with two neighbourhood operators to solve such problems. The performance of the proposed Bees Algorithm has been tested on a number of travelling salesman problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 228 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - An improved swarm-based optimization algorithm from the Bees Algorithm family for solving complex optimization problems is proposed. The algorithm performs a form of exploitative local search combined with random exploratory global search. This thesis details the development and optimization of this algorithm and demonstrates its robustness. The development includes a new method of tuning the Bees Algorithm called Meta Bees Algorithm and the functionality of the proposed method is compared to the standard Bees Algorithm and to a range of state-of-the-art optimisation algorithms. A new fitness evaluation method has been developed to enable the Bees Algorithm to solve a stochastic optimisation problem. The new modified Bees Algorithm was tested on the optimisation of parameter values for the Ant Colony Optimisation algorithm when solving Travelling Salesman Problems. Finally, the Bees Algorithm has been adapted and employed to solve complex combinatorial problems. The algorithm has been combined with two neighbourhood operators to solve such problems. The performance of the proposed Bees Algorithm has been tested on a number of travelling salesman problems.