Hédia Zardi

BEEDEA's Performance on Knapsack problem

Study of the performance of the Balanced Explore Exploit Distributed Evolutionary Algorithm ¿BEEDEA¿ on the multiobjective knapsack problem

• Broschiertes Buch

Produktbeschreibung

Most real world problems require the simultaneous optimization of multiple, competing, criteria (or objectives). In this case, the aim of a multiobjective resolution approach is to find a number of solutions known as Paretooptimal solutions. Evolutionary algorithms manipulate a population of solutions and thus are suitable to solve multi-objective optimization problems. In addition parallel evolutionary algorithms aim at reducing the computation time and solving large combinatorial optimization problems. In this work we study the performance of the "Balanced Explore Exploit Distributed Evolutionary Algorithm" (BEEDEA) [1] on the multi-objective Knapsack problem which is a combinatorial optimization problem. BEEDA is implemented after some improvements and tested on the Knapsack problem. Key words: multi-objective optimization, evolutionary algorithms, Knapsack problem, distributed metaheuristics.

Produktdetails

• Verlag: Éditions universitaires européennes
• Seitenzahl: 76
• Erscheinungstermin: 29. Mai 2011
• Englisch
• Abmessung: 220mm x 150mm x 5mm
• Gewicht: 131g
• ISBN-13: 9786131576164
• ISBN-10: 6131576165
• Artikelnr.: 33879643

Autorenporträt

Hédia ZARDI épouse Karamti, actuellement maître assistante à l'Institut Supérieur d'Informatique de Mahdia en Tunisie.