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Beedea's Performance on Knapsack Problem

Name: Beedea's Performance on Knapsack Problem
Brand: Omniscriptum
SKU: 07165112
Price: 37.29 EUR
Availability: InStock
Author: Hédia Zardi
ISBN: 9786131576164

Hédia Zardi

Lingua

Inglese

Libro In brossura

Codice Libristo: 07165112

Casa editrice Omniscriptum, febbraio 2018

Most real world problems require the simultaneous optimization of multiple, competing, criteria (or... Descrizione completa

Codice Libristo: 07165112

91 b

37.29 €

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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.