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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Beedea's performance on knapsack problem

Beedea's performance on knapsack problem

Zardi-H

Paperback

Series: Omn.Univ.Europ.

Literary CriticismGeneral Computers

ISBN10: 6131576165
ISBN13: 9786131576164
Publisher: Ed Universitaires Europeennes
Published: Feb 28 2018
Pages: 76
Weight: 0.27
Height: 0.18 Width: 6.00 Depth: 9.00
Language: French
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.

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