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BEEDEA's Performance on Knapsack problem


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  • Product Description

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.

Product Specifications
SKU :COC72059
AuthorHédia Zardi
Number of Pages76
Publishing Year2011-05-29T00:00:00.000
Edition1 st
Book TypeComputing & information technology
Country of ManufactureIndia
Product BrandNot defined
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-10-08 00:00:00