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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(2): 130-133 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.2.586

Stochastic Diffusion Binary Differential Evolution to Solve Multidimensional Knapsack Problem

Ayed A. Salman, Imtiaz Ahmad, and Mahmad G. H. Omran
Abstract—Multi Knapsack Problem (MKP) is NP-hard combinational optimization problem, also known as the multi-constraint knapsack problem. MKP is one of the most studied problems in combinatorial optimization, with variety of real-life applications. In this paper a Stochastic Diffusion Binary differential evolution (SD-BDE) algorithm is applied for optimizing the Multidimensional Knapsack Problem (MKP). SD-BDE, is a Binary version of Differential Evolution hybridized with ideas extracted from Stochastic Diffusion search. SD-BDE algorithm, in this paper, is compared against state-of-the-art existing algorithms in solving MKP. Experimental results show that the SD-BDE algorithm outperformed the existing algorithms by finding either better or at least similar solutions for all tested benchmarks.

Index Terms—Differential evolution, stochastic diffusion search, np-complete problem, multidimensional knapsack problem.

Ayed A. Salman and Imtiaz Ahmad are with the Computer Engineering Department, Kuwait University, Kuwait (e-mail: ayed.salman@ku.edu.kw, imtiaz.ahmad@ku.edu.kw).
Mahmad G. H. Omran is with the Department of Computer Science, Gulf University for Science and Technology, Kuwait (e-mail: omran.m@gust.edu.kw).

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Cite: Ayed A. Salman, Imtiaz Ahmad, and Mahmad G. H. Omran, "Stochastic Diffusion Binary Differential Evolution to Solve Multidimensional Knapsack Problem," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 130-133, 2016.

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