Intelligent water drops algorithm

Purpose - The purpose of this paper is to test the capability of a new population-based optimization algorithm for solving an NP-hard problem, called "Multiple Knapsack Problem", or MKP.Design methodology approach - Here, the intelligent water drops (IWD) algorithm, which is a population-b...

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Bibliographic Details
Published in:International journal of intelligent computing and cybernetics 2008-06, Vol.1 (2), p.193-212
Main Author: Shah-Hosseini, Hamed
Format: Article
Language:English
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Summary:Purpose - The purpose of this paper is to test the capability of a new population-based optimization algorithm for solving an NP-hard problem, called "Multiple Knapsack Problem", or MKP.Design methodology approach - Here, the intelligent water drops (IWD) algorithm, which is a population-based optimization algorithm, is modified to include a suitable local heuristic for the MKP. Then, the proposed algorithm is used to solve the MKP.Findings - The proposed IWD algorithm for the MKP is tested by standard problems and the results demonstrate that the proposed IWD-MKP algorithm is trustable and promising in finding the optimal or near-optimal solutions. It is proved that the IWD algorithm has the property of the convergence in value.Originality value - This paper introduces the new optimization algorithm, IWD, to be used for the first time for the MKP and shows that the IWD is applicable for this NP-hard problem. This research paves the way to modify the IWD for other optimization problems. Moreover, it opens the way to get possibly better results by modifying the proposed IWD-MKP algorithm.
ISSN:1756-378X
DOI:10.1108/17563780810874717