Intelligent water drops algorithm A new optimization method for solving the multiple knapsack problem

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-...

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Vydáno v:International journal of intelligent computing and cybernetics Ročník 1; číslo 2; s. 193 - 212
Hlavní autor: Shah‐Hosseini, Hamed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bingley Emerald Group Publishing Limited 01.01.2008
Témata:
Algorithms
Capital budgeting
Computer programming
Convergence
Cybernetics
Genetic algorithms
Knapsack problem
Mathematical models
Optimization
Optimization techniques
Rivers
Studies
Traveling salesman problem
Velocity
Water
Water drops
ISSN:1756-378X, 1756-3798
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Abstract 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. [PUBLICATION ABSTRACT]
AbstractList 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.
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. [PUBLICATION ABSTRACT]
Author Shah‐Hosseini, Hamed
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Snippet 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...
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...
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SubjectTerms Algorithms
Capital budgeting
Computer programming
Convergence
Cybernetics
Genetic algorithms
Knapsack problem
Mathematical models
Optimization
Optimization techniques
Rivers
Studies
Traveling salesman problem
Velocity
Water
Water drops
Subtitle A new optimization method for solving the multiple knapsack problem
Title Intelligent water drops algorithm
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