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A modified Intelligent Water Drops algorithm and its application to optimization problems
journal contribution
posted on 2014-11-01, 00:00 authored by B O Alijla, L-P Wong, Chee Peng LimChee Peng Lim, A T Khader, M A Al-BetarThe Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. © 2014 Elsevier Ltd. All rights reserved.
History
Journal
Expert Systems with ApplicationsVolume
41Issue
15Pagination
6555 - 6569Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2014, ElsevierUsage metrics
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No categories selectedKeywords
Feature selection (FS)Intelligent Water Drops (IWD)Multiple knapsack problem (MKP)Ranking-based selection methodsRough set (RS)Swarm-based optimizationTravelling salesman problem (TSP)Science & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringFEATURE-SELECTIONSEARCH
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