Modified And Ensemble Intelligent Water Drop Algorithms And Their Applications
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Date
2015-10
Authors
F. ALIJLA, BASEM O.
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Abstract
The Intelligent Water Drop (IWD) algorithm is a swarm-based model that is useful for
undertaking optimization problems. The main aim of this research is to enhance the
IWD algorithm and overcome its limitations pertaining to population diversity, as well
as balanced exploration and exploitation in handling optimization problems. Firstly,
a modified IWD algorithm is introduced. Two ranking-based selection methods, i.e.
linear ranking and exponential ranking, are proposed to replace the fitness proportionate
selection method. Secondly, the Master River Multiple Creeks Intelligent Water
Drops (MRMC-IWD) algorithm is proposed in an attempt to exploit the exploration
capability of the modified IWD algorithm. In addition, the hybrid MRMC-IWD model
is proposed. It combines MRMC-IWD with the iterated improvement local search
method, to empower MRMC-IWD with the exploitation capability. The usefulness
of the proposed models is evaluated systematically and comprehensively using three
combinatorial optimization problems, i.e., rough set feature subset selection, multiple
knapsack problem, and travelling salesman problem. The applicability of the hybrid
MRMC-IWD model is investigated to solving real-world optimization problems
related to feature selection and classification tasks. A number of publicly available
benchmark data sets and two real-world problems, namely human motion detection
and motor fault detection, are studied. The results ascertain the effectiveness of the
proposed models in improving the performance of the original IWD algorithm as well
as undertaking real-world optimization problems.
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Keywords
Modified And Ensemble Intelligent Water Drop Algorithms , And Their Applications