Paper Info
Title
An ensemble of intelligent water drop algorithms and its application to optimization problems
Abstract
The 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 propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.
Year
DOI
Venue
2015
10.1016/j.ins.2015.07.023
Information Sciences
Keywords
Field
DocType
Intelligent water drops,Optimization,Swarm intelligence,Exploitation,Exploration
Convergence (routing),Swarm behaviour,Swarm intelligence,Absolute deviation,Travelling salesman problem,Artificial intelligence,Optimization problem,Mathematical optimization,Algorithm,Rough set,Local search (optimization),Machine learning,Mathematics
Journal
Volume
Issue
ISSN
325
C
0020-0255
Citations 
PageRank 
References 
4
0.40
41
Authors
5
Name
Order
Citations
PageRank
Basem O. Alijla1212.43
Li-Pei Wong21098.32
Chee Peng Lim31459122.04
Ahamad Tajudin Khader468340.71
Mohammed Azmi Al-Betar562043.69