A modified Intelligent Water Drops algorithm and its application to optimization problems

作者：Alijla Basem O^*; Wong Li Pei; Lim Chee Peng; Khader Ahmed Tajudin; Al Betar Mohammed Azmi

来源：Expert Systems with Applications, 2014, 41(15): 6555-6569.

DOI：10.1016/j.eswa.2014.05.010

• Intelligent Water Drops (IWD)
• Swarm-based optimization
• Ranking-based selection methods
• Feature selection (FS)
• Rough set (RS)
• Multiple knapsack problem (MKP)
• Travelling salesman problem (TSP)

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 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.

• 出版日期2014-11-1
• 单位迪肯大学