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-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.