Multidimensional 0–1 Knapsack using directed Bee Colony algorithm

In this paper, a Directed Bee Colony (DBC) optimization algorithm has been successfully applied for solving a Multidimensional 0-1 Knapsack Problem (MKP). Knapsack problem is a typically NP hard problem. We have proposed an algorithm which is an integration of the tournament selection of Genetic Algorithm (GA) and bee's decision-making process. This paper combines multi-agent environment and honey bee swarms' techniques to help Directed Bee Colony solve for Multidimensional 0-1 Knapsack Problem (MKPDBC). The hybridization makes MKPDBC to obtain a fast and thus produce a superior solution for MKP problems. The MKPDBC algorithm has been applied to the benchmark datasets. The proposed algorithm results have been compared with conventional strategies like GA, Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO). The outcomes of proposed algorithm demonstrate that the algorithm is more robust and accurate over the traditional methods.