Solving 0-1 Multi-Dimensional Knapsack Problem using a Discrete Binary Version of Grey Wolf Optimizer Algorithm

摘要

Grey Wolf Optimizer Algorithm (GWO) is a recently proposed meta-heuristic algorithm based on the social hierarchy and hunting behavior of grey wolves. The GWO algorithm is used to solve the continuous optimization problems. In this paper, a discrete binary version of the GWO algorithm called Discrete Binary Grey Wolf Optimizer (DBGWO) is proposed to solve the NP-Hard 0-1 Multi-Dimensional Knapsack Problem (MKP). The proposed DBGWO method is tested for a set of seven MKP benchmark problems and the results are compared with three recent methods available in the literature for solving the MKP namely Multi Hybrid Particle Swarm Optimization (MHPSO) algorithm, Egyptian Vulture Optimization (EVO) Algorithm and binary Artificial Fish Swarm Algorithm (b-AFSA). The experimental results show that the proposed DBGWO method outperforms the MHPSO, EVO and b-AFSA algorithms.