A discrete binary version of bat algorithm for multidimensional knapsack problem

The nature has become a main inspiration source of scientists for developing new intelligent systems and techniques. Nature-inspired meta-heuristics is a kind of algorithms that imitate the social behaviour of some biological species. The bat algorithm (BA) is a new bio-inspired algorithm recently introduced by Yang (2010a). It is an optimisation method that is based on the echolocation behaviour of microbats. Firstly, the BA has been proposed for continuous problems. In this paper, we propose a discrete binary bat algorithm (BinBA) for solving the optimisation problems in binary space. The proposed algorithm is based on the sigmoid function used by Kennedy and Eberhart in 1997 for their binary particle swarm optimisation algorithm. The BinBA was tested on hard instances of the multidimensional knapsack problem. The obtained results are very promising compared to other bio-inspired algorithms.