A binary cuckoo search algorithm for knapsack problems

Knapsack problems are one of the classical NP-hard problems and it offers many practical applications in vast field of different areas. Several traditional as well as population based metaheuristic algorithms are applied to solve this problem. In this paper we introduce the binary version of cuckoo search algorithm (CSA) for solving knapsack problems, specially 01 knapsack problem. The proposed algorithm utilizes the balanced combination of local random walk and global explorative random walk. So far CSA is generally applied to continuous optimization problems. In order to investigate the performance of CSA on combinatorial optimization problem, an attempt is made in this paper. To demonstrate the efficiency of the proposed algorithm an extensive computational study is provided with standard benchmark problem instances and comparison with particle swarm optimization is also carried out.