Fusing Binary Particle Swarm Optimzation with Simulated Annealing for Knapsack Problems

The Knapsack Problems (KPs) is a well-known combinatorial optimization problem. It has a variety of practical applications. We propose the algorithm to solve both 0-1 Knapsack problem (KP) and Multidimensional Knapsack Problem (MKP) by fusing the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) with maximum profit objective. The main contribution is to develop a novel approach by hybridizing BPSO at the local optimum with the simulated annealing to help escape from the local optimum to reach the global optimum. The results indicate that the fusion approach outperforms individual implementation of both binary particle swarm optimization and simulated annealing.