A binary Particle Swarm Optimization for attacking knapsacks Cipher Algorithm

This paper presents a binary particle swarm optimization, PSO, for cryptanalysis of knapsack cipher algorithm so as to deduce the meaning of a ciphertext message (i.e. to determine a plaintext from the ciphertext). The implemented PSO has been characterized, in this paper, by a number of properties and operations that build up and evolve the swarmpsilas particles. These include particle representation, particle length, particle velocity, velocity and position updating, fitness evaluation, and new particle generation. The results of the PSO algorithm are compared with those of Grag et al. results that use genetic algorithm, GA, to discover the plaintext from the ciphertext. Experimental results show that binary particle swarm optimization algorithm is capable of finding correct results and, moreover, efficiently than GA.