ATTACKING CLASSICAL CRYPTOGRAPHY METHOD USING PSO BASED ON VARIABLE NEIGHBORHOOD SEARCH
Ahmed T. Sadiq, Amjed Abbas Ahmed, Al-Imam Al-Kadhum, Sura Mazin Ali · 2014
The Variable Neighborhood Search (VNS) algorithm is based on the variable neighborhood descent, which is a deterministic version of VNS. Particle Swarm Optimization (PSO) is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems and some NP-complete problems. This paper presents an improved PSO using the VNS. The benefit of VNS in PSO is use to find the local best particle, in addition it uses as momentum and diversity tool in the population. PSO based on VNS used to attack the two types of classical cryptography (substitution and transposition). Experimental results of the proposed PSO appear that the amount of recovered key of classical ciphers and fitness function values are best than with PSO, improved 2-opt PSO, MPSO and simulated annealing PSO.