A New Method Based on Particle Swarm Optimization Algorithm to Improve Statistical Randomness Properties

The aim of optimization algorithms is to find the best options demanded under the specified conditions. Having a quality source of entropy is an important requirement, as the need for randomness also plays a critical role in the foundation of many applications. The main obejective of this study is to address this need with an innovative approach. Comprehensive analysis tools have been developed in the literature to measure the quality of randomness source. One of the most widely known of these analysis tools is the NIST testing tool, which includes fifteen different hypothesis testing. The generated entropy source provides ideal statistical properties because NIST tests are used as objective function in the optimization algorithm. The expected output from the proposed new approach is to decide what configurations of the LFSR generator will produce ideal randomness. The successful results obtained using the proposed approach indicate that the outputs can be used as a basic building block in the future, especially in cryptology studies.