Public Key Cryptography Using Particle Swarm Optimization and Genetic Algorithms
نویسندگان
چکیده
This paper proposes an algorithm for Public Key Cryptography (PKC) using the hybrid concept of two evolutionary algorithms, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) respectively. PSO alone are fast and easy to implement, they follow the procedures of common evolutionary algorithm and posses memory feature which is absent in GA making it more valuable. In GA whole population or set of individual chromosome work together sharing information to reach an optimal solution whereas PSO focuses on only the best possible solution. Particles in PSO converges in small optimal area quickly. The random variables needed for PSO initialization are provided by GA ensuring that every time algorithm runs a new unique random value is initialized which cannot be guessed. PSO uses a set of fine fit initial keys as input from key domain generated by GA and outputs the position of key having the highest fitness among the keys. Thus, PSO-GA algorithm aims here for generating the fittest among the fine fit keys in key domain containing best keys of highest possible strength. The results produced by this hybrid algorithm to be tested for frequency test, gap test, auto-correlation test, binary derivative test, change point test, serial test, run test and also to check for the linear complexity of key proving its validity and practical use of the proposed work in PKC. Keywords— Public-Key Cryptography (PKC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fitness Function, Key Space, Genetic Crossover, Genetic Mutations, Evolutionary Algorithm, Artificial Intelligence, Optimisation, Swarm Intelligence, Key Generation
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