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Articles by Ramin Ayanzadeh
Total Records ( 5 ) for Ramin Ayanzadeh
  Alireza Jaberi , Ramin Ayanzadeh , Elaheh Raisi and Aidin Sadighi
  Taking into consideration the significance of normal distribution in statistical applications, it is fundamental to have an efficient and reliable method to generate Gaussian random numbers. This study proposes an innovative method to generate normal random numbers. The main novelties of this algorithm lie in the following: using multiple layers of cellular automata in which central limit theorem is applied to generate normal random numbers; and employing binary cellular automata motivated by Pseudo-Neumann neighborhood structure. To evaluate the functionality of proposed approach, extensive experiments have been carried out in terms of normality of generated random numbers. Simulation results show that multi-layer cellular automata produce better normal random numbers than MATLAB’s random number generator which justifies its efficiency and good performance in statistical tests.
  Ramin Ayanzadeh , Azam S. Zavar Mousavi and Hamidreza Navidi
  Concept of solving games is to find a set of equilibrant strategies in which, players have no interest to refuse it. In static games, strategies intended by players are unspecified and a player’s pay-off is affected by other players’ strategies. Thus, analyzing opponent’s capabilities during decision making process is critical. It's clear that if the number of players or the cardinality of strategy sets increases, solving a game based on traditional approaches are impossible most of the times. Therefore, in this study a novel approach based on honey bees foraging optimization algorithms is proposed to solve static games with complete information to estimate pure and mixed Nash equilibrium. In proposed approach, equilibrium points of game are represented by food sources to be probed by honey bees in optimization process. To verify and validate method, several simulations are performed on some study cases. Simulation results prove that suggested approach generates more desirable solutions in precision and stability than other metaheuristics.
  Ramin Ayanzadeh , Azam S. Zavar Mousavi and Ehsan Shahamatnia
  Due to the steady increasing trend toward computer simulations, generating random numbers has attracted many researches and several techniques have been introduced in recent years. In this study by employing fuzzy operators on the structure of cellular automata update rules a new approach has been proposed for uniformly distributed random number generation. The simulation results show that the uniformity of the proposed method is very promising. The nature of this approach also makes it very suitable for hardware implementation.
  Alireza Jaberi , Ramin Ayanzadeh and Azam S. Zavar Mousavi
  Cryptography is knowledge of manipulating data to conceal secure information. It serves an essential functionality in wide variety of applications. So, several encoding and decoding methods have been proposed to enhance cryptography techniques. In this study a novel approach based on multi layer cellular automata is proposed to be used in cryptography applications. Proposed multi layers cellular automata employs interaction between two heterogeneous cellular automata to imitate Pseudo-Neumann neighborhood structure and generate trackable random integers. These random numbers are assumed as time variant keys for encoding and decoding purposes. To verify and validate performance of proposed architecture, several simulations are performed. Simulation results prove that two-layer cellular automata generate more uniform random numbers in comparison with MATLAB. Consequently, proposed architecture demonstrated desirable behavior and has less risk. Furthermore, the architecture is suitable for hardware implementations.
  Amir Khosravani-Rad , Ramin Ayanzadeh and Elaheh Raisi
  In this study, a new method for optimal control of parameters in particle swarm optimization based on fuzzy rules, is presented. In proposed method, to prevent premature convergence, social and personal learning coefficients are updated according to the convergence rate of the algorithm. In other words, fuzzy linguistic variables and membership functions are employed to conduct the swarm toward global optimum point. Several computational simulations are carried out to demonstrate high performance and stability of this method. Simulation results reveal superior optimality and stability and lower computational cost of the new algorithm compared to the traditional metaheuristics such as standard particle swarm optimization, genetic algorithms and standard particle swarm optimization which justifies its advantages for particle swarm optimization algorithms.
 
 
 
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