This paper presents stochastic convergence analysis of particle swarm optimization algorithm involving randomness and applying the results to the Analog RF Circuits to optimize the circuit parameters. In every iteration, each particle position is represented as vector and the standard particle swarm algorithm determined by positive real tipple {w,c 1 ,c 2 }. Comparisons for convergence are presented with respect to fixed tipple {w,c 1 ,c 2 } and random tipple {w,c 1 ,c 2 }. Various results show that the randomness in defining new position to particle leads to better convergence property. Also, exploration and exploitation trade off are discussed with examples. It is demonstrated that each particle undergoes both exploration and exploitation in convergence process; if the randomness in the particle generation is considered. The parameters considered for RF circuit are cutoff frequency, Phase Noise and Signal to Noise Ratio (SNR). Results are compared between both fixed values and random values of parameters in convergence analysis of PSO.

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Recently, extending the use of memristor technology from memory to computing has received a lot of attention. Memristor-based logic design is a new concept that aims to make computing systems more efficient. Several logic families have emerged, each with its own set of characteristics. In this paper, CMOS-based hybrid memristor-based combinational circuits are designed. Many computational devices require combinational circuits. All of the proposed designs were analysed for power, latency, and transistor count. Cadence Virtuoso is used for simulation of circuits. In this study, we used the VTEAM model to describe the simulated memristor because it is easy to understand and gives accurate results.

ABSTRACT:Travelling salesman problem (TSP) is a routing problem, which is a sub-problem of many application domains such as transportation, network communication. In this paper, we propose a novel methodology which solves the problem ACO algorithm has defined with well distribution strategy in which total search space area is partition into P numbers of hyper-cubic quadrants where P is the dimension of total search space is updating ant colony optimization heuristic parameter that improve the performance When solving TSP. From experimental Results, we can see that proposed algorithm is giving better showing than other standard algorithms.

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