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This paper presents an accurate approach for Concentric Circular Antenna Array (CCAA) synthesis to improve the far-field radiation characteristics of the antenna using a meta-heuristic optimization technique called Moth Flame Optimization (MFO). The far-field radiation pattern improves with lower Side Lobe Level (SLL) which is essential for the reduction of interference in the entire side lobe regions. MFO algorithm is a recently proposed evolutionary algorithm which is applied to determine the optimum sets of current excitation weights and to find the optimal inter-element spacing between the array elements in the 3-rings structure of CCAA design. In this context, the design examples of two 3-ring CCAAs, one having the set of 4, 6, 8 elements and the other having the set of 8, 10, 12 elements, with and without centre element, are presented by optimizing the array parameters. The results obtained by using MFO algorithm based approach show a considerable improvement of SLL with respect to that of the uniform array pattern and those of the approaches reported in the recent literature. [ABSTRACT FROM AUTHOR]

A shaped beam synthesis from a concentric ring array has been presented. Two different cases are considered. In the first case, a flat-top beam with desired specification has been generated and in the second case, a cosec 2 beam pattern from the same array configuration is generated. The desired beam pattern in each of the individual case is obtained by finding optimum excitations of the array elements using Differential Evolution (DE) algorithm. Dynamic range ratio (DRR) of the excitation amplitudes are improved by eliminating the weakly excited array elements from the optimized array without distorting the obtained pattern. To illustrate the effectiveness of DE, the two beam-patterns are computed from the same array using Genetic Algorithm (GA) by finding out optimum excitations among the elements. Results clearly show the superiority of DE over GA to handle the proposed problem. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary approach to design CMOS Two-Stage Comparator (TSC) and CMOS Folded Cascode Operational Trans-conductance Amplifier (FCOTA) using simplex particle swarm optimization (Simplex-PSO) method. The simplex particle swarm optimization (Simplex-PSO) is a swarm intelligent based evolutionary computation method. Simplex-PSO is the hybridization of Nelder–Mead Simplex method (NMSM) and Particle Swarm Optimization (PSO) without the velocity term. The Simplex-PSO has fast optimizing capability and high computational precision for high-dimensionality functions. This work has focused on the optimization of the area, power and has improved all other performance parameters of the CMOS TSC and CMOS FCOTA with minimum computational time. The proposed Simplex-PSO based circuit optimization technique is relieved from the inherent drawbacks of premature convergence and stagnation, unlike Differential Evolution (DE), Harmony Search (HS). The simulation results prove that Simplex-PSO yields the optimized result with improved functionality. Simulation results obtained for CMOS TSC and CMOS FCOTA prove the effectiveness of the proposed Simplex-PSO method based approach over the reported DE, HS, and PSO in terms of convergence speed, design specifications and design objectives. The optimally designed TSC and FCOTA circuits, individually, occupy the least MOS areas and dissipate the least powers. The simulation plots and results are shown to have the improved performance parameters compared with those of other reported literature. Validation of the design is carried out by Cadence version 5 (IC 5.10.41) of TSMC 0.35 μm technology. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) system identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based system identification approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters. [Copyright &y& Elsevier]