Search

This paper presents an efficient approach for the optimal designs of two analog circuits, namely complementary metal oxide semiconductor) two-stage comparator with p-channel metal oxide semiconductor input driver and n-channel input and folded-cascode operational amplifier using a recently proposed meta-heuristic-based optimization algorithm named as colliding bodies optimization (CBO). It is a multi-agent algorithm that does not depend upon any internal control parameter, making the algorithm extremely simple. The main objective of this paper is to optimize the metal oxide semiconductor (MOS) transistors' sizes using CBO in order to reduce the areas occupied by the circuits and to get better performance parameters of the circuits. Simulation Program with Integrated Circuit Emphasis simulation has been carried out by using the optimal values of MOS transistors' sizes and other design parameters to validate that CBO-based design is satisfying the desired specifications. Simulation results demonstrate that the design specifications are closely met and the required functionalities are achieved. The simulation results also confirm that the CBO-based approach is superior to the other algorithms in terms of MOS area and performance parameters like gain, power dissipation, etc., for the examples considered. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents the optimal designs of two analogue complementary metal-oxide-semiconductor (CMOS) amplifier circuits, namely differential amplifier with current mirror load and two-stage operational amplifier. A modified Particle Swarm Optimization (PSO), called Craziness-based Particle Swarm Optimization (CRPSO) technique is applied to minimize the total MOS area of the designed circuits. CRPSO is a highly modified version of conventional PSO, which adopts a number of random variables and has a better and faster exploration and exploitation capability in the multidimensional search space. Integration of craziness factor in the fundamental velocity term of PSO not only brings diversity in particles but also pledges convergence close to global best solution. The proposed CRPSO-based circuit optimization technique is reassured to be free from the intrinsic disadvantages of premature convergence and stagnation, unlike Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The simulation results achieved for the two analogue CMOS amplifier circuits establish the efficacy of the proposed CRPSO-based approach over those of DE, HS, ABC and PSO in terms of convergence haste, design conditions and design goals. The optimally designed analogue CMOS amplifier circuits occupy the least MOS area and show the best performance parameters like gain and power dissipation, in compared with the other reported literature. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Because of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness-based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth-order Butterworth low-pass analog active filter and second-order state variable low-pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low-pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents an accurate approach to design a symmetrical linear antenna array (LAA) and a non‐uniformly excited single ring circular antenna array (CAA) to improve the far‐field radiation characteristics. The far‐field radiation pattern is improved with lower side lobe level (SLL) which is essential for the reduction of interference in the entire side lobe regions. A recently proposed meta‐heuristic based optimization algorithm called moth flame optimization (MFO) is applied to determine the optimal sets of current excitation weights and the inter‐element spacing among the array elements to reduce the SLL. In this context, 10‐element and 16‐element LAA and 8‐element, 10‐element, and 12‐element CAA design examples are presented by optimizing the array parameters. The simulation results obtained by using MFO algorithm based approach yield a considerable improvement in SLL and some marked improvement of half power beam width with respect to the uniform array pattern and those of the recently reported literature. [ABSTRACT FROM AUTHOR]