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This paper presents an investigation of mutual coupling effect among the array elements in a symmetric linear array antenna with the aim of reducing the side lobe level and the null control for the radiation pattern synthesis using BAT Algorithm. PSO and DE optimization techniques are also adopted for the sake of comparison and to prove the superiority of BAT algorithm based design. Reduced side lobe level and null control, with and without considering the mutual coupling effect in the cost function have been achieved by an optimum perturbation of the array elements' current excitation amplitude weights and the inter-element spacing among the array elements. The results are also compared with those of a uniform reference array having equal number of elements with λ 2 inter-element spacing. The approach proposed in this paper is a generic one and can be easily applied to any type of symmetrical linear arrays having any number of elements. Five different design examples are presented and their performances are studied to illustrate the capability of BAT algorithm based approach over those of PSO and DE. [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]

In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of system coefficients of the infinite impulse response (IIR) system identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), 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 FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters. [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]

Due to the manufacturing limitations, the task of optimal analog active filter design by hand is very difficult. Evolutionary computation may be a competent implement for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique used is particle swarm optimization (PSO) with Aging Leader and Challenger (ALC-PSO). ALC-PSO 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 ALC-PSO efficiently minimizes the total design error with respect to previously used optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper, Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach is hybridized with Wavelet Mutation (PSOCFIWA-WM) strategy for the optimal design of linear phase FIR filters. Real coded genetic algorithm (RGA), particle swarm optimization (PSO) and particle swarm optimization with constriction factor and inertia weight (PSOCFIWA) have also been adopted for the sake of comparison. PSOCFIWA-WM incorporates a new definition of swarm updating in PSOCFIWA with the help of wavelet based mutation. Wavelet mutation enhances the effectiveness of PSOCFIWA to explore the multidimensional solution space more effectively. In this design approach, filter length, pass band and stop band edge frequencies, feasible pass band and stop band ripple sizes are specified. A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filter designs. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an optimal thinning of a large multiple concentric circular ring arrays of uniformly excited isotropic antennas based on Particle Swarm Optimization method. Circular Antenna Array (CAA) has gained immense popularity in the field of communications nowadays. It has proved to be a better alternative over other types of antenna array configuration due to its all-azimuth scan capability, and the beam pattern which can be kept invariant. In this paper, a 9 ringed Concentric Circular Antenna Array (CCAA) with central element feeding is considered. Extensive simulation results justify the optimization efficacy of the proposed approach for antenna array synthesis. The simulation results show that the number of effective antenna elements can be brought down from 279 to 139 with simultaneous reduction in Side Lobe Level by 20.37dB relative to the main beam with a fixed half power beamwidth using PSO. Real coded Genetic Algorithm (RGA) as well is also adopted to compare the results of PSO algorithm. [Copyright &y& Elsevier]

Cardiovascular or electrocardiogram signals (ECG) are contaminated by various artefacts while recording which in turn degrades the quality of the vital information present in the signal. Hence, noise removal from ECG signal is an important matter of concern. In this paper, swarm intelligence techniques for the optimization purpose of adaptive filters/noise canceller (ANC) are utilized in the biomedical signal processing field. The analysis of results for de-noising ECG signals through adaptive filtration is presented using the symbiotic organisms search (SOS), particle swarm optimization (PSO) and harmony search (HS) algorithms which are applied to estimate and adjust the parameters of the adaptive filter in such a way to keep a close track of spontaneous variations of non–stationary signals in a faithful manner. From the simulation results, it has been observed that the ANC filter designed with the SOS technique achieves significant improvement in fidelity parameters, such as signal to noise ratio (SNR), mean square error (MSE), maximum error (ME), mean difference (MD), peak reconstruction error (PRE), normalized root mean squared error (NRMSE) and normalized root mean error (NRME) when compared with other reported techniques in the literature as well as the benchmark algorithms, namely PSO and HS. The SOS based ANC technique results in nearly 7 dB improvement in output SNR as compared to the recently reported ANC filter based on bacteria foraging optimization (BFO) algorithm and also provides a fair reduction of more than 90% in NRMSE and NRME when compared to other state-of-art denoising techniques. Furthermore, the ANC filter using the SOS technique enhances the correlation between the pure and reconstructed ECG signals. [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]