scholarly journals An Optimal Design of CMOS Two Stage Comparator Circuit using Swarm Intelligence Technique

2018 ◽  
Vol 7 (3) ◽  
pp. 125
Author(s):  
Sasikumar Sasikumar ◽  
Muthaiah Muthaiah
Keyword(s):  
Swarm Intelligence ◽  
Optimization Technique ◽  
Harmony Search ◽  
Flower Pollination Algorithm ◽  
Two Stage ◽  
Mos Transistor ◽  
Flower Pollination ◽  
Input Control ◽  
Simulation Based ◽  
Intelligence Technique

A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works

scholarly journals An Optimal Design of CMOS Two Stage Comparator Circuit using Swarm Intelligence Technique

2018 ◽  
Vol 7 (3) ◽  
pp. 131
Author(s):  
Sasikumar Sasikumar ◽  
Muthaiah Muthaiah
Keyword(s):  
Swarm Intelligence ◽  
Optimization Technique ◽  
Harmony Search ◽  
Flower Pollination Algorithm ◽  
Two Stage ◽  
Mos Transistor ◽  
Flower Pollination ◽  
Input Control ◽  
Simulation Based ◽  
Intelligence Technique

A swarm intelligent based optimization technique named as Flower pollination algorithm (FPA) is applied for the design of the CMOS two stage comparator circuit. The basic idea of FPA mimics the flower pollination process of flowering plants. The input control parameters of FPA improve the exploration and exploitation capabilities of optimization problem. This paper presents the design of a CMOS two-stage comparator circuit using simulation based model called swarm intelligence technique. Simulation results shows that the proposed method is capable to determine the transistor sizes and bias current values of the CMOS comparator. The results obtained from the FPA improved the design performance of comparator in terms of power consumption, MOS transistor area and gain. To investigate the efficiency of proposed approach, comparisons have been carried out with differential evolution (DE) and harmony search (HS) algorithm based circuit design. The performances of FPA based comparator design are better than the previously reported works

Optimal Design of Fractional-Order Digital Differentiator Using Flower Pollination Algorithm

2018 ◽  
Vol 27 (08) ◽  
pp. 1850129 ◽  
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Fractional Order ◽  
Optimization Technique ◽  
Phase Response ◽  
Computational Time ◽  
Infinite Impulse Response ◽  
Flower Pollination Algorithm ◽  
Solution Quality ◽  
Digital Differentiator ◽  
Flower Pollination ◽  
Real Coded Genetic Algorithm

This paper presents an efficient approach to design wideband, accurate, stable, and minimum-phase fractional-order digital differentiators (FODDs) in terms of the infinite impulse response (IIR) filters using an evolutionary optimization technique called flower pollination algorithm (FPA). The efficiency comparisons of FPA with real-coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE)-based designs are conducted with respect to different magnitude and phase response error metrics, parametric and nonparametric statistical hypotheses tests, computational time, and fitness convergence. Exhaustive simulation results clearly demonstrate that FPA significantly outperforms RGA, PSO, and DE in attaining the best solution quality consistently. Extensive analysis is also conducted in order to determine the role of control parameters of FPA on the performance of the designed FODDs. The proposed FPA-based FODDs outperform all the designs published in the recent literature with respect to the magnitude responses and also achieve a competitive performance in terms of the phase response.

scholarly journals Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3548 ◽  
Author(s):  
Dalia Yousri ◽  
Thanikanti Sudhakar Babu ◽  
Dalia Allam ◽  
Vigna. K. Ramachandaramurthy ◽  
Eman Beshr ◽  
...  
Keyword(s):  
Dynamic Change ◽  
Optimization Technique ◽  
Maximum Power ◽  
Flower Pollination Algorithm ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Flower Pollination ◽  
Power Point

Solar Photovoltaic (PV) systems have become prominent and have attained the attention of energy engineers, governments and researchers. To achieve the maximum benefit from the PV system in spite of its nonlinear characteristic and environmental conditions, finding a robust maximum power point tracking method is essential. Over two decades, various researchers proposed numerous MPPT methods, but they failed to evaluate their methods on consistency, reliability, and robustness over several numbers of runs. Most of the researchers examined one configuration and they did not to consider the dynamic change in the irradiation conditions. Therefore, in this manuscript, the authors introduced a novel optimization technique Fractional chaotic Flower Pollination Algorithm (FC-FPA), by merging fractional chaos maps with flower pollination algorithm (FPA). The proposed technique, help FPA in extracting the Global Maximum Power Point (GMPP) under different partial shading patterns including with different PV array configurations. The proposed FC-FPA technique is tested and evaluated over 5 different patterns of partial shading conditions. The first three patterns are tested over 4S configuration made with Shell S36 PV module. The other two patterns are applied to the 4S2P configuration of Shell SM55 PV panels. The performance of the proposed variant is investigated by tracking the GMPP for abruptly changing shade pattern. Exclusive statistical analysis is performed over several numbers of runs. Comparison with perturb and observe MPPT technique is established. These results confirm that, the proposed method shows fast convergence, zero oscillation and rapid response for the dynamic change in irradiation with consistent behavior.

scholarly journals Comparison Investigation into Power System Optimization and Constraint-Based Generator Load Scheduling Using Metaheuristic Algorithms

2021 ◽  
Vol 19 (2) ◽  
pp. 200-208
Author(s):  
D. S. Naga Malleswara Rao ◽  
Dogga Raveendhra ◽  
Devineni Gireesh Kumar ◽  
Bharat Kumar Narukullapati ◽  
Davu Srinivasa Rao ◽  
...  
Keyword(s):  
Power System ◽  
Transmission Loss ◽  
Optimization Technique ◽  
System Optimization ◽  
Flower Pollination Algorithm ◽  
Fuel Cost ◽  
Power System Optimization ◽  
Flower Pollination ◽  
Single Function ◽  
Penalty Factor

In this paper, a novel flower pollination algorithm (FPA) is implemented to solve the problem of combined economic emission dispatch (CEED) in the power system. The FPA is a new metaheuristic optimization technique, which takes a biological approach to flower pollination. The FPA mimics the characteristics of flower pollination according to the survival of the fittest concept. CEED represents a combination of the emission and economic dispatch functions, formulated into a single function using the penalty factor. In this paper, the effect of valve point loading in the power system network is considered to obtain minimum fuel cost, minimum emissions, and optimum power generation. The performance of the proposed algorithm is evaluated using two test systems, namely 10 and 14 generating units by contemplating the valve point loading effect as well as transmission loss. The results of the 10 and 14 system units are compared with a learning-based optimization technique to demonstrate the effectiveness of the FPA. The findings reveal that the proposed FPA gives better performance than other algorithms with minimum fuel cost and emissions.

scholarly journals Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Weijia Cui ◽  
Yuzhu He
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Orthogonal Experiment ◽  
Optimization Technique ◽  
Iteration Process ◽  
Population Diversity ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Effect Mechanism ◽  
Orthogonal Learning

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

scholarly journals Flower Pollination Algorithm for Adaptive Beamforming of Phased Array Antennas

2017 ◽  
Vol 2 (2) ◽  
pp. 1-5
Author(s):  
Tarek Abdel Rahman Sallam ◽  
Adel Bedair Abdel-Rahman ◽  
Masoud Alghoniemy ◽  
Zen Kawasaki
Keyword(s):  
Phased Array ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Adaptive Beamforming ◽  
Array Antenna ◽  
Flower Pollination Algorithm ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Flower Pollination ◽  
Array Antennas

This paper introduces the flower pollination algorithm (FPA) as an optimization technique suitable for adaptive beamforming of phased array antennas. The FPA is a new nature-inspired evolutionary computation algorithm that is based on pollinating behaviour of flowering plants. Unlike the other nature-inspired algorithms, the FPA has fewer tuning parameters to fit into different optimization problems. The FPA is used to compute the complex beamforming weights of the phased array antenna. In order to exhibit the robustness of the new technique, the FPA has been applied to a uniform linear array antenna with different array sizes. The results reveal that the FPA leads to the optimum Wiener weights in each array size with less number of iterations compared with two other evolutionary optimization algorithms namely, particle swarm optimization and cuckoo search.

scholarly journals Design of Power System Stabilizer using Flower Pollination Algorithm

2018 ◽  
Vol 7 (4.10) ◽  
pp. 177 ◽  
Author(s):  
S. Venkateswarlu ◽  
Janaki M ◽  
Thirumalaivasan R
Keyword(s):  
Power System ◽  
Optimization Technique ◽  
Low Frequency ◽  
Oscillatory Instability ◽  
Power System Stabilizer ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Low Frequency Oscillations ◽  
System Stabilizer ◽  
Selection Of

The Power System Stabilizer (PSS) is a controller which is used to mitigate the instability of Low Frequency Oscillations (LFOs) in power systems. The condition of oscillatory instability can also cause the loss of generator synchronism. It is observed that the damping provided by PSS depends on the proper selection of its parameters. This paper presents the systematic method for the selection of PSS parameters using evolutionary nature inspired optimization technique called Flower Pollination Algorithm (FPA). FPA is employed for selecting the optimal parameters of PSS so as to mitigate the low frequency oscillations of generator rotor and thereby oscillatory instability. The system consists of Single Machine with PSS which is connected to Infinite Bus (SMIB) through a transmission line. The transient simulation validates the performance of the system with optimized PSS. The results show that PSS with FPA optimized parameters provides fast and stable response.  

scholarly journals GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wanjun Yang ◽  
Zengwu Sun
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Chaotic Map ◽  
Geographic Location ◽  
Urban Traffic ◽  
Flower Pollination Algorithm ◽  
Important Research ◽  
Chaotic Mapping ◽  
Flower Pollination ◽  
Swarm Intelligence Optimization

GPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA) is a new swarm intelligence optimization algorithm (SIOA) and easy to implement and has other characteristics; more and more scholars have continuously improved it and applied it to more fields. Aiming at the fact that FPA leads to the local optimal value in cross-pollination, the chaotic mapping strategy is proposed to optimize related issues that the population is not rich enough in the self-pollination process. The improved flower pollination algorithm has better advantages in testing function convergence and geographic location prediction effect.

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