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.

A Heuristic Approach to Design Discrete Fractional Order Integrators without Using s-to-z Transform

2017 ◽  
Vol 261 ◽  
pp. 386-393
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal ◽  
Nilotpal Banerjee
Keyword(s):  
Fractional Order ◽  
Search Algorithm ◽  
Statistical Tests ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Infinite Impulse Response ◽  
Error Metrics ◽  
Real Coded Genetic Algorithm

In this paper, a heuristic optimization technique called Harmony Search Algorithm (HSA) is efficiently employed to design Infinite Impulse Response (IIR) Discrete Fractional Order Integrators (DFOIs). Unlike the methods reported in the literature, no discretization (s-to-z transform) operator is necessary to obtain the DFOIs by using the proposed approach. To investigate the design efficiency, the HSA-based DFOIs have been evaluated against the designs based on Real coded Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) using different frequency response error metrics. The reliability in the performance of the proposed DFOIs are extensively investigated by conducting various statistical tests. Comparison of fitness convergence demonstrates that HSA achieves the near global optimal solution in the least number of iterations. Thus, HSA exhibits superior computational efficiency in solving this multimodal optimization problem. The proposed DFOIs also outperform the reported designs.

scholarly journals Synthesis of Circular Array Antenna for Sidelobe Level and Aperture Size Control Using Flower Pollination Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
V. S. S. S. Chakravarthy Vedula ◽  
S. R. Chowdary Paladuga ◽  
M. Rao Prithvi
Keyword(s):  
Array Antenna ◽  
Computational Time ◽  
Flower Pollination Algorithm ◽  
Side Lobe Level ◽  
Aperture Size ◽  
Circular Array ◽  
Sidelobe Level ◽  
Beam Scanning ◽  
Flower Pollination ◽  
Circular Array Antenna

Sidelobe level suppression is a major problem in circular array antenna (CAA) synthesis. Many conventional numerical techniques are proposed to achieve this which are time consuming and often fail to handle multimodal problems. In this paper, a method of circular array synthesis using nature inspired flower pollination algorithm (FPA) is proposed. The synthesis technique considered here adapts one and two degrees of freedom, namely, amplitude only and amplitude spacing. The effectiveness of the FPA is studied by comparing the results with genetic algorithm (GA) and uniform circular array antenna (UCAA) with uniform spacing. Also the effect of additional degree of freedom on the aperture size and the computational time is analyzed. A relative side lobe level (SLL) of −25 dB is achieved using the algorithm under both no beam scanning (0°) and beam scanning (15°) conditions for 20 and 40 elements of CAA.

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

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.

An improved hybrid flower pollination algorithm for assembly sequence optimization

2019 ◽  
Vol 39 (1) ◽  
pp. 165-185 ◽  
Author(s):  
Atul Mishra ◽  
Sankha Deb
Keyword(s):  
Global Optimum ◽  
Convergence Speed ◽  
Assembly Sequence Planning ◽  
Computational Time ◽  
Flower Pollination Algorithm ◽  
Initial Population ◽  
Assembly Sequence ◽  
Sequence Optimization ◽  
Content Type ◽  
Flower Pollination

PurposeAssembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.Design/methodology/approachIn view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.FindingsThe results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.Practical implicationsIt is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.Originality/valueDifferent representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

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.

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