scholarly journals The use of Metaheuristic Algorithms in Early Prediction and Forecasting of Flood – A Use of Cuckoo Search Algorithm based Optimization for Flood Controlling

2020 ◽  
Vol 9 (2) ◽  
pp. 139-143
Keyword(s):  
Flood Control ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Early Prediction ◽  
Neural Network Models

Flood is one of the disasters which have multiple impacts on the society and industry. It has severe impacts on the urban economy and has forced the scholars to develop resiliency plans. Various types of flood forecasting techniques developed by the scholars and have certain limitations. There are various types of multiple modeling techniques which are being used for flood controlling and each has certain limitations. The optimization techniques along with the artificial intelligence algorithms can be helpful for monitoring and early prediction of flood. The neural network models promises better accuracy compared to convention models for prediction, but they face great difficulties in selection of appropriate model parameters. In the said context, here an effort has been made to explore the importance of Cuckoo theorem in flood management. The cuckoo search algorithm can be used for parameter tuning. The hybrid approach of using cuckoo search algorithm with neural networks has given far better accuracy compared to standalone algorithms. The use of such Cuckoo Search Metaheuristic algorithm will help us to predict early warning system than any other method and helps us to align the flood controlling activities. The paper presents the used of variants of cuckoo search algorithm for early flood prediction. The paper unfolds major insights of flood scenarios along with the significance of flood control and monitoring.

scholarly journals Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

2017 ◽  
Vol 7 (5) ◽  
pp. 2349 ◽  
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Scheduling Problem ◽  
Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

scholarly journals Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 4272-4279
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Similarity Index ◽  
Structural Similarity ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Entropy Maximization ◽  
Image Thresholding ◽  
Segmentation Threshold

Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS. An entropy maximization of hGSA-PS dependent multilevel image thresholds is developed, where the results are best demonstrated in PSNR, misclassification, structural similarity index and segmented image quality compared to the Firefly algorithm, adaptive cuckoo search algorithm and the search algorithm gravitational.

scholarly journals Cuckoo search algorithm to solve the problem of economic emission dispatch with the incorporation of facts devices under the valve-point loading effect

2021 ◽  
Vol 34 (4) ◽  
pp. 569-588
Author(s):  
Larouci Benyekhlef ◽  
Sitayeb Abdelkader ◽  
Boudjella Houari ◽  
Ayad Ahmed Nour El Islam
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Fuel Cost ◽  
Loading Effect ◽  
Facts Devices ◽  
Emission Dispatch ◽  
Valve Point Loading Effect ◽  
Economic Emission Dispatch

The essential objective of optimal power flow is to find a stable operating point which minimizes the cost of the production generators and its losses, and keeps the power system acceptable in terms of limits on the active and reactive powers of the generators. In this paper, we propose the nature-inspired Cuckoo search algorithm (CSA) to solve economic/emission dispatch problems with the incorporation of FACTS devices under the valve-point loading effect (VPE). The proposed method is applied on different test systems cases to minimize the fuel cost and total emissions and to see the influence of the integration of FACTS devices. The obtained results confirm the efficiency and the robustness of the Cuckoo search algorithm compared to other optimization techniques published recently in the literature. In addition, the simulation results show the advantages of the proposed algorithm for optimizing the production fuel cost, total emissions and total losses in all transmission lines.

scholarly journals An Optimized Model for Energy Efficiency on Cloud System using PSO & CUCKOO Search Algorithm

2019 ◽  
Vol 8 (9S) ◽  
pp. 138-144
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Programming Languages ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Google Docs ◽  
Platform As A Service ◽  
Multiple Resources

Cloud computing is one of the growing technologies, these days. Cloud computing is a paradigm that is surrounded by multiple resources, which helps in resource utilization. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (Saas) are named as services of cloud computing. In the IaaS models, users can rent infrastructure of the data center as a service. Some of the examples of IAAS are Google Compute Engine (GCE) and Amazon Web Service (AWS). In the PaaS models, users can take services like operating system and database. Some of the examples of PAAS are Microsoft Azure and Google App Engine. In the SaaS models, users can access and install application software and databases via Internet. Examples of SAAS are Citrix GoToMeeting and Google Docs. In this paper algorithms named as PSO and CSA are discussed The objective of optimization for energy consumption on cloud has also been discussed in the paper. Along with the optimization techniques, the detailed literature reviews have been presented. To achieve the proposed work, CloudSim simulators and standard programming languages have been used. The performance of the proposed work will be analyzed by using the various performance parameters such as response time, energy efficiency and execution time.

scholarly journals Remote Sensing Imagery Segmentation: A Hybrid Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 4604
Author(s):  
Shreya Pare ◽  
Himanshu Mittal ◽  
Mohammad Sajid ◽  
Jagdish Chand Bansal ◽  
Amit Saxena ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parameter Analysis ◽  
Energy Curve ◽  
Remote Sensing Imagery ◽  
Renyi’S Entropy

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

scholarly journals Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind Farm

2018 ◽  
Vol 8 (6) ◽  
pp. 4871
Author(s):  
Kaushik Paul ◽  
Niranjan Kumar
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Search Algorithm ◽  
Past Research ◽  
Cuckoo Search ◽  
Congestion Management ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Management Approach ◽  
Sensitivity Factor

The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm.The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.

scholarly journals A Hybrid Approach for Test Case Prioritization using PSO Based on Software Quality Metrics

2018 ◽  
Vol 7 (3.12) ◽  
pp. 300
Author(s):  
K Senthil Kumar ◽  
A Muthukumaravel
Keyword(s):  
Software Quality ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Software Application ◽  
Multiple Test ◽  
Software Quality Metrics

Effective functionality checking of any software application is the crucial event that determines the quality of outcome obtained.  Generally, checking scenarios that involves multiple test cases in mixture with multiple components is time consuming and also increases the quality assurance cost. Selection of suitable method/approach for optimization and prioritization of test cases as well as appropriate evaluation of the application would result in reduction of fault detection effort without appreciable information loss and further would also significantly decrease the clearing up cost. In the proposed method, test cases are optimized and then prioritized by Particle Swarm Optimization algorithm (PSO) and Improved Cuckoo Search algorithm (ICSA), respectively. Finally, the result will be evaluated for software quality measures. 

scholarly journals Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 54 ◽  
Author(s):  
Juan Templos-Santos ◽  
Omar Aguilar-Mejia ◽  
Edgar Peralta-Sanchez ◽  
Raul Sosa-Cortez
Keyword(s):  
Search Algorithm ◽  
Permanent Magnet Synchronous Motor ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Cuckoo Search Algorithm ◽  
Flower Pollination Algorithm ◽  
Speed Regulation ◽  
Pi Controllers ◽  
Nature Inspired Algorithms

This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

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