scholarly journals Diagnosis of Faulty Elements in Array Antenna using Nature Inspired Cuckoo Search Algorithm

2018 ◽  
Vol 8 (3) ◽  
pp. 1870 ◽  
Author(s):  
Shafqat Ullah Khan ◽  
M. K. A. Rahim ◽  
Murtala Aminu-Baba ◽  
Atif Ellahi Khan Khalil ◽  
Sardar Ali
Keyword(s):  
Mobile Communications ◽  
Linear Array ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Correction Method ◽  
Cuckoo Search Algorithm ◽  
Array Antenna ◽  
Single Element

Detection and correction of faulty elements in a linear array have great importance in radar, sonar, mobile communications and satellite. Due to single element failure, the whole radiation pattern damage in terms of side lobes level and nulls. Once we have detect the position of defective element, then correction method is applied to achieve the desired pattern. In this work, we introduce a nature inspired meta-heuristic cuckoo search algorithm to diagnose the position of defective elements in a linear array. The nature inspired cuckoo search algorithm is new to the optimization family and is used first time for fault detection in an array antenna. Cuckoo search algorithm is a global search optimization technique. The cost function is used as a fitness function which defines an error between the degraded far field power pattern and the estimated one. The proposed technique is used effectively for the diagnosis of complete, as well as, for partial faulty elements position. Different simulation results are evaluated for 40 elements Taylor pattern to validate and check the performance of the proposed technique.

scholarly journals FAILURE CORRECTION OF LINEAR ARRAY ANTENNA WITH MULTIPLE NULL PLACEMENT USING CUCKOO SEARCH ALGORITHM

2014 ◽  
Vol 05 (01) ◽  
pp. 877-881 ◽  
Author(s):  
Muralidaran R. ◽  
◽  
Vallavaraj A ◽  
Hemant Patidar ◽  
Mahanti G.K. ◽  
...  
Keyword(s):  
Linear Array ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Array Antenna ◽  
Null Placement

scholarly journals A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2675 ◽  
Author(s):  
Yang Zhang ◽  
Huihui Zhao ◽  
Yuming Cao ◽  
Qinhuo Liu ◽  
Zhanfeng Shen ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Elapsed Time ◽  
Route Design

The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

scholarly journals Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2953-2964
Keyword(s):  
Data Flow ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Automatic Generation ◽  
Cuckoo Search Algorithm ◽  
Test Case ◽  
Stochastic Algorithm ◽  
Data Flow Testing ◽  
Work Done

Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.

scholarly journals An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wentan Jiao ◽  
Wenqing Chen ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Threshold Function ◽  
Size Factor ◽  
Step Size ◽  
Multiple Thresholds

Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.

Non-convex Economic Load Dispatch using Cuckoo Search Algorithm

2017 ◽  
Vol 5 (1) ◽  
pp. 48 ◽  
Author(s):  
N. Karthik ◽  
A.K. Parvathy ◽  
R. Arul
Keyword(s):  
Fossil Fuel ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Economic Load Dispatch ◽  
Transmission Losses ◽  
Soft Computing Techniques ◽  
Valve Point Loading Effect ◽  
Optimisation Technique

<p>This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.</p>

Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm

2020 ◽  
pp. 107754632095138
Author(s):  
Rosmazi Rosli ◽  
Zamri Mohamed
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Magnetorheological Damper ◽  
Size Factor ◽  
Mean Square

This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. The newly proposed algorithm was tested using a set of standard benchmark functions with different searching space and global optima placement. An engineering optimization application was chosen to evaluate the performance of the algorithm in complex engineering applications. The optimization task involved hysteresis parameter identification of the root mean square error between the model and an actual magnetorheological damper. The magnetorheological damper response was chosen as the objective function. The final value of the fitness function and the iteration number it took to converge were used as the qualifying indicator to the proposed cuckoo search algorithm efficiency. A comparison was done against particle swarm optimization, genetic algorithm, and sine–cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.

Cuckoo Search Algorithm for Hydrothermal Scheduling Problem

2016 ◽  
pp. 367-400 ◽  
Author(s):  
Thang Trung Nguyen ◽  
Dieu Ngoc Vo
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Scheduling Problems ◽  
Short Term ◽  
Step Size ◽  
Hydrothermal Scheduling

This chapter proposes a Cuckoo Search Algorithm (CSA) and a Modified Cuckoo Search Algorithm (MCSA) for solving short-term hydrothermal scheduling (ST-HTS) problem. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. In the MCSA method, the eggs are first classified into two groups in which ones with low fitness function are put in top group whereas others with higher fitness function are put in abandoned group. In addition, an updated step size in the MCSA changes and tends to decrease as the iteration increases leading to near global optimal solution. The robustness and effectiveness of the CSA and MCSA are tested on several systems with different objective functions of thermal units. The results obtained by the CSA and MCSA are analyzed and compared have shown that the two methods are favorable for solving short-term hydrothermal scheduling problems.

scholarly journals Design and Application of Optimally-Tuned Variable Parameters PID Controller for Nonlinear Engineering Systems

2020 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Magdy A. S. Aboelela
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Electric Power System ◽  
Engineering Systems ◽  
Variable Parameters ◽  
Simulation Process ◽  
Simulation Results

The goal of this article is to investigate the implementation of the Cuckoo Search Algorithm (CSA) as an optimization technique to determine the parameters of variable parameters PID (VP-PID) controller. The VP-PID has three parameters that have to be optimally evaluated. A case of three physical imbedded nonlinearities in a single area electric power system has been selected to test the suitability of the proposed technique. The integral-square error (ISE) criterion has been considered as a part of the objective function together with the percentage overshoot and settling time. Matlab/Simulink software has been used in the simulation process. The simulation results show that the proposed VP-PID controller furnishes a better performance than the conventional PID controller.

scholarly journals Cuckoo Search Algorithm with Chaotic Maps

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Lijin Wang ◽  
Yiwen Zhong
Keyword(s):  
Random Walk ◽  
Chaotic Maps ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Scaling Factor ◽  
Convergence Speed ◽  
Solution Quality ◽  
Two Parameters

Cuckoo search algorithm is a novel nature-inspired optimization technique based on the obligate brood parasitic behavior of some cuckoo species. It iteratively employs Lévy flights random walk with a scaling factor and biased/selective random walk with a fraction probability. Unfortunately, these two parameters are used in constant value schema, resulting in a problem sensitive to solution quality and convergence speed. In this paper, we proposed a variable value schema cuckoo search algorithm with chaotic maps, called CCS. In CCS, chaotic maps are utilized to, respectively, define the scaling factor and the fraction probability to enhance the solution quality and convergence speed. Extensive experiments with different chaotic maps demonstrate the improvement in efficiency and effectiveness.

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