scholarly journals Mismatched Filter Design using Improved Cuckoo Search Algorithm for Optimum Detection

2019 ◽  
Vol 9 (2S3) ◽  
pp. 93-97
Keyword(s):  
Pulse Compression ◽  
Filter Design ◽  
Matched Filter ◽  
Search Algorithm ◽  
Short Pulse ◽  
Cuckoo Search ◽  
Optimization Techniques ◽  
Radar Signal ◽  
Lévy Flight ◽  
Levy Flight

In radar signal processing pulse compression has been extensively used which solves the problem of maintaining simultaneously high transmit energy of long pulse and large range resolution of short pulse. The concept of pulse compression can be best understood from matched filtering that determines the ratio of peak of the sidelobe to peak value of mainlobe. But the resolution of weak targets from stronger one is difficult due to range sidelobes in the auto-correlation pattern of matched filter. With this idea of reducing these sidelobes, various optimization techniques are used. This paper represents a method to optimize the performance of chaotic sequence using mismatched filter. The optimization completely depends on the design of coefficients of mismatched filter at the receiver side. Here improved cuckoo search method is used instead of Lévy flight cuckoo search with the differential evolution technique to complete the design of cascaded mismatched filter. Finally, improved results are obtained as compared to Lévy flight method of cuckoo search.

scholarly journals Simulation and Performance Analysis of Chaotic Sequences using Enhanced Cuckoo Search Optimization Method

2019 ◽  
Vol 8 (4) ◽  
pp. 10225-10231
Keyword(s):  
Adaptive Filters ◽  
Pulse Compression ◽  
Matched Filter ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Chaotic Sequence ◽  
Radar Signal ◽  
Linear Filter

The mostdesirable property required for pulse compression is that the output should have low peak sidelobes that prevent weaker targets from being masked off in the nearby strong targets. Pulse compression can be obtained with matched filter. Matched filter is an optimal linear filter used in radar signal processing and various communication fields to increase the signal to noise ratio. The output of matched filter consists of unavoidable sidelobes which causes false alarm for multiple target detection in many radar system design.For this purpose, mismatched filter is used after matched filter. Inthis paper a new method of design of mismatched filter is discussed which reduces these sidelobes in the compressed waveform. Here new version of cuckoo search algorithm is used along with differential evolution techniquefor complete design of proposed filter to compare the performance of chaotic sequence. The performance of pulse compression is measured in terms of peak sidelobe ratio. The simulation results showthatdevelopmentin the performance of chaotic sequence is obtained at the output of cascaded filter. And further improved performance is achieved with adaptive filters

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

scholarly journals A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Le Wang ◽  
Yuelin Gao ◽  
Jiahang Li ◽  
Xiaofeng Wang
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Chaotic Map ◽  
Feature Selection Method ◽  
Cuckoo Search ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Mutation Strategy ◽  
Nature Inspired Algorithm

Feature selection is an essential step in the preprocessing of data in pattern recognition and data mining. Nowadays, the feature selection problem as an optimization problem can be solved with nature-inspired algorithm. In this paper, we propose an efficient feature selection method based on the cuckoo search algorithm called CBCSEM. The proposed method avoids the premature convergence of traditional methods and the tendency to fall into local optima, and this efficient method is attributed to three aspects. Firstly, the chaotic map increases the diversity of the initialization of the algorithm and lays the foundation for its convergence. Then, the proposed two-population elite preservation strategy can find the attractive one of each generation and preserve it. Finally, Lévy flight is developed to update the position of a cuckoo, and the proposed uniform mutation strategy avoids the trouble that the search space is too large for the convergence of the algorithm due to Lévy flight and improves the algorithm exploitation ability. The experimental results on several real UCI datasets show that the proposed method is competitive in comparison with other feature selection algorithms.

scholarly journals Hybridizing the Cuckoo Search Algorithm with Different Mutation Operators for Numerical Optimization Problems

2018 ◽  
Vol 29 (1) ◽  
pp. 1043-1062 ◽  
Author(s):  
Bilal H. Abed-alguni ◽  
David J. Paul
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Mutation Operators ◽  
Efficient Exploration ◽  
Polynomial Mutation

Abstract The Cuckoo search (CS) algorithm is an efficient evolutionary algorithm inspired by the nesting and parasitic reproduction behaviors of some cuckoo species. Mutation is an operator used in evolutionary algorithms to maintain the diversity of the population from one generation to the next. The original CS algorithm uses the Lévy flight method, which is a special mutation operator, for efficient exploration of the search space. The major goal of the current paper is to experimentally evaluate the performance of the CS algorithm after replacing the Lévy flight method in the original CS algorithm with seven different mutation methods. The proposed variations of CS were evaluated using 14 standard benchmark functions in terms of the accuracy and reliability of the obtained results over multiple simulations. The experimental results suggest that the CS with polynomial mutation provides more accurate results and is more reliable than the other CS variations.

An Efficient Load Forecasting in Predictive Control Strategy Using Hybrid Neural Network

2019 ◽  
Vol 29 (01) ◽  
pp. 2050010 ◽  
Author(s):  
Shweta Sengar ◽  
Xiaodong Liu
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Cuckoo Search ◽  
Forecasting Model ◽  
Lévy Flight ◽  
Hybrid Neural Network ◽  
Levy Flight ◽  
Power Load ◽  
Power Load Forecasting

Load forecasting is a difficult task, because the load series is complex and exhibits several levels of seasonality. The load at a given hour is dependent not only on the load at the previous day, but also on the load at the same hour on the previous day and previous week, and because there are many important exogenous variables that must be considered. Most of the researches were simultaneously concentrated on the number of input variables to be considered for the load forecasting problem. In this paper, we concentrate on optimizing the load demand using forecasting of the weather conditions, water consumption, and electrical load. Here, the neural network (NN) power load forecasting model clubbed with Levy-flight from cuckoo search algorithm is proposed, i.e., called hybrid neural network (HNN), and named as LF-HNN, where the Levy-flight is used to automatically select the appropriate spread parameter value for the NN power load forecasting model. The results from the simulation work have demonstrated the value of the LF-HNN approach successfully selected the appropriate operating mode to achieve optimization of the overall energy efficiency of the system using all available energy resources.

scholarly journals A cuckoo search algorithm: Effects of coevolution and application in the development of distributed layouts

2019 ◽  
Vol 13 ◽  
pp. 174830261988952
Author(s):  
Yung C Shih
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Computational Time ◽  
Lévy Flight ◽  
Egg Weight ◽  
Levy Flight ◽  
Low Degree ◽  
Animal Communities

This article presents a cuckoo search algorithm, via Lévy flight, considering the effects of coevolution on the displacement dynamics of animal communities, and applies this algorithm to the development of maximally distributed physical arrangements. Some variables are introduced such as egg weight and the acquired learning of host birds. The results show that the proposed algorithm prevails, in the majority of the interviewed instances, in the degree of distribution in comparison with the classic TARGET and ALVO methods, and with the Genetic Algorithm. The proposed algorithm was shown to be able to obtain a low degree of distribution in a satisfactory computational time, especially in large problems.

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