scholarly journals A Modified Cuckoo Search Algorithm and Its Applications in Function Optimization

2021 ◽  
Vol 2129 (1) ◽  
pp. 012025
Author(s):  
Shao Qiang Ye ◽  
Fang Ling Wang ◽  
Kai Qing Zhou
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Step Length ◽  
Cuckoo Search Algorithm ◽  
Function Optimization ◽  
High Dimensional ◽  
Random Operators ◽  
Lévy Flight ◽  
Adjustment Mechanism ◽  
Search Mechanism

Abstract A modified Cuckoo search algorithm (MCS) is proposed in this paper to improve the accuracy of the algorithm’s convergence by implementing random operators and adapt the adjustment mechanism of the Levy Flight search step length. Comparative experiments reveal that MCS can effectively adjust the search mechanism in the high-dimensional function optimization and converge to the optimal global value.

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.

Performances of Adaptive Cuckoo Search Algorithm in Engineering Optimization

2016 ◽  
pp. 676-699 ◽  
Author(s):  
Pauline Ong ◽  
S. Kohshelan
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Breeding Strategy ◽  
Function Optimization ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Step Size ◽  
Swarm Optimization ◽  
Searching Strategy

A new optimization algorithm, specifically, the cuckoo search algorithm (CSA), which inspired by the unique breeding strategy of cuckoos, has been developed recently. Preliminary studies demonstrated the comparative performances of the CSA as opposed to genetic algorithm and particle swarm optimization, however, with the competitive advantage of employing fewer control parameters. Given enough computation, the CSA is guaranteed to converge to the optimal solutions, albeit the search process associated to the random-walk behavior might be time-consuming. Moreover, the drawback from the fixed step size searching strategy in the inner computation of CSA still remain unsolved. The adaptive cuckoo search algorithm (ACSA), with the effort in the aspect of integrating an adaptive search strategy, was attached in this study. Its beneficial potential are analyzed in the benchmark test function optimization, as well as engineering optimization problem. Results showed that the proposed ACSA improved over the classical CSA.

Chaotic Cuckoo Search Algorithm for High-dimensional Functions

2014 ◽  
Vol 9 (5) ◽  
Author(s):  
Aijia Ouyang ◽  
Guo Pan ◽  
Guangxue Yue ◽  
Jiayi Du
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
High Dimensional

Improved Cuckoo Search Algorithm for Future Selection

2021 ◽  
Vol 9 (2) ◽  
pp. 113-123
Author(s):  
T. Mathi Murugan ◽  
◽  
E. Baburaj ◽  
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
High Dimensional ◽  
Svm Classifier ◽  
Knn Classifier ◽  
Experimental Process ◽  
Improved Cuckoo Search Algorithm

The classification of high-dimensional dataset is challenging as it contains large amount irrelevant and noisy features. Thus, feature selection is performed in the dataset to eliminate these redundant features. It reduces the dimensionality of the dataset and increases the classification accuracy. Hence, for selecting the relevant features in high dimensional data, an improved cuckoo search algorithm (ICSA) was proposed in this paper. After feature selection, the dataset undergo classification using KNN classifier and SVM classifier. The experimental process illustrates that the improved cuckoo search algorithm effectively increases the classification accuracy by reducing the number of features in the dataset. For analysing the proposed algorithm, seven UCI repository dataset have been utilised. Also, the ICS algorithm is compared with other existing algorithms for the given dataset. From the investigation process, it was concluded that the proposed algorithm selects lesser number of features and also enhances the classification accuracy than the other existing algorithms.

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