Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model

2020 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Ting Zhang ◽  
Chun Liu ◽  
Yuan-xiang Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Size Control ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optimum ◽  
Step Size ◽  
Q Learning ◽  
Fitness Value ◽  
The Individual ◽  
Evolution Effect

Abstract As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.

scholarly journals Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 149 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Hong Lei ◽  
Ting Zhang ◽  
Tian Tian
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Distribution Center ◽  
Step Size ◽  
Q Learning ◽  
Logistics Distribution ◽  
Center Location ◽  
The Individual ◽  
Distribution Center Location

Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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.

scholarly journals Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pauline Ong
Keyword(s):  
Marked Improvement ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimal Solutions ◽  
Step Size ◽  
Adjustment Strategy ◽  
Global Optimal ◽  
Adaptive Step ◽  
Size Adjustment

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

scholarly journals Corrigendum to: Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model

2020 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Ting Zhang ◽  
Chun Liu ◽  
Yuan-xiang Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Learning Model ◽  
Cuckoo Search Algorithm ◽  
Q Learning

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.

Optimized Radial Basis Neural Network for the Classification of Breast Cancer Images

2020 ◽  
Vol 16 ◽  
Author(s):  
G. M. Rajathi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Breast Feeding ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Feature Reduction ◽  
Radial Basis Neural Network ◽  
Fitness Value ◽  
Radial Basis

Background: The breast cancer is not such a dreadful if the detection is not performed at an early. The chances of having breast cancer is the married woman highly after the breast-feeding phase because, the cancer is formed from the blocked milk ducts. Introduction: Recent days, the cancer is the major issue for human death. The women are mostly affected by breast cancer. This leads to deadliest life of most of the women. The breast cancer is caused while breast-feeding phase. The early detection technique uses the mammography image analysis. Various researchers are used the artificial intelligence based mammogram techniques. This process of mammography will reduce the death rate of the patients affected breast cancer. This process is improved by image analysing, detection, screening, diagnosing, and other performance measures. Methods: The radial basis neural network will be used for the classification purpose. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for training process. The cuckoo search algorithm will be used for this purpose. Results: Thus, the proposed optimum RBNN is determined to classify the breast cancer images. In this, the three set of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a. Since the proposed system is most efficient than most recent related literatures. Conclusion: Thus, it concluded with the efficient classification process of RBNN using cuckoo search algorithm for breast cancer images. The mammogram images are taken into the recent research because the breast cancer is the major issue for women. This process is carried to classify the various features for three set of properties. The optimized classifier improves the performance and provides the better result. In this proposed research work, the input image is filtered using wiener filter and the classifier extracts the feature based on the breast image.

scholarly journals An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yanhong Feng ◽  
Ke Jia ◽  
Yichao He
Keyword(s):  
Optimization Problem ◽  
Feasible Solution ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Genetic Mutation ◽  
Cuckoo Search Algorithm ◽  
Knapsack Problems ◽  
Local Optimum ◽  
Transform Method ◽  
Continuous Space

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

scholarly journals Collision Avoidance Controller for Unmanned Surface Vehicle Based on Improved Cuckoo Search Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9741
Author(s):  
Yunsheng Fan ◽  
Xiaojie Sun ◽  
Guofeng Wang ◽  
Dongdong Mu
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Variable Step Size ◽  
Size Factor ◽  
Step Size ◽  
Unmanned Surface Vehicle ◽  
Avoidance Strategy ◽  
Improved Cuckoo Search Algorithm

For the dynamic collision avoidance problem of an unmanned surface vehicle (USV), a dynamic collision avoidance control method based on an improved cuckoo search algorithm is proposed. The collision avoidance model for a USV and obstacles is established on the basis of the principle of the velocity obstacle method. Simultaneously, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is incorporated in the collision avoidance process. For the improvement of the cuckoo algorithm, the adaptive variable step-size factor is designed to realize the adaptive adjustment of flight step-size, and a mutation and crossover strategy is introduced to enhance the population diversity and improve the global optimization ability. The improved cuckoo search algorithm is applied to the collision avoidance model to obtain an optimal collision avoidance strategy. According to the collision avoidance strategy, the desired evasion trajectory is obtained, and the tracking controller based on PID is used for the Lanxin USV. The experimental results show the feasibility and effectiveness of the proposed collision avoidance method, which provides a solution for the autonomous dynamic collision avoidance of USVs.

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