Improving Cuckoo Search Algorithm With Mittag-Leffler Distribution

2019 ◽  
Author(s):  
Jiamin Wei ◽  
YangQuan Chen ◽  
Yongguang Yu ◽  
Yuquan Chen
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Lévy Flights ◽  
Lévy Distribution ◽  
Levy Flights ◽  
Levy Distribution ◽  
Heavy Tailed ◽  
The Impact

Abstract Cuckoo search (CS), as one of the recent nature-inspired metaheuristic algorithms, has proved to be an efficient approach due to the combination of Lévy flights, local search capabilities and guaranteed global convergence. CS uses Lévy flights in global random walk to explore the search space. The Lévy step is taken from the Lévy distribution which is a heavy-tailed probability distribution. In this case, a fraction of large steps are generated, which plays an important role in enhancing search capability of CS. Besides, although many foragers and wandering animals have been shown to follow a Lévy distribution of steps, investigation into the impact of other different heavy-tailed probability distributions on CS is still insufficient up to now. Based on the above considerations, we are motivated to apply the well-known Mittag-Leffler distribution to the standard CS algorithm, and proposed an improved cuckoo search algorithm (CSML) in this paper, where a more efficient search is supposed to take place in the search space thanks to the long jumps. In order to verify the performance of CSML, experiments are carried out on a test suite of 20 benchmark functions. In terms of the observations and results analysis, CSML can be regarded as a new potentially promising algorithm for solving optimization problems.

scholarly journals Optimal Randomness in Swarm-Based Search

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 828 ◽  
Author(s):  
Jiamin Wei ◽  
YangQuan Chen ◽  
Yongguang Yu ◽  
Yuquan Chen
Keyword(s):  
Probability Distributions ◽  
Cauchy Distribution ◽  
Cuckoo Search ◽  
Search Algorithms ◽  
Lévy Flights ◽  
Lévy Distribution ◽  
Resource Searching ◽  
Levy Flights ◽  
Levy Distribution ◽  
Heavy Tailed

Lévy flights is a random walk where the step-lengths have a probability distribution that is heavy-tailed. It has been shown that Lévy flights can maximize the efficiency of resource searching in uncertain environments and also the movements of many foragers and wandering animals have been shown to follow a Lévy distribution. The reason mainly comes from the fact that the Lévy distribution has an infinite second moment and hence is more likely to generate an offspring that is farther away from its parent. However, the investigation into the efficiency of other different heavy-tailed probability distributions in swarm-based searches is still insufficient up to now. For swarm-based search algorithms, randomness plays a significant role in both exploration and exploitation or diversification and intensification. Therefore, it is necessary to discuss the optimal randomness in swarm-based search algorithms. In this study, cuckoo search (CS) is taken as a representative method of swarm-based optimization algorithms, and the results can be generalized to other swarm-based search algorithms. In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution, Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS. Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances. Finally, the proposed methods are used to system identification to demonstrate the effectiveness.

scholarly journals Cuckoo Search Algorithm with Lévy Flights for Global-Support Parametric Surface Approximation in Reverse Engineering

Symmetry ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 58 ◽  
Author(s):  
Andrés Iglesias ◽  
Akemi Gálvez ◽  
Patricia Suárez ◽  
Mikio Shinya ◽  
Norimasa Yoshida ◽  
...  
Keyword(s):  
Reverse Engineering ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parametric Surface ◽  
Lévy Flights ◽  
Surface Approximation ◽  
Levy Flights

scholarly journals Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Lévy Flights

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ozgur Baskan
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Search Algorithm ◽  
Road Networks ◽  
Cuckoo Search ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Lévy Flights ◽  
Link Capacity ◽  
Levy Flights

During the last two decades, Continuous Network Design Problem (CNDP) has received much more attention because of increasing trend of traffic congestion in road networks. In the CNDP, the problem is to find optimal link capacity expansions by minimizing the sum of total travel time and investment cost of capacity expansions in a road network. Considering both increasing traffic congestion and limited budgets of local authorities, the CNDP deserves to receive more attention in order to use available budget economically and to mitigate traffic congestion. The CNDP can generally be formulated as bilevel programming model in which the upper level deals with finding optimal link capacity expansions, whereas at the lower level, User Equilibrium (UE) link flows are determined by Wardrop’s first principle. In this paper, cuckoo search (CS) algorithm with Lévy flights is introduced for finding optimal link capacity expansions because of its recent successful applications in solving such complex problems. CS is applied to the 16-link and Sioux Falls networks and compared with available methods in the literature. Results show the potential of CS for finding optimal or near optimal link capacity expansions in a given road network.

A Survey of the Cuckoo Search and Its Applications in Real-World Optimization Problems

2017 ◽  
pp. 541-555 ◽  
Author(s):  
Christopher Expósito-Izquierdo ◽  
Airam Expósito-Márquez
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Population Based ◽  
Cuckoo Search Algorithm ◽  
Breeding Strategy ◽  
Egg Laying ◽  
Practical Applications ◽  
Wide Range

The chapter at hand seeks to provide a general survey of the Cuckoo Search Algorithm and its most highlighted variants. The Cuckoo Search Algorithm is a relatively recent nature-inspired population-based meta-heuristic algorithm that is based upon the lifestyle, egg laying, and breeding strategy of some species of cuckoos. In this case, the Lévy flight is used to move the cuckoos within the search space of the optimization problem to solve and obtain a suitable balance between diversification and intensification. As discussed in this chapter, the Cuckoo Search Algorithm has been successfully applied to a wide range of heterogeneous optimization problems found in practical applications over the last few years. Some of the reasons of its relevance are the reduced number of parameters to configure and its ease of implementation.

Automatic Test Data Generation using Metaheuristic Cuckoo Search Algorithm

2015 ◽  
Vol 5 (2) ◽  
pp. 16-29 ◽  
Author(s):  
Madhumita Panda ◽  
Partha Pratim Sarangi ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Benchmark Problems ◽  
Unit Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
High Convergence Rate

The proposed work emphasizes on the automated process of test data generation for unit testing of structured programs, targeting complete path coverage of the software under test. In recent years, Cuckoo Search (CS) has been successfully applied in many engineering applications because of its high convergence rate to the global solution. The authors motivated with the performance of Cuckoo search, utilized it to generate test suits for the standard benchmark problems, covering entire search space of the input data in less iterations. The experimental results reveal that the proposed approach covers entire search space generating test data for all feasible paths of the problem in few number of generations. It is observed that proposed approach gives promising results and outperforms other reported algorithms and it can be an alternative approach in the field of test data generation.

A Multilevel Image Thresholding Method Using the Darwinian Cuckoo Search Algorithm

2021 ◽  
pp. 2150052
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Computational Time ◽  
Energy Curve ◽  
Multilevel Thresholding ◽  
Curve Method ◽  
Thresholding Algorithm

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods.

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.

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