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.

Cuckoo Search Algorithm for Optimization Problems - A Literature Review

2013 ◽  
Vol 421 ◽  
pp. 502-506 ◽  
Author(s):  
Azizah Mohamad ◽  
Azlan Mohd Zain ◽  
Nor Erne Nazira Bazin ◽  
Amirmudin Udin
Keyword(s):  
Optimization Algorithm ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Egg Laying ◽  
Wireless Sensor ◽  
Data Generation ◽  
Recognition Software

Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This paper describes an overview of CS which is inspired by the life of a bird family, called Cuckoo as well as overview of CS applications in various categories for solving optimization problems. Special lifestyle of Cuckoo and their characteristics in egg laying and breeding has been the basic motivation for this optimization algorithm. The categories that reviewed are Engineering, Pattern Recognition, Software Testing & Data Generation, Networking, Job Scheduling and Data Fusion and Wireless Sensor Networks. From the reviewed CS mostly applied in engineering area for solving optimization problems. The objective of this paper is to provide overview and summarize the review of application of the CS.

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.

scholarly journals A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1840
Author(s):  
Nicolás Caselli ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Sergio Valdivia ◽  
Rodrigo Olivares
Keyword(s):  
Machine Learning ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Parameter Configuration ◽  
Self Tuning ◽  
Problem Solvers

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

Economic Load Dispatch With Multiple Fuel Options and Valve Point Effect Using Cuckoo Search Algorithm With Different Distributions

2020 ◽  
Vol 9 (3) ◽  
pp. 24-38
Author(s):  
Cuong Dinh Tran ◽  
Tam Thanh Dao ◽  
Ve Song Vo
Keyword(s):  
Random Walk ◽  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cauchy Distribution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Natural Phenomenon ◽  
Economic Load Dispatch ◽  
Valve Point Effect

The cuckoo search algorithm (CSA), a new meta-heuristic algorithm based on natural phenomenon of the cuckoo species and Lévy flights random walk has been widely and successfully applied to several optimization problems so far. In the article, two modified versions of CSA, where new solutions are generated using two distributions including Gaussian and Cauchy distributions in addition to imposing bound by best solutions mechanisms are proposed for solving economic load dispatch (ELD) problems with multiple fuel options. The advantages of CSA with Gaussian distribution (CSA-Gauss) and CSA with Cauchy distribution (CSA-Cauchy) over CSA with Lévy distribution and other meta-heuristic are fewer parameters. The proposed CSA methods are tested on two systems with several load cases and obtained results are compared to other methods. The result comparisons have shown that the proposed methods are highly effective for solving ELD problem with multiple fuel options and/nor valve point effect.

Reliability Optimization of Complex Systems Using Cuckoo Search Algorithm

2017 ◽  
pp. 94-110 ◽  
Author(s):  
Anuj Kumar ◽  
Sangeeta Pant ◽  
S. B. Singh
Keyword(s):  
Complex Systems ◽  
Life Support ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Reliability Optimization ◽  
Space Capsule ◽  
Metaheuristic Techniques

In this chapter, authors briefly discussed about the classification of reliability optimization problems and their nature. Background of reliability and optimization has also been provided separately so that one can clearly understand the basic terminology used in the field of reliability optimization. Classification of various optimization techniques have also been discussed by the authors. Few metaheuristic techniques related to reliability optimization problems like Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been discussed in brief. Thereafter, authors have discussed about Cuckoo Search Algorithm (CSA) which is the main focus of this chapter. Finally, Cuckoo Search Algorithm has been applied for solving reliability optimization problems of two complex systems namely complex bridge system and life support system in space capsule. Simulation results and conclusion have been presented in the last followed by the references.

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.

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