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Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications

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Metaheuristic and Evolutionary Computation: Algorithms and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 916))

Abstract

Metaheuristic algorithms, in the field of engineering, have attracted researchers for problem-solving of complex and non-linear optimization. Many algorithms have been designed to address wide-ranging applications such as GSA—gravitational search algorithms (GSA), PSO—particle swarm optimization, GWO—grey wolf optimization, and various hybrid plus evolutionary algorithms. Hybrid algorithms also made for such wide-range application, but the drawback of such algorithms are convergence time is very high and challenging to implement for multiple wide range applications. Cuckoo Search (CS) is an optimization technique, developed in 2009, is a highly efficient algorithm. It is an algorithm that is based on population and is also a nature-inspired metaheuristic algorithm, which is easy to implement for such applications. The success of the algorithm has been fueled because of its characteristics, i.e. its simplicity, few parameter, ease of implementation. Cuckoos are delightful birds, which have attracted people not only because of their melodious sound but also because of their aggressive reproduction capability. The algorithm addresses two important behavioral aspects of some cuckoos i.e. brood parasitism and levy flights. The two Ani and Guira cuckoo species will all lay eggs in a communal nest. The cuckoo can then remove the eggs laid by others to improve the probability of hatching the laid eggs. Cuckoo immigration along with environmental factors make the cuckoos to find an appropriate and also a place for reproduction and breeding. Cuckoos presents the random walk Levy flight behavior, which enables the algorithm to completely explore the search space. In CS algorithm fixed number of better fitness cuckoos survive in the environment. This chapter introduces with the mathematical concept of CS algorithm and summarizes different research articles where the algorithm has been explored in the field of engineering. Furthermore, the resent version of CS algorithm are addressed which mainly focuses on modified and hybrid versions. The novelty of this chapter is that it presents current trending research aspects of CS algorithm in the field of engineering, machine and deep learning. The chapter concludes with the future direction which can be investigated using CS algorithm in the field of science and technology.

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Abbreviations

ACO:

Ant colony optimization

ANN:

Artificial neural network

APO:

Artificial physics optimization

CBO:

Colliding bodies optimization

CFO:

Central force optimization

CS:

Cuckoo search

CSS:

Charged system search

DE:

Differential evolution

FA:

Firefly algorithm

FPGA:

Field programmable gate array

GA:

Genetic algorithm

GHS:

Global harmony search

GSA:

Gravitational search algorithm

GWO:

Grey wolf optimization

HS:

Harmony search

PCB:

Printed circuit board

PSO:

Particle swarm optimization

SA:

Simulated annealing

SVM:

Support vector machines

TLBO:

Teachin learning based optimization

TS:

Tabu search

WOA:

Whale optimization algorithm

WSN:

Wireless sensor network

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Abhinav Sharma & Aayush Srivastava

  2. Department of Research and Development, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Abhishek Sharma

  3. Department of Electrical Engineering, Rajkiya Engineering College, Ambedkar Nagar, Uttar Pradesh, 224122, India

    Vinay Chowdary & Puneet Joshi

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  5. Puneet Joshi
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Correspondence to Abhinav Sharma .

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Editors and Affiliations

  1. ICE Division, NSUT, Delhi, India

    Hasmat Malik

  2. Dept. of Electrical Engineering, Qatar University, Qatar, Qatar

    Atif Iqbal

  3. Electrical Engineering Department, Rajkiya Engineering College, Uttar Pradesh, India

    Puneet Joshi

  4. Electrical Engineering Department, Rajkiya Engineering College, Uttar Pradesh, India

    Sanjay Agrawal

  5. Electrical Engineering Department, National Institute of Technology, Srinagar, India

    Farhad Ilahi Bakhsh

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Sharma, A., Sharma, A., Chowdary, V., Srivastava, A., Joshi, P. (2021). Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications. In: Malik, H., Iqbal, A., Joshi, P., Agrawal, S., Bakhsh, F.I. (eds) Metaheuristic and Evolutionary Computation: Algorithms and Applications. Studies in Computational Intelligence, vol 916. Springer, Singapore. https://doi.org/10.1007/978-981-15-7571-6_8

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