Nature-Inspired Algorithms and Applications

11Applications of Cuckoo Search Algorithm for Optimization Problems

Akanksha Deep and Prasant Kumar Dash*

Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha, India

Abstract

In recent years, algorithms inspired by nature have congregated a lot of attention in solving the complex real-world problems. The optimization technique is the way to reach the best performance systems by maximizing the output factors and minimizing input. Similar algorithms inspired by nature used for optimization are Ant Colony Optimization, Genetic Algorithm, Bat Algorithm, Firefly Algorithm, Particle Swarm Optimization (PSO) Algorithm, Cuckoo Search (CS), Bird Flocking, Tabu Search (TS), Artificial Bee Colony Optimization, etc. These algorithms are classified on the basis of two key elements such as diversification and aggregation generally called as exploitation and exploration. The challenges with intense exploration are that it does not provide an optimal solution. On the other hand, if deep exploitation is used, then it traps the algorithm with local optima. A harmony between the local and global optima is eminent fundamental for algorithm inspired by nature. According to the statistical results, Cuckoo Search Algorithm (CSA) has been implemented in different area to optimize the solution. The major domains where CSA implements are image processing, pattern recognition, software testing, data mining, cyber security, cloud computing, IoT etc. This chapter ...