An efficient cuckoo search algorithm for numerical function optimization

2013 ◽  
Author(s):  
Pauline Ong ◽  
Zarita Zainuddin
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Function Optimization ◽  
Numerical Function ◽  
Numerical Function Optimization

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 A Modified Cuckoo Search Algorithm and Its Applications in Function Optimization

2021 ◽  
Vol 2129 (1) ◽  
pp. 012025
Author(s):  
Shao Qiang Ye ◽  
Fang Ling Wang ◽  
Kai Qing Zhou
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Step Length ◽  
Cuckoo Search Algorithm ◽  
Function Optimization ◽  
High Dimensional ◽  
Random Operators ◽  
Lévy Flight ◽  
Adjustment Mechanism ◽  
Search Mechanism

Abstract A modified Cuckoo search algorithm (MCS) is proposed in this paper to improve the accuracy of the algorithm’s convergence by implementing random operators and adapt the adjustment mechanism of the Levy Flight search step length. Comparative experiments reveal that MCS can effectively adjust the search mechanism in the high-dimensional function optimization and converge to the optimal global value.

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