Seagull-Cuckoo Search Algorithm for Function Optimization

2021 ◽  
Author(s):  
Gyanesh Das ◽  
Rutuparna Panda
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
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.

scholarly journals Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jie-sheng Wang ◽  
Shu-xia Li ◽  
Jiang-di Song
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Pso Algorithm ◽  
Cuckoo Search Algorithm ◽  
Function Optimization ◽  
Bee Colony ◽  
Disturbance Factor ◽  
Improved Cuckoo Search Algorithm ◽  
Self Learning

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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