scholarly journals A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 555 ◽  
Author(s):  
José García ◽  
Victor Yepes ◽  
José V. Martí
Keyword(s):  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Retaining Wall ◽  
Harmony Search ◽  
Solution Space ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Random Operator ◽  
Point Of View ◽  
Optimization Mechanism

The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems

2016 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Distributed Resources ◽  
Computing Systems ◽  
Grid Systems ◽  
Job Scheduling Problem

Job scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization (ACO) to show the importance of the proposed algorithm.

Application of Differential Evolution Cuckoo Search Algorithm in Parameter Optimization of VG Equation

2021 ◽  
Vol 35 (09) ◽  
pp. 2159033
Author(s):  
Bo Yuan ◽  
Deji Chen
Keyword(s):  
Soil Moisture ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Optimization Model ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Characteristic Curve ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Moisture Characteristic

Van Genuchten (VG) equation is the most commonly used equation of soil moisture characteristic curve, and the accuracy of its parameters directly affects the calculation accuracy of soil moisture motion equation. In order to obtain the parameters of the equation more accurately, this paper establishes an optimization model of the VG equation parameters. This optimization model uses the advantages of the cuckoo search algorithm and the differential evolution algorithm to combine the two into a new hybrid cuckoo search algorithm, namely DECS algorithm, and uses this algorithm to solve the parameter optimization problem of VG equation of soil moisture characteristic curve. By collecting and analyzing the relevant experimental data of various soil qualities, the dehumidification and moisture absorption curves of three different soil qualities were selected for simulation calculation. The results show that in the experiment of solving the parameter estimation problem of the VG equation, the DECS hybrid algorithm has better exploration and development capabilities than the cuckoo search algorithm. The hybrid algorithm has relatively significant performance in terms of calculation accuracy and convergence.

scholarly journals Cuckoo Search Algorithm for Optimal Placement and Sizing of Static Var Compensator in Large-Scale Power Systems

2016 ◽  
Vol 6 (2) ◽  
pp. 59-68 ◽  
Author(s):  
Khai Phuc Nguyen ◽  
Goro Fujita ◽  
Vo Ngoc Dieu
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Harmony Search Algorithm ◽  
Optimization Techniques ◽  
Optimal Placement ◽  
Static Var Compensator

Abstract This paper presents an application of Cuckoo search algorithm to determine optimal location and sizing of Static VAR Compensator. Cuckoo search algorithm is a modern heuristic technique basing Cuckoo species’ parasitic strategy. The Lévy flight has been employed to generate random Cuckoo eggs. Moreover, the objective function is a multiobjective problem, which minimizes loss power, voltage deviation and investment cost of Static VAR Compensator while satisfying other operating constraints in power system. Cuckoo search algorithm is evaluated on three case studies and compared with the Teaching-learning-based optimization, Particle Swarm optimization and Improved Harmony search algorithm. The results show that Cuckoo search algorithm is better than other optimization techniques and its performance is also better.

scholarly journals A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2611
Author(s):  
José García ◽  
José Lemus-Romani ◽  
Francisco Altimiras ◽  
Broderick Crawford ◽  
Ricardo Soto ◽  
...  
Keyword(s):  
Local Search ◽  
Knapsack Problem ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Local Search Operator ◽  
Insight Into

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

A new hybrid cuckoo search and firefly optimization

2018 ◽  
Vol 24 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Mariam Elkhechafi ◽  
Hanaa Hachimi ◽  
Youssfi Elkettani
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Firefly Optimization ◽  
New Generation

Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms (CS-FA).

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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