Genetic algorithms and greedy-randomized adaptive search procedure for router placement problem in wireless networks

2019 ◽  
Vol 25 (3) ◽  
pp. 273-300
Author(s):  
Kadri Sylejmani ◽  
Admir Kadriu ◽  
Endrit Ilazi ◽  
Bujar Krasniqi
Keyword(s):  
Genetic Algorithms ◽  
Wireless Networks ◽  
Search Procedure ◽  
Adaptive Search ◽  
Placement Problem

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

scholarly journals Jobs scheduling within Industry 4.0 with consideration of worker’s fatigue and reliability using Greedy Randomized Adaptive Search Procedure

2019 ◽  
Vol 52 (19) ◽  
pp. 85-90
Author(s):  
I. El Mouayni ◽  
G. Demesure ◽  
H. Bril-El Haouzi ◽  
P. Charpentier ◽  
A. Siadat
Keyword(s):  
Industry 4.0 ◽  
Search Procedure ◽  
Adaptive Search

Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks

2012 ◽  
Vol 3 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Hui Cheng
Keyword(s):  
Genetic Algorithms ◽  
Wireless Networks ◽  
Shortest Path ◽  
Network Topology ◽  
Ad Hoc ◽  
Optimization Techniques ◽  
Mobile Wireless Networks ◽  
Mobile Wireless ◽  
Routing Problem ◽  
Mobile Ad Hoc

In recent years, the static shortest path (SP) routing problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network (WMN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. This paper proposes to use two types of hyper-mutation GAs to solve the dynamic SP routing problem in MANETs. The authors consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the two hyper-mutation GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

Finding weaknesses in networks using Greedy Randomized Adaptive Search Procedure and Path Relinking

2020 ◽  
Vol 37 (6) ◽  
Author(s):  
Sergio Pérez‐Peló ◽  
Jesús Sánchez‐Oro ◽  
Abraham Duarte
Keyword(s):  
Path Relinking ◽  
Search Procedure ◽  
Adaptive Search

Extraction of Classification Rules in Databases through Metaheuristic Procedures Based on GRASP

2014 ◽  
Vol 945-949 ◽  
pp. 3369-3375
Author(s):  
Genival Pavanelli ◽  
Maria Teresinha Arns Steiner ◽  
Anderson Roges Teixeira Góes ◽  
Alessandra Memari Pavanelli ◽  
Deise Maria Bertholdi Costa
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Discovery In Databases ◽  
Personal Knowledge ◽  
Professional Experience ◽  
Search Procedure ◽  
Adaptive Search ◽  
Classification Rules ◽  
Constant Attention ◽  
Whole Process

The process of knowledge management in the several areas of society requires constant attention to the multiplicity of decisions to be made about the activities in organizations that constitute them. To make these decisions one should be cautious in relying only on personal knowledge acquired through professional experience, since the whole process based on this method would be slow, expensive and highly subjective. To assist in this management, it is necessary to use mathematical tools that fulfill the purpose of extracting knowledge from database. This article proposes the application of Greedy Randomized Adaptive Search Procedure (GRASP) as Data Mining (DM) tool within the process called Knowledge Discovery in Databases (KDD) for the task of extracting classification rules in databases.

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