Rough Sets Crow Search Algorithm for Inverse Kinematics

2021 ◽  
pp. 221-235
Author(s):  
Mohamed Slim ◽  
Nizar Rokbani ◽  
Mohamed Ali Terres
Keyword(s):  
Rough Sets ◽  
Inverse Kinematics ◽  
Search Algorithm

Modified cuckoo search algorithm with rough sets for feature selection

2016 ◽  
Vol 29 (4) ◽  
pp. 925-934 ◽  
Author(s):  
Mohamed Abd El Aziz ◽  
Aboul Ella Hassanien
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

scholarly journals Learning the Kinematics of a Manipulator Based on VQTAM

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 519
Author(s):  
Luo Lan ◽  
Hou Li ◽  
Wu Yang ◽  
Wei Yongqiao ◽  
Zhang Qi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Surrogate Model ◽  
Inverse Kinematics ◽  
Search Algorithm ◽  
Robotic Manipulator ◽  
Local Linear Regression ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Value Decomposition

The kinematics of a robotic manipulator is critical to the real-time performance and robustness of the robot control system. This paper proposes a surrogate model of inverse kinematics for the serial six-degree of freedom (6-DOF) robotic manipulator, based on its kinematics symmetry. Herein, the inverse kinematics model is derived via the training of the Vector-Quantified Temporal Associative Memory (VQTAM) network, which originates from Self-Organized Mapping (SOM). During the processes of training, testing, and estimating of this neural network, a priority K-means tree search algorithm is utilized, thus improving the training efficacy. Furthermore, Local Linear Regression (LLR), Local Weighted Linear Regression (LWR), and Local Linear Embedding (LLE) algorithms are, respectively, combined with VQTAM to obtain three improvement algorithms, all of which aim to further optimize the prediction accuracy of the networks for subsequent comparison and selection. To speed up the solving of the least squared equation, which is common among the three algorithms, Singular Value Decomposition (SVD) is introduced. Finally, data from forward kinematics, in the form of the exponential product of a motion screw, are obtained, and are used for the construction and validation of the VQTAM neural network. Our results show that the prediction effect of the LLE algorithm is better than others, and that the LLE algorithm is a potential surrogate model to estimate the output of inverse kinematics.

scholarly journals Geometric Lattice Structure of Covering-Based Rough Sets through Matroids

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Aiping Huang ◽  
William Zhu
Keyword(s):  
Rough Sets ◽  
Rough Set Theory ◽  
Lattice Structure ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Geometric Lattice ◽  
Lattice Structures ◽  
Upper Approximation ◽  
The Relationship ◽  
Geometric Lattices

Covering-based rough set theory is a useful tool to deal with inexact, uncertain, or vague knowledge in information systems. Geometric lattice has been widely used in diverse fields, especially search algorithm design, which plays an important role in covering reductions. In this paper, we construct three geometric lattice structures of covering-based rough sets through matroids and study the relationship among them. First, a geometric lattice structure of covering-based rough sets is established through the transversal matroid induced by a covering. Then its characteristics, such as atoms, modular elements, and modular pairs, are studied. We also construct a one-to-one correspondence between this type of geometric lattices and transversal matroids in the context of covering-based rough sets. Second, we present three sufficient and necessary conditions for two types of covering upper approximation operators to be closure operators of matroids. We also represent two types of matroids through closure axioms and then obtain two geometric lattice structures of covering-based rough sets. Third, we study the relationship among these three geometric lattice structures. Some core concepts such as reducible elements in covering-based rough sets are investigated with geometric lattices. In a word, this work points out an interesting view, namely, geometric lattice, to study covering-based rough sets.

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.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems

2014 ◽  
Vol 2 ◽  
pp. 362-365
Author(s):  
Akio Watanabe ◽  
Kaori Kuroda ◽  
Kantaro Fujiwara ◽  
Tohru Ikeguchi
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Quadratic Assignment ◽  
Local Search Algorithm ◽  
Assignment Problems ◽  
Quadratic Assignment Problems
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