scholarly journals Genetic Optimization of a Manipulator: Comparison between Straight, Rounded, and Curved Mechanism Links

2021 ◽  
Vol 11 (6) ◽  
pp. 2471
Author(s):  
Robert Pastor ◽  
Zdenko Bobovský ◽  
Daniel Huczala ◽  
Stefan Grushko
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Lower Cost ◽  
Robot Manipulators ◽  
Industrial Robots ◽  
Genetic Optimization ◽  
Mechanism Synthesis ◽  
Angular Structure ◽  
Manipulator Link ◽  
Hermite Spline

There are several ubiquitous kinematic structures that are used in industrial robots, with the most prominent being a six-axis angular structure. However, researchers are experimenting with task-based mechanism synthesis that could provide higher efficiency with custom optimized manipulators. Many studies have focused on finding the most efficient optimization algorithm for task-based robot manipulators. These manipulators, however, are usually optimized from simple modular joints and links, without exploring more elaborate modules. Here, we show that link modules defined by small numbers of parameters have better performance than more complicated ones. We compare four different manipulator link types, namely basic predefined links with fixed dimensions, straight links that can be optimized for different lengths, rounded links, and links with a curvature defined by a Hermite spline. Manipulators are then built from these modules using a genetic algorithm and are optimized for three different tasks. The results demonstrate that manipulators built from simple links not only converge faster, which is expected given the fewer optimized parameters, but also converge on lower cost values.

scholarly journals A Genetic Optimization Algorithm Based on Adaptive Dimensionality Reduction

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Tai Kuang ◽  
Zhongyi Hu ◽  
Minghai Xu
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
High Dimensional ◽  
Adaptive Genetic Algorithm ◽  
Genetic Optimization ◽  
Number Of Zeros ◽  
Genetic Optimization Algorithm

With the rise of big data in cloud computing, many optimization problems have gradually developed into high-dimensional large-scale optimization problems. In order to address the problem of dimensionality in optimization for genetic algorithms, an adaptive dimensionality reduction genetic optimization algorithm (ADRGA) is proposed. An adaptive vector angle factor is introduced in the algorithm. When the angle of an individual’s adjacent dimension is less than the angle factor, the value of the smaller dimension is marked as 0. Then, the angle between each individual dimension is calculated separately, and the number of zeros in the population is updated. When the number of zeros of all individuals in a population exceeds a given constant in a certain dimension, the dimension is considered to have no more information and deleted. Eight high-dimensional test functions are used to verify the proposed adaptive dimensionality reduction genetic optimization algorithm. The experimental results show that the convergence, accuracy, and speed of the proposed algorithm are better than those of the standard genetic algorithm (GA), the hybrid genetic and simulated annealing algorithm (HGSA), and the adaptive genetic algorithm (AGA).

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

scholarly journals Advanced SIMION Techniques: Boundary Matching and Genetic Optimization

2015 ◽  
Vol 21 (S4) ◽  
pp. 218-223 ◽  
Author(s):  
D. Dowsett
Keyword(s):  
Genetic Algorithm ◽  
Real World ◽  
Great Accuracy ◽  
Genetic Optimization ◽  
Real World Applications ◽  
Boundary Matching

AbstractTwo techniques for use with SIMION [1] are presented, boundary matching and genetic optimization. The first allows systems which were previously difficult or impossible to simulate in SIMION to be simulated with great accuracy. The second allows any system to be rapidly and robustly optimized using a parallelized genetic algorithm. Each method will be described along with examples of real world applications.

Sign in / Sign up

Export Citation Format

Share Document