Calibration of Modular Reconfigurable Robots Based on a Hybrid Search Method

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
Yu Lin ◽  
Fengfeng Xi ◽  
Richard Phillip Mohamed ◽  
Xiao-wei Tu
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Configuration Space ◽  
Global Solutions ◽  
Search Method ◽  
Hybrid Search ◽  
Reconfigurable Robots ◽  
Genetic Algorithm Method

Developed in this paper is a hybrid method for calibration of modular reconfigurable robots (MRRs). The underlying problem under study is unique to MRRs, that is, how to calibrate a set of MRR’s geometric parameters that are applicable to all feasible configurations. For this reason, a hybrid search method is developed to ensure a global search over the MRRs’ workspace for each feasible configuration. By combining a genetic algorithm method with a Monte Carlo method, this method includes three levels of search, namely, pose, workspace, and configuration-space. The final set of global solutions is generated progressively from the results of these three levels of search. The effectiveness of this method is demonstrated through a case study.

Constructing Attractors via the Improved Eugenics Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1786-1789
Author(s):  
Li Ming Du ◽  
Feng Ying Wang ◽  
Zi Yang Han
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Genetic Drift ◽  
Chaotic Attractors ◽  
Parameter Vector ◽  
Distance Limit

The paper introduces Monte Carlo method and Eugenics genetic algorithm, which be used to generate a great diversity of chaotic attractors firstly. By an analysis of their algorithms, a improved eugenics genetic algorithm is presented to avoid the "genetic drift" phenomenon in attractor graphics. A parameter vector distance limit is adopted to solve the problem and lots of experiments applying equivalent mappings of frieze group are finished to validate effectiveness for algorithm.

scholarly journals Optimization of the Nonlinear Muskingum Model Parameters for the River Routing, Tigris River a Case Study

2021 ◽  
Vol 16 (6) ◽  
pp. 649-656
Author(s):  
Maher Abd Ameer Kadim ◽  
Isam Issa Omran ◽  
Alaa Ali Salman Al-Taai
Keyword(s):  
Genetic Algorithm ◽  
Least Squares Method ◽  
Harmony Search ◽  
Flood Routing ◽  
Model Parameters ◽  
Muskingum Model ◽  
Tigris River ◽  
Nonlinear Muskingum Model ◽  
Genetic Algorithm Method

Flood forecasting and management are one of the most important strategies necessary for water resource and decision planners in combating flood problems. The Muskingum model is one of the most popular and widely used applications for the purpose of predicting flood routing. The particle swarm optimization (PSO) methodology was used to estimate the coefficients of the nonlinear Muskingum model in this study, comparing the results with the methods of genetic algorithm (GA), harmony search (HS), least-squares method (LSM), and Hook-Jeeves (HJ). The average monthly inflow for the Tigris River upstream at the Al-Mosul dam was selected as a case study for estimating the Muskingum model's parameters. The analytical and statistical results showed that the PSO method is the best application and corresponds to the results of the Muskingum model, followed by the genetic algorithm method, according to the following general descending sequence: PSO, GA, LSM, HJ, HS. The PSO method is characterized by its accurate results and does not require many assumptions and conditions for its application, which facilitates its use a lot in the subject of hydrology. Therefore, it is better to recommend further research in the use of this method in the implementation of future studies and applications.

scholarly journals Optimal sampling in a noisy genetic algorithm for risk-based remediation design

2003 ◽  
Vol 5 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Gayathri Gopalakrishnan ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Fitness Function ◽  
Sampling Strategies ◽  
Robust Designs ◽  
Highly Efficient ◽  
Efficient Sampling

A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.

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