A Coverage Construction and Hill Climbing Approach for Mesh Router Placement Optimization: Simulation Results for Different Number of Mesh Routers and Instances Considering Normal Distribution of Mesh Clients

Different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA), and PSO-GA hybrid optimization, have been used to optimize digital differential operators so that these can be better fitted to exemplify their new improved fractional order differentiator counterparts. First, the paper aims to provide efficient 2nd and 3rd order operators in connection with process of minimization of error fitness function by registering mean, median, and standard deviation values in different random iterations to ascertain the best results among them, using all the abovementioned EAs. Later, these optimized operators are discretized for half differentiator models for utilizing their restored qualities inhibited from their optimization. Simulation results present the comparisons of the proposed half differentiators with the existing and amongst different models based on 2nd and 3rd order optimized operators. Proposed half differentiators have been observed to approximate the ideal half differentiator and also outperform the existing ones reasonably well in complete range of Nyquist frequency.

Download Full-text

Path planning of a mobile robot using genetic heuristics

Robotica ◽

10.1017/s0263574798000289 ◽

1998 ◽

Vol 16 (5) ◽

pp. 575-588 ◽

Cited By ~ 45

Author(s):

Andreas C. Nearchou

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Mobile Robot ◽

Optimal Path ◽

Hill Climbing ◽

Planning Problem ◽

Well Performance ◽

Simulation Results ◽

Task Oriented ◽

Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

Download Full-text

The Power Optimization Control of Shearer Based on ANFIS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.496-500.1727 ◽

2014 ◽

Vol 496-500 ◽

pp. 1727-1731

Author(s):

Yuan Hua Zhou ◽

Hong Wei Ma ◽

Hai Yan Wu ◽

You Jun Zhao

Keyword(s):

Real Time ◽

Power Optimization ◽

Optimization Control ◽

Optimization Simulation ◽

Novel Method ◽

Simulation Results

Aiming at the power optimization control of shearer, a novel method by ANFIS was proposed to control the power of shearer. Firstly, the method based ANFIS was used to evaluate the current power state of the shearer, and then ANN MPC model was used to control the speed of shearer. By adjusting the speed of shearer in real time, it was to achieve the purpose of power optimization. Simulation results in Mat lab shown that the effectiveness and real-time of the method which was proposed in this thesis was verified.

Download Full-text

Mixture Normal Distribution for Gibbs Sampler and its Application in the Surface of Single Crystal

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.529.585 ◽

2012 ◽

Vol 529 ◽

pp. 585-589

Author(s):

Wei Shao ◽

Guo Qing Zhao ◽

Yu Jie Gai

Keyword(s):

Single Crystal ◽

Normal Distribution ◽

Gibbs Sampler ◽

Target Distribution ◽

Efficient Sampling ◽

Full Conditional Distribution ◽

Sampling Algorithms ◽

Normal Method ◽

Hit And Run ◽

Simulation Results

Gibbs sampler is widely used in Bayesian analysis. But it is often difficult to sample from the full conditional distribution, and this hardly weakens the efficiency of Gibbs sampler. In this paper, we propose to use mixture normal distribution for Gibbs sampler. The mixture normal distribution can approximate the target distribution. So carrying more information from target distribution, the mixture normal distribution tremendously improves the efficiency of Gibbs sampler. Further more, combining with mixture normal method, Hit-and-Run algorithm can also get more efficient sampling results. Simulation results show that Gibbs sampler with mixture normal distribution outperforms other sampling algorithms. The Gibbs sampler with mixture normal distribution can also be applied to explorer the surface of single crystal.

Download Full-text