Grid-Based Path Planner Using Multivariant Optimization Algorithm

To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.

Multivariate Optimization of Volatile Compounds Extraction in Chardonnay Wine by Headspace-Solid Phase Micro Extraction and Gas Chromatography Coupled with Tandem Mass Spectrometry

A method for optimization of extraction of volatile compounds in Chardonnay wine was developed using headspace-solid phase microextraction (HS-SPME) and gas chromatography coupled with triple quadrupole tandem mass spectrometry (GC-MS/MS). Optimization of the HS-SPME conditions, temperature (T, °C) and extra-ction time (t, minutes), was carried out using a 22 factorial central composite rota- tional design (CCRD). Total area of chromatographic peaks of nineteen compounds was monitored in order to identify the best response and the data was collected on multiple reaction monitoring (MRM) mode. The mathematical model that describes the response surface for the CCRD was validated using the analysis of variance (ANO VA) with 95% of confidence level. This model showed a lack of fit based on mean square pure error ratios for each response, in which Fcalculated was 2.23 higher than Ftabulated. Even though the models cannot be rigorously used to make quantitative predictions, the coefficients of the model, especially the linear ones, are useful for understanding systematic behaviour of the response values as a function of the factor levels. Multivariate statistical design can be used in optimization of HS-SPME extraction parameters with reduced number of experiments and can be useful in sampling method of volatile compounds of Chardonnay wines analysis by CG-MS/MS. The optimal condition achieved in this method was 30°C and 45 minutes of extraction.

Multivariate spectral estimation based on THREE

Power spectrum estimation is to use the limited length of data to estimate the power spectrum of the signal. In this paper, we study the recently proposed tunable high-resolution estimator（THREE）, which is based on the best approximation to a given spectrum, with respect to different notions of distance between power spectral densities. We propose and demonstrate a different distance for the optimization part to estimate the multivariate spectrum. Its effectiveness is tested through Matlab simulation. Simulation shows that our approach constitutes a valid estimation procedure. And we also demonstrate the superiority of the method, which is more reliable and effective compared with the standard multivariate identification techniques.