scholarly journals An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1605
Author(s):  
Micaela Jara Ten Kathen ◽  
Isabel Jurado Flores ◽  
Daniel Gutiérrez Reina

Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.

2013 ◽  
Vol 634-638 ◽  
pp. 4011-4016
Author(s):  
Wei Xia ◽  
Hong Chen Pan ◽  
Xiao Ping Liao

Constructing a high-fidelity surrogate model to optimize production process is often required to meet the requirement of manufacturing process programming ,one of the most popular techniques for the construction of such a surrogate model is that of Gaussian process surrogate model. In this paper, the development of a gradient particle swarm optimization is described, which aims to reduce the cost of this likelihood optimization by drawing on an efficient adjoint of the likelihood, and improve the precision of the model. A multimodal benchmark function was used to test, show that the tuning strategy can provide an accurate Gaussian process surrogate model. Based on LHS ,Gaussian process surrogate model (GP) and gradient particle swarm optimization algorithm (GPSO), a optimization model which is used for improving the quality of Al profile welding is built and utilized to obtain optimal multi-parameters of Al alloy profile extruding processes and moulds. Optimal solution is validated by experiment.


2019 ◽  
Vol 8 (3) ◽  
pp. 108-122 ◽  
Author(s):  
Halima Salah ◽  
Mohamed Nemissi ◽  
Hamid Seridi ◽  
Herman Akdag

Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.


MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 540-548 ◽  
Author(s):  
Roya Peirovi Minaee ◽  
Mojtaba Afsharnia ◽  
Alireza Moghaddam ◽  
Ali Asghar Ebrahimi ◽  
Mohsen Askarishahi ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document