Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm

Delavar

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2015

ABSTRACT:Being one of the natural disasters, earthquake can seriously damage buildings, urban facilities and cause road blockage. Postearthquake route planning is problem that has been addressed in frequent researches. The main aim of this research is to present a route planning model for after earthquake. It is assumed in this research that no damage data is available. The presented model tries to find the optimum route based on a number of contributing factors which mainly indicate the length, width and safety of the road. The safety of the road is represented by a number of criteria such as distance to faults, percentage of non-standard buildings and percentage of high buildings around the route. An integration of genetic algorithm and ordered weighted averaging operator is employed in the model. The former searches the problem space among all alternatives, while the latter aggregates the scores of road segments to compute an overall score for each alternative. Ordered weighted averaging operator enables the users of the system to evaluate the alternative routes based on their decision strategy. Based on the proposed model, an optimistic user tries to find the shortest path between the two points, whereas a pessimistic user tends to pay more attention to safety parameters even if it enforces a longer route. The results depicts that decision strategy can considerably alter the optimum route. Moreover, post-earthquake route planning is a function of not only the length of the route but also the probability of the road blockage.

Section: Mutationmentioning

confidence: 99%

A Gis-Based Model for Post-Earthquake Personalized Route Planning Using the Integration of Evolutionary Algorithm and Owa

Delavar

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2015

“…It can make dynamic path planning, but the shortest path is still influenced by the density of grid [17]. In addition, AC needs grid to compute the matrix of pheromone concentration [18] and GA needs grid to make genetic operation [19]. Both of the methods rely on infinite iterations to approach a theoretical optimum.…”

Section: Introductionmentioning

confidence: 99%

A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment

Xiao

Meng

et al. 2018

Intel Serv Robotics

This paper presents a geometrical path planning method, and it can help unmanned aerial vehicle to find a collision-free path in two-dimensional and three-dimensional (2D and 3D) complex environment quickly. First, a list of tree is designed to describe obstacles, and it is used to query the obstacles which block the line from starting point to finishing point (blocking obstacle). Specially, the list also stores the edge information of blocking obstacle. For the obstacles with short distance, a reasonable way to fly over is studied. Then, a shortest path planning method based on geometrical computation is proposed according to different shapes of obstacles. The obstacles are convex and divided into two cases of 2D and 3D. 2D environment includes rectangular obstacle, trapezoidal obstacle, triangular obstacle, circular obstacle and elliptic obstacle. In 3D, it includes cuboid, sphere and ellipsoid. To compare with other methods, the simulation is made in different environments. In 2D environment with circular obstacles, the method is similar to the artificial potential field. In 2D environment with rectangular obstacles, the performance of the proposed method is better than A-star. Compared with genetic algorithm, the proposed method gives a better result in 3D environment with cuboid obstacles. In 3D environment with hybrid obstacles, it is similar to interfered fluid dynamical system. Through comprehensive comparison and analysis, the conclusion is that the method has good adaptability and does not require grid modeling. It can find a shorter path in 2D/3D complex environment within a short time, so it has the ability of real-time path planning. Keywords Unmanned aerial vehicle (UAV) • Path planning • Geometrical shortest path • 2D/3D complex environment • Convex obstacles • Optimal and real-time planning B Xiao Liang

International Journal of Agricultural and Biological Engineering

“…NSGA-II with a low computational complexity, effective elite strategy and the unnecessity to specify the sharing radius, has become one of the main methods for the multi-objective genetic algorithm and has been successfully applied to solve a variety of complex engineering optimization problems [26] . In view of this, the NSGA-II algorithm is chosen to optimize the parameters of the boom suspension.…”

Section: The Optimization Algorithm Of Nsga-iimentioning

confidence: 99%

Design optimization and test for a pendulum suspension of the crop sprayer boom in dynamic conditions based on a six DOF motion simulator

Cui

Mao

Xue

et al. 2018

Spray deposit distribution from a field sprayer is mainly affected by the boom movements when the tractor is driven over a rough soil surface, the pendulum suspension that used to reduce and control the movement of spray boom by isolating the boom from vibrations of the tractor will directly enhance uniform deposition of chemicals. However, how to match the parameters of the suspension with the properties of the boom is the key problem. The dynamic rigid-flexible coupling model of the virtual prototype of the spray boom suspension system was established by using ADAMS and ABAQUS software. An optimization of the suspension parameters for a large spay boom was carried out based on the optimal Latin hypercube design, radial basis function neural network, and multi-objective genetic algorithm NSGA-II. After modified parameters of the suspension, the travel of the sprayer on a typical field motion track was simulated based on a six DOF motion simulator, and the dynamic behavior of the boom suspension was measured. The results show that RMS of the measured boom roll angle and the boom center displacement for optimized solution were reduced by 14.76% and 12.43% compared with the original suspension. Finally, the inertial measurement unit (IMU) was used to measure the movements of the sprayer vehicle during the pesticide application on the Hongze Lake Farm, the experiment of field condition reproduced by using the six DOF motion simulator, the standard deviation of the roll angle and vibration displacement for the optimized sprayer boom are only 0.6382° and 62.279 mm respectively. The research provides theoretical basis and experimental method for parameter optimization of large scale boom suspension.