Mobile adaptive crowd optimization scheme based on distance field

Ya-Chun Tang,Xiao-Bo Guo,Xiang-Dong Yin

doi:10.1186/s13639-016-0061-7

Abstract

For complexity and efficiency of the multi-objective optimization, we proposed the mobile distance field-driven adaptive crowd optimization algorithm. In space, we modify the surface parameters based on the corresponding changes of the distance field to obtain the moving target’s moving track and moving surface. When the curve of the moving track is changed, the x axis and the y axis of the moving track are adjusted adaptively. In this paper, the moving process is divided into three processes: the target dynamic crowd control, the crowd model algorithm, and the predictive control of linear time domain based on the moving target prediction and crowd control algorithm. Then, the multi-objective optimization algorithm of moving objects is proposed by using the crowd model to predict the status and the position of the target. The experimental results show the high accuracy, low complexity, and high efficiency of the proposed optimization algorithm.

Highlights

Multi-objective optimization can be applied to a large number of practical applications [1]
The novel algorithm was presented in the article [9] for the removal of reflections generated by objects on reflecting floors
In view of the above achievements and problems, based on the multi-objective optimization model, we proposed adaptive mobile crowd optimization based on distance field

Summary

Introduction

Multi-objective optimization can be applied to a large number of practical applications [1]. The search of a compromise between multiple characteristics [5] becomes the key to the development of multi-objective optimization applications. In article [8], a multi-objective particle crowd optimization technique is applied to a group of consecutive frames to reduce the number of branches in each tracking tree. The author of article [12] proposed a repeated use of screened Poisson to compute a part coding and extracting distance field. A highly scalable method was proposed in article [13] for computing 3D distance fields on massively parallel distributed-memory machines. In view of the above achievements and problems, based on the multi-objective optimization model, we proposed adaptive mobile crowd optimization based on distance field.

Moving target distance field analysis model

Adaptive mobile crowd optimization

Adaptive multi-objective optimization

Conclusions