Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

Zhen Yang,Zengwu Sun

doi:10.3389/fphy.2020.00259

Abstract

With the rapid development of vehicle-mounted communication technology, GPS data is an effective method to predict the current road vehicle track based on vehicle-mounted data. GPS-oriented vehicle-mounted data position prediction method is currently a hot research work and an effective method to realize intelligent transportation. In this paper, an improvement scheme is proposed based on the problem of falling into local optimization existing in the basic algorithm of teaching and learning optimization algorithm. An interference operator is used to disturb teachers to enhance the kinetic energy of the population to jump out of local optimization. By comparing the performance of GA, PSO, TLBO and ITLBO algorithms with four test functions, the results show that ITLBO has efficient optimization effect and generalization ability. Finally, the ITLBO-ELM algorithm has the best prediction effect by comparing the vehicle GPS data and comparing the experimental algorithms.

Highlights

As a new swarm intelligence algorithm, Teaching Learning Based Optimization (TLBO) simulates the process of teachers’ teaching to students and students’ learning and the process of students’ learning from each other
Verify the experimental results of TLBO-Extreme Learning Machine (ELM) algorithm, The algorithm uses Python language to implement the server Dell T610 operating system uses Ubuntu 64 bits, 2 CPU: x5650 main frequency 2.6G with 12 cores and 24 threads, memory 64G, The algorithm is run independently on four commonly used Benchmark functions for 30 times, The maximum number of iterations is 1,000, 100 iteration times are recorded, respectively, 200,..., The average fitness value obtained 1,000 times describes the fitness value curve, The algorithms involved in the comparison include GA, PSO, TLBO and three commonly used swarm intelligence algorithms
The standard deviation of TLBO algorithm is similar to that of ITLBO algorithm, while the standard deviation of GA and PSO is larger, which shows that TLBO algorithm performs better in convergence stability

Summary

Introduction

As a new swarm intelligence algorithm, Teaching Learning Based Optimization (TLBO) simulates the process of teachers’ teaching to students and students’ learning and the process of students’ learning from each other. B. Teaching stage The best individual in the population is the teacher, and students improve their performance through the difference between the average value of teachers and classes.

Results

Conclusion