GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Authors: Yang, Wanjun, Sun, Zengwu
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Analysis; Control algorithms; Flow mapping; Foraging behavior; Genetic algorithms; Geographical locations; Global Positioning System; Global positioning systems; GPS; Mathematical optimization; Methods; Optimization; Optimization algorithms; Population; Predation; Random variables; Route planning; Swarm intelligence; Traffic flow; Traffic planning
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2021/9972701

GPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA) is a new swarm intelligence optimization algorithm (SIOA) and easy to implement and has other characteristics; more and more scholars have continuously improved it and applied it to more fields. Aiming at the fact that FPA leads to the local optimal value in cross-pollination, the chaotic mapping strategy is proposed to optimize related issues that the population is not rich enough in the self-pollination process. The improved flower pollination algorithm has better advantages in testing function convergence and geographic location prediction effect.