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Related Experiment Video

Updated: Sep 27, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

Particle Swarm Algorithm and Its Application in Tourism Route Design and Optimization.

Bing Lu¹, Chunlei Zhou²

¹Tourism Management, Zhengzhou Tourism College, Zhengzhou 450000, China.

Computational Intelligence and Neuroscience

|April 8, 2022

Summary

This study enhances tourism route planning by optimizing the discrete particle swarm optimization algorithm using geographic data. A novel self-balancing mechanism and multithread parallelism improve efficiency and global search capabilities for practical applications.

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Area of Science:

Computer Science
Operations Research
Geographic Information Systems

Background:

Traditional tourism route planning often relies on single-factor analysis, neglecting comprehensive optimization.
The particle swarm optimization (PSO) algorithm offers robustness and ease of implementation but requires adaptation for complex geographic routing.

Purpose of the Study:

To develop an optimized discrete particle swarm optimization (DPSO) model for practical tourism route planning using geographic coordinates.
To enhance the global search capability and efficiency of the DPSO algorithm for complex routing problems.

Main Methods:

A discrete particle swarm optimization model was developed using actual geographic data for tourism route planning.
A self-balancing mechanism was introduced to improve the algorithm's global search ability and performance.
Multithread parallelism was implemented to accelerate the algorithm's solution speed.

Main Results:

The proposed DPSO model effectively addresses practical tourism route planning challenges.
The self-balancing mechanism significantly improved the algorithm's global search performance.
Multithread parallelism enhanced the computational speed, addressing limitations in parallelization research.

Conclusions:

The enhanced DPSO algorithm provides a robust and efficient solution for complex tourism route planning.
The integration of geographic data, a self-balancing mechanism, and parallelism offers a significant advancement in the field.
This approach offers practical benefits for optimizing travel itineraries and resource management in tourism.