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

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Modified prairie dog optimization algorithm for global optimization and constrained engineering problems.

Huangjing Yu¹, Yuhao Wang¹, Heming Jia¹

¹School of Information Engineering, Sanming University, Sanming 365004, China.

Mathematical Biosciences and Engineering : MBE

|December 5, 2023

Summary

This summary is machine-generated.

This study enhances the prairie dog optimization (PDO) algorithm by incorporating randomized audio signals and chaotic tent mapping. The modified PDO algorithm demonstrates improved performance in optimization tasks.

Keywords:

audio signal factor engineering design problems lens opposition-based learning strategy merit-seeking ability prairie dog optimization algorithm

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Area of Science:

Computational Intelligence
Metaheuristic Optimization
Swarm Intelligence

Background:

The prairie dog optimization (PDO) algorithm is a metaheuristic inspired by prairie dog behavior.
Existing PDO algorithms lack mechanisms to simulate complex information exchange critical for survival and resource acquisition.

Purpose of the Study:

To enhance the prairie dog optimization algorithm's performance.
To improve the algorithm's merit-seeking ability and global exploration capabilities.

Main Methods:

Introduced a randomized audio signal factor to simulate prairie dog communication for food and danger.
Incorporated chaotic tent mapping for population initialization to increase diversity.
Applied a lens opposition-based learning strategy to boost global exploration.

Main Results:

The modified PDO algorithm was tested on 23 benchmark functions, IEEE CEC2014 test functions, and six engineering design problems.
Experimental results indicate superior optimization performance compared to the original algorithm.

Conclusions:

The proposed enhancements significantly improve the prairie dog optimization algorithm's effectiveness.
The modified PDO algorithm shows strong potential for solving complex optimization problems.