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Related Experiment Video

Updated: Jul 4, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A Padé approximation and intelligent population shrinkage chicken swarm optimization algorithm for solving global

Tianbao Liu1, Yue Li1, Xiwen Qin1

  • 1School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, Jilin, China.

Mathematical Biosciences and Engineering : MBE
|February 2, 2024
PubMed
Summary
This summary is machine-generated.

A novel Chicken Swarm Optimization (CSO) algorithm, PRCPSO, enhances engineering design by integrating Padé approximation, random learning, and population reduction. This bio-inspired optimization method overcomes local optima and improves convergence for complex problems.

Keywords:
Padé approximatechicken swarm optimizationengineering optimizationintelligent population size shrinkagerandom learning mechanism

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Area of Science:

  • Computational Intelligence
  • Engineering Optimization
  • Bio-inspired Algorithms

Background:

  • Bio-inspired optimization algorithms offer competitive solutions for engineering design.
  • Traditional Chicken Swarm Optimization (CSO) risks local optima in complex problems.

Purpose of the Study:

  • To propose a novel Chicken Swarm Optimization algorithm (PRPCSO) that combines Padé approximation, random learning, and population reduction techniques.
  • To enhance the performance of CSO in addressing complex optimization challenges and preventing premature convergence.

Main Methods:

  • Incorporated Padé approximation for rapid convergence to approximate solutions using rational functions.
  • Implemented a random learning mechanism for improved local exploitation by learning from similar high-performing agents.
  • Developed an intelligent population size shrinking strategy to dynamically adjust population size and prevent premature convergence.

Main Results:

  • PRPCSO demonstrated superior performance across 23 standard test functions and six engineering problems.
  • The algorithm effectively addressed the local optimum problem inherent in traditional CSO.
  • Comparative analysis showed PRPCSO outperforming several mainstream optimization algorithms.

Conclusions:

  • PRPCSO offers a significant improvement over traditional CSO for complex engineering design problems.
  • The proposed enhancements lead to better accuracy, faster convergence, and practical utility.
  • PRPCSO exhibits substantial potential for real-world engineering applications.