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Related Experiment Video

Updated: Jun 24, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Improved marine predators algorithm for engineering design optimization problems.

Ye Chun1,2, Xu Hua3, Chen Qi4

  • 1Internet of Things Engineering College, Jiangsu Vocational College of Information Technology, Wuxi, 214001, China. yechun1979@sohu.com.

Scientific Reports
|June 6, 2024
PubMed
Summary

The Improved Marine Predator Algorithm (IMPA) enhances optimization for complex engineering designs by addressing local optima and premature convergence. This novel approach improves solution accuracy and robustness in industrial applications.

Keywords:
Complex industrial engineering design problemsImproved marine predators algorithmSelf-adaptive weightSocial strategy

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Engineering design

Background:

  • The Marine Predator Algorithm (MPA) is a population-based algorithm with limitations like local optima traps and premature convergence.
  • These limitations hinder its effectiveness in complex industrial engineering design problems.

Purpose of the Study:

  • To propose a novel Improved Marine Predator Algorithm (IMPA) to overcome the limitations of the standard MPA.
  • To enhance the algorithm's performance in solving complex optimization problems.

Main Methods:

  • Introduction of an adaptive weight adjustment strategy.
  • Integration of a dynamic social learning mechanism to improve predator-prey interactions.
  • Evaluation using benchmark functions, CEC2021 suite, and engineering design problems (welded beam, spring, pressure vessel, three-bar).

Main Results:

  • The IMPA demonstrated significant success compared to other optimization methods.
  • Achieved excellent performance in finding optimal parameter solutions and objective function values.
  • Showcased high accuracy and robustness in solving complex industrial engineering design problems.

Conclusions:

  • The IMPA effectively addresses the limitations of the standard MPA.
  • The proposed enhancements lead to superior performance in optimization accuracy and robustness.
  • IMPA is an efficient tool for tackling complex industrial engineering design challenges.