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Related Experiment Video

Updated: May 31, 2025

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Crisscross Moss Growth Optimization: An Enhanced Bio-Inspired Algorithm for Global Production and Optimization.

Tong Yue1, Tao Li1

  • 1School of Geosciences, Yangtze University, Wuhan 430100, China.

Biomimetics (Basel, Switzerland)
|January 24, 2025
PubMed
Summary

The new crisscross moss growth optimization (CCMGO) algorithm enhances global optimization by improving information exchange and balancing exploration. CCMGO outperforms existing methods on benchmark tests and real-world reservoir production problems.

Keywords:
bionic algorithmcrisscrossglobal optimizationmetaheuristicmoss growth optimizationreservoir production optimization

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Biomimetic computing

Background:

  • Global optimization problems are common in science and engineering, requiring efficient search algorithms.
  • The original moss growth optimization (MGO) algorithm, inspired by moss colonies, faces challenges like premature convergence.
  • Enhanced algorithms are needed to improve exploration and exploitation in complex search spaces.

Purpose of the Study:

  • To introduce an enhanced bio-inspired algorithm, crisscross moss growth optimization (CCMGO).
  • To improve upon the moss growth optimization algorithm by addressing its limitations.
  • To evaluate CCMGO's performance on benchmark functions and a real-world application.

Main Methods:

  • Developed CCMGO by incorporating a crisscross (CC) strategy and a dynamic grouping parameter.
  • Mimicked moss's interwoven growth for better information exchange and spore dispersal for diversity.
  • Emulated adaptive resource allocation for balancing exploration and exploitation.

Main Results:

  • CCMGO demonstrated superior performance against nine established metaheuristic algorithms on the CEC2017 benchmark suite.
  • The algorithm achieved significantly higher net present value (NPV) in a three-channel reservoir production optimization problem.
  • Experimental results confirm CCMGO's effectiveness in diverse and complex optimization tasks.

Conclusions:

  • CCMGO offers a robust and adaptable solution for complex global optimization challenges.
  • The enhanced algorithm shows significant potential for real-world applications, particularly in resource management.
  • CCMGO represents an advancement in nature-inspired optimization techniques.