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An improved termite life cycle optimizer algorithm for global function optimization.

Yanjiao Wang1, Mengjiao Wei1

  • 1School of Electrical Engineering, Northeast Electric Power University, Jilin, Jilin, China.

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Summary
This summary is machine-generated.

An improved termite life cycle optimizer algorithm (ITLCO) enhances convergence speed and accuracy. Novel strategies for worker and soldier generation, plus a replacement mechanism, balance population diversity and prevent local optima for better optimization performance.

Keywords:
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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Meta-heuristic algorithms are crucial for solving complex optimization problems.
  • The termite life cycle optimizer algorithm (TLCO) is a bionic algorithm inspired by termite behavior.
  • Existing TLCO versions face challenges in balancing convergence speed and population diversity, risking local optima.

Purpose of the Study:

  • To introduce an improved termite life cycle optimizer algorithm (ITLCO).
  • To enhance the convergence speed and accuracy of the TLCO algorithm.
  • To address limitations in balancing population diversity and convergence to avoid local optima.

Main Methods:

  • Developed a novel worker generation strategy to improve communication and balance convergence/diversity.
  • Introduced a soldier generation strategy with an evolutionary step factor to boost convergence speed.
  • Implemented a replacement update mechanism for lower-quality individuals to maintain population diversity.

Main Results:

  • ITLCO demonstrated superior performance on CEC2013, CEC2019, and CEC2020 benchmark test functions.
  • The improved algorithm showed significant gains in convergence speed and accuracy.
  • ITLCO exhibited enhanced stability compared to the basic TLCO and four other leading meta-heuristic algorithms.

Conclusions:

  • The proposed ITLCO effectively improves upon the original TLCO algorithm.
  • ITLCO offers a better balance between convergence and diversity, mitigating the risk of local optima.
  • The enhanced strategies contribute to faster, more accurate, and stable optimization outcomes.