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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
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Related Experiment Video

Updated: Aug 29, 2025

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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Ladybug Beetle Optimization algorithm: application for real-world problems.

Saadat Safiri1, Amirhossein Nikoofard1

  • 1Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

The Journal of Supercomputing
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

A new Ladybug Beetle Optimization (LBO) algorithm, inspired by ladybug behavior, efficiently solves complex problems. LBO excels in benchmark functions and real-world applications like power systems and pandemic modeling.

Keywords:
Annihilated ladybugsCovid-19Economic-environmental dispatch problemLadybug Beetle Optimization algorithmPandemic predictionSearch for a warm position

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Area of Science:

  • Computational Intelligence
  • Nature-Inspired Algorithms
  • Optimization Techniques

Background:

  • Optimization algorithms are crucial for solving complex problems in science and engineering.
  • Existing algorithms face challenges in efficiency and convergence speed.
  • Nature-inspired algorithms offer novel approaches to optimization.

Purpose of the Study:

  • To introduce a novel nature-inspired optimization algorithm, the Ladybug Beetle Optimization (LBO) algorithm.
  • To evaluate the performance of LBO on a suite of benchmark functions.
  • To demonstrate the applicability of LBO to real-world engineering and modeling problems.

Main Methods:

  • The Ladybug Beetle Optimization (LBO) algorithm is proposed, mimicking ladybug foraging behavior.
  • LBO incorporates unique strategies for position updating and removal of suboptimal solutions.
  • The algorithm was tested on 78 standard benchmark functions and two real-world problems.

Main Results:

  • LBO achieved optimal values for 73.3% of benchmark functions, outperforming existing methods.
  • It was the sole algorithm to find the best solution for 20.5% of benchmark functions.
  • LBO demonstrated superior performance in Economic-Environmental Dispatch Problems and Covid-19 modeling, minimizing cost, emission, and prediction error.

Conclusions:

  • The Ladybug Beetle Optimization (LBO) algorithm is a highly effective and competitive optimization technique.
  • LBO shows significant advantages in both theoretical benchmark testing and practical problem-solving.
  • The algorithm's unique approach offers a promising advancement in computational intelligence and optimization.