Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Derivatives: Problem Solving01:26

Derivatives: Problem Solving

Temperature-Dependent Growth of Brook TroutThe growth of brook trout is closely influenced by water temperature. Experimental data demonstrate how trout weight changes over a 24-day period in response to varying water temperatures. At lower temperatures, such as 15.5 degrees Celsius, brook trout show significant weight gain. However, as the temperature increases, the amount of weight gained steadily decreases. At the highest temperature measured, 24.4 degrees Celsius, trout experience a net...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Stabilizing Lattice Oxygen to Enable Durable MnO<sub>2</sub> Electrocatalyst for Simultaneous Acidic Hydrogen Production and Biomass Valorization.

Angewandte Chemie (International ed. in English)·2025
Same author

Effect of ultrasonic burst microbubbles on microwave coagulation hemostasis in a pig model of hepatic hemorrhage.

Scientific reports·2025
Same author

Unveiling the industrial synergy optimization pathways in Beijing-Tianjin-Hebei urban agglomeration based on water-energy-carbon nexus.

Journal of environmental management·2025
Same author

Overlooked role of extracellular polymeric substances in antibiotic-resistance gene transfer within microalgae-bacteria system.

Journal of hazardous materials·2025
Same author

Relationship between the <i>CUBN</i> and the <i>MIA3</i> gene copy number variation and growth traits in different cattle breeds.

Animal biotechnology·2025
Same author

Interplay between energy metabolism and NADPH oxidase-mediated pathophysiology in cardiovascular diseases.

Frontiers in pharmacology·2025

Related Experiment Video

Updated: May 28, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Dung Beetle with Reflection Cuckoo Catfish Optimizer for Numerical Optimization and Reservoir Production

Shengnan Li1, Taiju Yin1

  • 1School of Earth Sciences, Yangtze University, Jingzhou 434023, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
Summary

The new Dung Beetle with Reflection CCO (DBRCCO) algorithm enhances metaheuristic optimization. It improves convergence and stability for complex engineering problems, outperforming existing methods in benchmark tests and reservoir optimization.

Keywords:
Cuckoo Catfish Optimizeradaptive local searchbio-inspired algorithmreflecting boundary mechanismreservoir production optimization

More Related Videos

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development
06:00

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development

Published on: March 17, 2023

Related Experiment Videos

Last Updated: May 28, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development
06:00

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development

Published on: March 17, 2023

Area of Science:

  • Engineering Optimization
  • Computational Intelligence
  • Swarm Intelligence

Background:

  • Complex engineering systems necessitate reliable metaheuristic optimization algorithms.
  • Existing swarm intelligence methods, like the Cuckoo Catfish Optimizer (CCO), face challenges such as premature convergence and inadequate boundary handling.
  • These limitations hinder their effectiveness in sophisticated optimization tasks.

Purpose of the Study:

  • To propose an improved metaheuristic algorithm, the Dung Beetle with Reflection CCO (DBRCCO), addressing the limitations of the standard CCO.
  • To enhance local exploitation and population diversity through novel mechanisms.
  • To validate the efficacy of DBRCCO on benchmark functions and a practical engineering problem.

Main Methods:

  • Integration of an adaptive local search strategy inspired by dung beetle foraging for intensified exploitation.
  • Implementation of a momentum-preserving reflecting boundary mechanism to maintain population diversity near constraints.
  • Comparative evaluation against eight contemporary metaheuristic algorithms using 29 CEC2017 benchmark functions and a reservoir production optimization problem.

Main Results:

  • DBRCCO demonstrated competitive performance, achieving a Friedman ranking of 1.5172 (p<0.05) against eight other algorithms.
  • In the reservoir production optimization, DBRCCO significantly improved the mean Net Present Value (NPV) by 12.54%.
  • DBRCCO reduced the variance in the reservoir application by over 72% compared to the standard CCO, indicating enhanced stability.

Conclusions:

  • The proposed DBRCCO algorithm effectively overcomes the premature convergence and boundary handling issues of the standard CCO.
  • DBRCCO offers a robust and efficient alternative for tackling complex engineering optimization challenges.
  • The adaptive local search and reflecting boundary mechanisms contribute to improved performance and stability.