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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Related Experiment Video

Updated: Aug 27, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Traffic flow forecasting using natural selection based hybrid Bald Eagle Search-Grey Wolf optimization algorithm.

Sivakumar R1, Angayarkanni S A2, Ramana Rao Y V3

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.

Plos One
|September 26, 2022
PubMed
Summary

This study enhances traffic prediction accuracy by optimizing Support Vector Regression (SVR) parameters using a novel Hybrid Grey Wolf Optimization-Bald Eagle Search (GWO-BES) algorithm. The hybrid approach significantly reduces prediction errors, improving traffic flow management.

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Area of Science:

  • Transportation Engineering
  • Machine Learning
  • Computational Intelligence

Background:

  • Traffic congestion poses significant challenges in modern transportation systems.
  • Accurate traffic prediction is crucial for mitigating congestion and optimizing resource allocation.
  • Machine learning algorithms, particularly Support Vector Regression (SVR), are widely used for traffic forecasting but require precise parameter tuning.

Purpose of the Study:

  • To improve the accuracy of traffic prediction models.
  • To introduce a novel hybrid optimization algorithm for tuning Support Vector Regression parameters.
  • To evaluate the performance of the proposed algorithm using real-world traffic data.

Main Methods:

  • The study proposes a Hybrid Grey Wolf Optimization-Bald Eagle Search (GWO-BES) algorithm to optimize Support Vector Regression (SVR) parameters.
  • The GWO-BES algorithm incorporates natural selection principles for enhanced search efficiency.
  • The model was validated using the Caltrans Performance Measurement System (PeMS) and Chennai city traffic datasets.

Main Results:

  • The proposed SVR-GWO-BES model demonstrated a significant improvement in error performance, reducing Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) by 48%.
  • The study observed that an increased population of search agents within the GWO-BES algorithm positively correlates with improved prediction performance.
  • The hybrid optimization approach proved effective in enhancing the accuracy of traffic forecasting.

Conclusions:

  • The Hybrid Grey Wolf Optimization-Bald Eagle Search algorithm offers a superior method for tuning SVR parameters in traffic prediction applications.
  • The enhanced accuracy achieved by SVR-GWO-BES can lead to more efficient traffic management strategies.
  • Further research into increasing the population of search agents could yield even greater performance gains in traffic forecasting.