Raman Effect-Inspired Optimization Algorithm (REO)

complex objective function is tested
10 Downloads
Aktualisiert 11. Nov 2024

Lizenz anzeigen

Explanation of the Code:
  1. Initialization:
  • The algorithm initializes numPhotons potential solutions randomly within the defined bounds.
  • It evaluates the initial fitness of all solutions and identifies the best one.
  1. Scattering Events:
  • For each photon (solution), the algorithm performs a scattering event:
  • Stokes Shift (Exploration): A random, larger perturbation to explore new areas.
  • Anti-Stokes Shift (Exploitation): A smaller perturbation to refine and improve the solution locally.
  • The rand < 0.5 probability ensures a 50-50 chance between exploration and exploitation.
  1. Fitness Evaluation and Update:
  • If a newly generated solution improves the fitness, it replaces the current solution.
  • The global best solution is updated accordingly if the new solution outperforms the previous best.
  1. History and Visualization:
  • The history array records the best fitness value at each iteration for convergence analysis.
  • The final plot shows how the best fitness value evolves over the iterations.
Customization:
  • Objective Function: You can replace the example objFunction with your specific function.
  • Algorithm Parameters: Adjust numPhotons, maxIterations, lowerBound, and upperBound based on your problem's requirements.
  • Exploration and Exploitation: Modify the shiftFactor parameters to fine-tune the balance between exploration and exploitation.
Kompatibilität der MATLAB-Version
Erstellt mit R2022b
Kompatibel mit allen Versionen
Plattform-Kompatibilität
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Veröffentlicht Versionshinweise
1.0.0