Raman Effect-Inspired Optimization Algorithm (REO)
Version 1.0.0 (2,47 KB) von
praveen kumar
complex objective function is tested
Explanation of the Code:
- 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.
- 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.
- 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.
- 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
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REO
Version | Veröffentlicht | Versionshinweise | |
---|---|---|---|
1.0.0 |