Raman Effect-Inspired Optimization Algorithm (REO)

complex objective function is tested

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.