Raman Effect-Inspired Optimization Algorithm (REO)

版本 1.0.0 (2.5 KB) 作者: praveen kumar
complex objective function is tested
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更新时间 2024/11/11

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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.
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REO

版本 已发布 发行说明
1.0.0