Vimal Optimization Algorithm

版本 1.0.0 (2.0 KB) 作者: praveen kumar
sphere function is used
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更新时间 2024/11/11

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Explanation of the Code
  1. Sphere Function: The objective function sphereFunction computes the sum of squares of the elements of x.
  2. Initialization: The algorithm generates an initial random population of solutions within the defined bounds.
  3. Purity Score: The purity score is calculated as the inverse of the sphere function value. Higher purity scores correspond to better solutions.
  4. Purification Process:
  • Mutation/Refinement: Small random perturbations are added to the purest solutions to refine them.
  • Combination: The purest solutions are blended with their neighbors to create new solutions.
  1. Convergence: The algorithm stops when a sufficiently pure (optimal) solution is found or when the maximum number of iterations is reached.
  2. Output: The purest solution and its corresponding value of the sphere function are displayed.
Features
  • Purification Concept: The algorithm focuses on refining and purifying solutions to reach optimality.
  • Combination and Mutation: These mechanisms introduce diversity and ensure the search space is well-explored.
Use Case: Sphere Function Optimization
The sphere function is a standard benchmark for testing optimization algorithms. The Vimal Optimization Algorithm showcases a novel approach inspired by the concept of purity, which can be adapted for more complex problems.
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1.0.0