Cloud Buster Optimization (CBO) algorithm

sphere function is tested
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Updated 30 Nov 2024

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The Cloud Buster Optimization (CBO) technique is a novel optimization algorithm inspired by natural processes involving clouds, weather systems, or air circulation. Though it may not yet be an officially established algorithm, such a technique would likely draw inspiration from meteorological phenomena and cloud behaviors, such as:Key Concepts (Hypothetical Framework):
  1. Cloud Formation & Dissipation:
  • Solutions (particles) represent clouds that gather around optimal regions (low-pressure systems).
  • Poor solutions dissipate like clouds in unfavorable weather conditions.
  1. Wind Patterns & Convergence:
  • Solutions are influenced by directional changes (wind flow) to explore new regions.
  • Convergence occurs when multiple solutions (clouds) merge around an optimal solution (storm center).
  1. Rainfall as Objective Evaluation:
  • When clouds reach saturation (a threshold), they "rain," indicating evaluation of the objective function.
  • Heavier rainfall (better evaluation) signifies closer proximity to the optimal solution.
Algorithm Outline (Hypothetical Steps):
  1. Initialization:
  • Generate an initial population of solutions representing cloud clusters.
  • Assign random positions and velocities.
  1. Evaluation:
  • Calculate the fitness of each solution (e.g., rainfall intensity).
  1. Movement and Update:
  • Solutions move based on wind directions (random or adaptive) and cloud density (diversity in the population).
  1. Convergence:
  • When clouds merge into storms (converge near better solutions), perform local search for refinement.
  1. Dissipation:
  • Solutions with poor fitness are removed or repositioned, similar to dissipating clouds.
  1. Termination:
  • Repeat until convergence or a specified number of iterations.
Possible Applications:
  • Weather Forecasting Models
  • Supply Chain Optimization
  • Environmental Resource Management
  • Energy Systems (Solar, Wind)
MATLAB Release Compatibility
Created with R2022b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.0.0