Cloud Buster Optimization (CBO) algorithm

sphere function is tested

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)