Wisdom-based Decision Making Optimization (WDMO)

this algorithm is based on wisdom and decision making

Key Features:

• Exploration vs. Exploitation: The balance between exploration and exploitation allows the algorithm to explore new areas of the solution space while refining the best solutions found so far.
• Feedback Mechanism: The feedback from past experiences (personal bests) helps the particles adapt and improve over time, making decisions more wisely.
• Adaptive Nature: The algorithm dynamically adjusts its behavior, using both randomness and learned information to find the optimal solution.

Applications:

This optimization approach is suitable for:

• Global Optimization: Problems where finding the global minimum or maximum is difficult.
• Multi-objective Optimization: Problems involving multiple conflicting objectives.
• Machine Learning Hyperparameter Tuning: Optimizing hyperparameters using both exploration and exploitation strategies.