Bull Taming Optimization Algorithm (BTOA)

this program minimizes sphere function

The Bull Taming Optimization Algorithm (BTOA) is a nature-inspired metaheuristic based on the traditional sport of bull taming, where tamers (search agents) attempt to control bulls (dynamic objective points). The algorithm models optimization as an interaction between exploration (bulls moving chaotically) and exploitation (tamers learning and adapting to bulls' positions) to find the best solution.Key Concepts in BTOA

1. Tamers (Candidate Solutions):

• Represent potential solutions to the optimization problem.
• Move based on their proximity to bulls.
• Update their positions using a mix of local search and random perturbations.

1. Bulls (Dynamic Targets):

• Represent guiding points in the search space.
• Move unpredictably to encourage exploration.
• Shift their positions based on the collective intelligence of the tamers.

1. Movement Strategies:

• Bulls’ Movement:
• Bulls move toward the global best region but also randomly explore new areas.
• Tamers’ Movement:
• Tamers move toward the nearest bull while incorporating a random perturbation for diversification.
• Levy flights can be introduced to enhance global search.

Algorithm Steps

1. Initialize a population of tamers and bulls randomly within the search space.
2. Evaluate the fitness of all tamers using the objective function.
3. Move bulls toward promising regions with some randomness.
4. Move tamers based on the closest bull and apply exploration mechanisms.
5. Update the best solution found so far.
6. Repeat steps 3–5 for a maximum number of iterations or until convergence.

Advantages of BTOA

✅ Strong exploration due to bull movement.

✅ Balanced exploitation as tamers refine their positions.

✅ Simple yet efficient implementation.

✅ Flexible hybridization with PSO, GA, ACO, and Levy flights for performance enhancement.

Applications of BTOA

📌 Engineering design optimization.

📌 Machine learning hyperparameter tuning.

📌 Renewable energy system optimization.

📌 Economic Load Dispatch and OPF problems.

📌 Feature selection in AI models.

Enhancements & Hybrid Variants

• Levy Flight BTOA: Improves global search capabilities.
• Hybrid BTOA-PSO: Combines social learning from PSO with BTOA's local interactions.
• BTOA with Differential Evolution (DE): Enhances robustness in complex landscapes.
• BTOA with Ant Lion Optimization: Enhances exploration with elite-based selection.

Conclusion

BTOA is a novel and adaptable optimization algorithm that effectively balances exploration and exploitation. By hybridizing it with other techniques (PSO, GA, Levy flight), it can achieve higher convergence speed and accuracy in solving complex optimization problems.