A new human-based metahurestic optimization method based on mimicking cooking training.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often struggle to balance exploration and exploitation effectively.
• A need exists for novel metaheuristic approaches with enhanced performance.

Purpose of the Study:

• To introduce a new metaheuristic algorithm, the Chef-Based Optimization Algorithm (CBOA).
• To mathematically model the cooking training process for optimization.
• To evaluate CBOA's performance in exploration and exploitation capabilities.

Main Methods:

• Developed the Chef-Based Optimization Algorithm (CBOA) inspired by culinary training.
• Modeled cooking training stages to enhance global search (exploration) and local search (exploitation).
• Tested CBOA on 52 standard objective functions and four real-world engineering problems.

Main Results:

• CBOA effectively balances exploration and exploitation, yielding acceptable solutions.
• The algorithm demonstrates high efficiency in addressing various optimization issues.
• Comparative analysis shows CBOA outperforms twelve established metaheuristic algorithms.

Conclusions:

• CBOA is a highly efficient and effective metaheuristic algorithm for optimization.
• The algorithm shows significant advantages over existing methods in benchmark and engineering tests.
• CBOA offers a promising new tool for tackling complex optimization challenges.