Learning cooking algorithm for solving global optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Meta-heuristic Computing

Background:

• Meta-heuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often face challenges in balancing exploration and exploitation.
• Novel approaches are continuously sought to enhance problem-solving efficiency.

Purpose of the Study:

• To introduce a novel human-based meta-heuristic algorithm, the Learning Cooking Algorithm (LCA).
• To evaluate LCA's performance on diverse benchmark functions and real-world engineering problems.
• To demonstrate LCA's effectiveness in addressing complex optimization challenges.

Main Methods:

• The Learning Cooking Algorithm (LCA) was developed, inspired by human cooking learning processes.
• LCA's strategy involves two phases: children learning from mothers and collective learning from a chef.
• Performance was assessed using 51 benchmark functions (including CEC 2005 and CEC 2019) and seven engineering design problems.

Main Results:

• Statistical analyses (t-test, Wilcoxon, Friedman) confirmed LCA's effectiveness in optimization.
• LCA demonstrated a superior ability to balance exploration and exploitation.
• The algorithm outperformed state-of-the-art meta-heuristic algorithms on benchmark and engineering tasks.

Conclusions:

• The Learning Cooking Algorithm (LCA) is a capable and efficient meta-heuristic method.
• LCA shows significant potential for solving complex real-world optimization problems.
• The human-inspired approach offers a promising direction for future algorithm development.