Crocodile optimization algorithm for solving real-world optimization problems.

Area of Science:

• Computational intelligence
• Nature-inspired algorithms
• Swarm intelligence

Background:

• Nature-inspired algorithms offer flexible and simple solutions for complex computational problems.
• Bionic algorithms, mimicking natural behaviors, are a key area in computational intelligence research.
• Crocodiles' hunting strategies provide a unique model for optimization.

Purpose of the Study:

• To introduce a novel nature-inspired algorithm, the Crocodile Optimization Algorithm (COA).
• To simulate crocodile hunting behaviors, specifically premeditation and waiting, for optimization.
• To evaluate COA's effectiveness against existing algorithms in solving optimization problems.

Main Methods:

• Developed the Crocodile Optimization Algorithm (COA) based on observed crocodile hunting strategies.
• Tested COA on 29 standard mathematical test functions.
• Applied COA to 5 real-world engineering optimization problems.

Main Results:

• COA demonstrated superior performance compared to similar variants and state-of-the-art algorithms.
• The algorithm showed advantages in solution accuracy, robustness, and convergence speed.
• Statistical tests confirmed the viability and potential applications of COA.

Conclusions:

• The Crocodile Optimization Algorithm (COA) is a highly effective new nature-inspired optimization technique.
• COA's performance validates the efficacy of mimicking crocodile hunting behaviors for computational intelligence.
• The algorithm shows significant promise for addressing challenging real-world optimization problems.