The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Optimization problems are prevalent in science and engineering.
• Existing algorithms often struggle with large-scale and complex optimization tasks.
• Nature-inspired algorithms offer promising alternatives for complex problem-solving.

Purpose of the Study:

• To introduce a novel nature-inspired algorithm, the cheetah optimizer (CO).
• To enhance population diversification, convergence, and robustness in optimization.
• To evaluate CO's effectiveness on benchmark and engineering problems.

Main Methods:

• The cheetah optimizer (CO) algorithm is developed, mimicking cheetah hunting strategies.
• Incorporation of 'leave the prey and go back home' strategy for improved diversification.
• Extensive testing on CEC-2005, CEC-2010, and CEC-2013 benchmark functions.
• Application to the economic load dispatch problem.

Main Results:

• The CO algorithm significantly outperformed state-of-the-art and conventional algorithms.
• CO demonstrated robust performance on shifted-rotated, large-scale, and complex engineering problems.
• Superior convergence and robustness were observed compared to existing methods.

Conclusions:

• The cheetah optimizer (CO) is a highly effective algorithm for challenging optimization tasks.
• CO offers a significant advantage over existing standard, improved, and hybrid algorithms.
• The algorithm shows promise for solving real-world engineering optimization problems.