Felis Catus Optimization (FCO): A novel nature‑inspired metaheuristic algorithm

Area of Science:

• Computational Intelligence
• Nature-Inspired Algorithms
• Optimization Techniques

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often face challenges with premature convergence and maintaining population diversity.
• Novel approaches are needed to enhance the efficiency and robustness of optimization.

Purpose of the Study:

• To introduce Felis Catus Optimization (FCO), a novel metaheuristic algorithm.
• To model FCO on the adaptive behaviors of domestic cats for dynamic equilibrium between exploration and exploitation.
• To evaluate FCO's performance on benchmark functions and real-world engineering problems.

Main Methods:

• FCO utilizes distinct male (explorer) and female (exploiter) agents with specific movement and exploitation strategies.
• A rejuvenation-and-noise ecological cycle is implemented to sustain population diversity and prevent stagnation.
• The algorithm employs direct position-update rules for continuous exploration.

Main Results:

• FCO demonstrated competitive performance, ranking among top optimizers on CEC 2005 and CEC 2017 benchmarks.
• Statistical analysis (Holm's post-hoc, Critical-Difference) confirmed FCO's significant outperformance and robust convergence.
• Applications to engineering design problems showed consistent near-optimal results with low variance.

Conclusions:

• Felis Catus Optimization (FCO) is a scalable and dependable optimizer for continuous and constrained problems.
• FCO exhibits stable convergence, effective population renewal, and resilience against premature stagnation.
• The algorithm's unique approach, inspired by feline behavior, offers a promising alternative in metaheuristic optimization.