Felis Catus Optimization (FCO): A novel nature‑inspired metaheuristic algorithm - Mohammad Salehi; Raouf Khayami; Mirpouya Mirmozaffari (PLoS ONE, 2026)
This study introduces Felis Catus Optimization (FCO), a novel nature‑inspired metaheuristic algorithm modeled on the ecological and adaptive behavioral dynamics of urban domestic cats. FCO divides its population into explorer (male) and exploiter (female) agents to maintain a dynamic equilibrium between global search and local refinement. Male agents perform asynchronous triplet movements governed by adaptive exploration scaling, while female agents execute Gaussian‑based local exploitation and cooperative litter burst. A rejuvenation‑and‑noise ecological cycle replaces explicit renewal events, sustaining diversity and preventing stagnation through random reallocation and mild environmental perturbation. These mechanisms collectively achieve continuous exploration using direct position-update rules. Extensive experiments on CEC 2005 and CEC 2017 benchmarks confirmed FCO’s competitive behavior ranking among top optimizers and outperforming seven algorithms significantly under Holm’s post‑hoc procedure (p < 0.05). The critical‑difference (CD) analysis positioned FCO in the central, statistically equivalent cluster, validating its robust convergence pattern. Applications to three real‑world engineering design problems demonstrated consistent near‑optimal performance and low result variance. Overall, FCO exhibits stable convergence, reliable population renewal, and strong resilience against premature stagnation, establishing it as a scalable and dependable optimizer for continuous and constrained engineering problems.