Cat Reflex Optimization (CRO): a Multi-Stage Metaheuristic for High-Dimensional and Constrained Engineering Optimization

This paper introduces the Cat Reflex Optimization (CRO) algorithm, a metaheuristic inspired by the multi-stage adaptive movement behavior of felids. The proposed algorithm addresses fundamental challenges in optimization including high dimensionality, non-convexity, and complex constraints through a biologically grounded framework that systematically balances global exploration and local exploitation. CRO is modeled on how cats navigate discontinuous environments by exploring multiple candidate positions, performing pre-transition evaluation, selecting the most promising targets, and adapting their future decisions based on previous outcomes. CRO incorporates three key innovations: a reflexive behavioral repertoire that concurrently generates diverse candidate solutions; a meta-controller architecture for intelligent candidate selection based on fitness improvement, population diversity, and proximity to the global best; and adaptive homeostasis mechanisms that preserve population vigor while mitigating premature convergence. Comprehensive experiments on the CEC2017 (30, 50, and 100 dimensions), CEC2019, and CEC2020 benchmark suites show that CRO performs competitively against ten state-of-the-art metaheuristics, with statistical significance assessed using the Wilcoxon rank-sum test and Friedman ranking. Additional evaluations on the CEC2014 and CEC2022 benchmark suites further support the effectiveness of the proposed algorithm. The applicability of CRO is also demonstrated on seven constrained engineering design problems, where it achieves competitive solution quality and stable performance across multiple statistical measures. These results demonstrate that CRO is an effective optimization framework for challenging continuous and constrained optimization problems.