Frenetic Cat-inspired particle optimization: A Markov state-switching hybrid swarm optimizer with application to cardiac digital twinning

Abstract

Designing optimizers that remain effective under tight evaluation budgets is critical in expensive black-box settings such as cardiac digital twinning. We propose Frenetic Cat-inspired Particle Optimization (FCPO), a hybrid swarm method that couples particle swarm optimization-like dynamics with an explicit-state Markov switching controller to schedule exploration and refinement operators online. FCPO integrates (i) state-conditioned bounded motion, (ii) an elite-difference global jump operator to escape stagnation, (iii) eigen-space guided local refinement from elite covariance, and (iv) linear population size reduction to control late-stage computational cost. We benchmark FCPO on five representative functions from the Congress on Evolutionary Computation (CEC) 2022 suite (F1, F2, F3, F6 and F10) at dimensions $D\in \left\{\mathrm{10,20}\right\}$ over 30 independent runs, comparing against PSO, CSO, CLPSO, SHADE, L-SHADE, HO and CMA-ES. FCPO achieves the lowest mean runtime across the ten benchmark cases among the full methods (average 0.181 s). HO, CMA-ES, and L-SHADE required approximately 2.33, 2.39, and 2.56 times the mean runtime of FCPO, respectively, in our Python implementation. Across the eight full methods, the aggregate rank analysis placed CMA-ES first, followed by FCPO, L-SHADE, and HO, with average ranks of 1.90, 3.40, 3.60, and 4.10, respectively. On structured landscapes (F1–F3) and on the hybrid function (F6), CMA-ES remains the most accurate method, while FCPO substantially improves over classical swarms and maintains a favorable accuracy–runtime trade-off. Finally, in a ventricular activation digital twin calibration task, FCPO reaches the target electrocardiogram (ECG) fidelity (RMSE $<0.1$  mV) within $\sim 40$ iterations and produces physiologically plausible activation maps with robust convergence across repeated initializations, supporting its use as a practical optimizer for expensive inverse problems.