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$\in${10,20} over 30 independent runs, comparing against PSO, CSO, CLPSO, SHADE, L-SHADE and CMA-ES. FCPO achieves the lowest mean runtime across the ten benchmark cases (average 0.183 s), about 2.3x faster than CMA-ES and 2.6x faster than L-SHADE in our Python implementation. On the multimodal composition function F10 at D=20, FCPO attains the best mean objective (9.625x 10^2 $\pm$ 1.275x 10^3) and remains faster than CMA-ES (0.602 s vs. 1.126 s mean runtime). 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 ~ 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.

翻译：在心脏数字孪生等昂贵黑箱优化场景中，如何设计在严格评估预算下仍保持有效性的优化器至关重要。本文提出疯狂灵感启发式粒子优化算法（FCPO），这是一种将类粒子群优化动力学与显式状态马尔可夫切换控制器相结合的混合群智能方法，可在线上调度勘探与精炼算子。FCPO集成了以下四个核心机制：（i）状态条件约束运动；（ii）用于跳出停滞状态的精英差分全局跳跃算子；（iii）基于精英协方差的本征空间引导局部精炼策略；（iv）线性种群规模缩减机制以控制后期计算成本。我们选取进化计算大会（CEC）2022基准测试套件中五个代表性函数（F1、F2、F3、F6、F10）在维度D∈{10,20}上进行了30次独立运行实验，并将FCPO与PSO、CSO、CLPSO、SHADE、L-SHADE及CMA-ES算法进行对比。在十个基准案例中，FCPO实现了最低平均运行时间（0.183秒），其Python实现版本较CMA-ES快约2.3倍，较L-SHADE快2.6倍。在维度D=20的多模态复合函数F10测试中，FCPO获得了最优平均目标值（9.625×10^2 ± 1.275×10^3），且运行速度仍优于CMA-ES（平均运行时间0.602秒对比1.126秒）。在结构化地貌函数（F1-F3）及混合函数（F6）测试中，CMA-ES保持最高精度，而FCPO显著提升了经典群智能算法的表现，实现了精度与运行时间之间的有利权衡。最后，在心室激活数字孪生标定任务中，FCPO能够在大约40次迭代内达到目标心电图重构保真度（均方根误差<0.1毫伏），生成生理可信的激活图，并在多次重复初始化中保持稳健收敛，证实其作为昂贵逆问题实用性优化器的价值。