Frenetic Cat-inspired Particle Optimization: a Markov state-switching hybrid swarm optimizer with application to cardiac digital twinning

2026-04-17

AI Summary

This study addresses the challenge of expensive black-box optimization under tight computational budgets, such as in cardiac digital twin calibration, where conventional methods suffer from low efficiency. The authors propose a hybrid population-based optimization algorithm that integrates particle swarm dynamics with a Markov-switching controller to adaptively schedule exploration and exploitation operators online. Key innovations include state-conditioned bounded motion, elite-differential global jumps, covariance-guided local refinement, and a linear population reduction mechanism, collectively achieving an effective exploration–exploitation balance. Evaluated on the CEC2022 benchmark suite, the method achieves an average runtime of only 0.183 seconds—2.3× faster than CMA-ES—and demonstrates robust convergence in cardiac digital twin calibration, attaining high-fidelity ECG fitting (RMSE < 0.1 mV) within just 40 iterations.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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$\in${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.

Problem

• expensive black-box optimization
• evaluation budget
• cardiac digital twinning
• swarm intelligence
• inverse problems

Innovation

• Markov state-switching
• hybrid swarm optimization
• elite-difference jump operator
• eigen-space refinement
• digital twinning