Particle swarm optimisation strategies for IOL formula constant optimisation

Langenbucher, Achim; Szentmáry, Nóra; Cayless, Alan; Wendelstein, Jascha and Hoffmann, Peter (2023). Particle swarm optimisation strategies for IOL formula constant optimisation. Acta Ophthalmologica, 101(7) pp. 775–782.



To investigate particle swarm optimisation (PSO) as a modern purely data driven non‐linear iterative strategy for lens formula constant optimisation in intraocular lens power calculation.

A PSO algorithm was implemented for optimising the root mean squared formula prediction error (rmsPE, defined as achieved refraction minus predicted refraction) for the Castrop formula in a dataset of N = 888 cataractous eyes with implantation of the Hoya Vivinex hydrophobic acrylic aspheric lens. The hyperparameters were set to inertia: 0.8, accelerations c1 = c2 = 0.1. The algorithm was initialised with NP = 100 particles having random positions and velocities within the box constraints of the constant triplet parameter space C = 0.25 to 0.45, H = −0.25 to 0.25 and R = −0.25 to 0.25. The performance of the algorithm was compared to classical gradient‐based Trust‐Region‐Reflective and Interior‐Point algorithms.

The PSO algorithm showed fast and stable convergence after 37 iterations. The rmsPE reduced systematically to 0.3440 diopters (D). With further iterations the scatter of the particle positions in the swarm decreased but without further reduction of rmsPE. The final constant triplet was C/H/R = 0.2982/0.2497/0.1435. The Trust‐Region‐Reflective/Interior‐Point algorithms showed convergence after 27/17 iterations, respectively, resulting in formula constant triplets C/H/R = 0.2982/0.2496/0.1436 and 0.2982/0.2495/0.1436, both with the same rmsPE as the PSO algorithm (rmsPE = 0.3440 D).

The PSO appears to be a powerful adaptive nonlinear iteration algorithm for formula constant optimisation even in formulae with more than 1 constant. It acts independently of an analytical or numerical gradient and is in general able to search for the best solution even with multiple local minima of the target function.

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