Use case
A Reduced Variable Neighbourhood Search for the Beam Angle Optimisation Problem
Discover how Knitro has enabled solving the challenge of optimizing radiation dose distribution by defining the ideal configuration of beams, thereby ensuring the effective targeting of cancer cells while preserving healthy tissues.

IMRT is a widely used therapy technique to treat cancer. The main goal of IMRT is to eliminate cancer cells while minimizing the damage to vulnerable organs. To this end, it is essential to identify the best possible set of beam angles, called BAC, from which to irradiate cancerous cells.

The authors propose a reduced Variable Neighbourhood Search (rVNS) algorithm that explores the search space with two different local search movements at each iteration of the algorithm. In order to improve the current BAC at each iteration, the algorithm solves the so-called FMO problem which is a complex mixed-integer nonlinear problem (MINLP)

Artelys Knitro is leveraged for the resolution of each FMO allowing the authors to determine a good and stable beam angle configuration within fewer iterations when compared to other local search algorithms proposed in the literature..

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You’re not familiar with nonlinear optimization? This tutorial will present some examples of nonlinear problems for various applications. You will discover nonlinear programming methods using the Artelys Knitro solver in a Python notebook, through different examples.

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Artelys Knitro has been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.

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The Artelys technical support team comprises Artelys’consultants (PhD-level) who are used to solving the most difficult problems and deploying enterprise-wide optimization solutions. They can advise on algorithmic or software features that may result in enhanced performance in your usage of Artelys Knitro.

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The development team works continuously to provide two releases of Artelys Knitro every year. Based on feedback, we always improve our solver to meet users’ requirements and need to solve larger models faster.

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