83 research outputs found

    Tomographic reconstruction with search space expansion

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    A search space expansion process is proposed in the context of tomographic reconstruction (TR). The idea is to widen the effective search space in a series of increasing sizes with clamping on the search space boundary. The technique was tested on four simple phantoms and on the clinically important Shepp-Logan phantom. Dispersive flies optimisation (DFO), a lightweight particle swarm optimisation (PSO) variant, is shown to produce lower reproduction errors compared to standard TR toolbox algorithms. The expansion technique demonstrably decreases salt-and-pepper noise. DFO with 50 subspace searches was found to be superior to differential evolution, PSO and, more importantly, a number of conventional reconstruction techniques. To the best of our knowledge, this is the first work where search space expansion, in its literal form, is introduced, discussed and applied to this problem

    Swarm optimised few-view binary tomography

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    This paper considers a swarm optimisation approach to few-view tomographic reconstruction. DFOMAX, a high diversity swarm optimiser, demonstrably reconstructs binary images to a high fidelity, outperforming a leading algebraic technique, differential evolution and particle swarm optimisation on four standard phantoms. The paper considers the effectiveness of optimisers that have been developed for optimal low dimensional performance and concludes that trial solution clamping on the walls of the feasible search space is important for good performance

    Swarm led tomographic reconstruction

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    Image reconstruction from ray projections is a common technique in medical imaging. In particular, the few-view scenario, in which the number of projections is very limited, is important for cases where the patient is vulnerable to potentially damaging radiation. This paper considers swarm-based reconstruction where individuals, or particles, swarm in image space in an attempt to lower the reconstruction error. We compare several swarm algorithms with standard algebraic reconstruction techniques and filtered backprojection for five standard test phantoms viewed under reduced projections. We find that although swarm algorithms do not produce solutions with lower reconstruction errors, they generally find more accurate reconstructions; that is, swarm techniques furnish reconstructions that are more similar to the original phantom. A function profiling method suggests that the ability of the swarm to optimise these high dimensional problems can be attributed to a broad funnel leading to complex structure close to the optima. This finding is further exploited by optimising the parameters of the best performing swarm technique, and the results are compared against three unconstrained and boxed local search methods. The tomographic reconstruction-optimised swarm technique is shown to be superior to prominent algebraic reconstructions and local search algorithms

    Evolutionary optimisation of beer organoleptic properties: a simulation framework

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    Modern computational techniques offer new perspectives for the personalisation of food properties through the optimisation of their production process. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. Furthermore, this work presents a solution discovery method that could be suitable for more complex, industrial setups. An evolutionary computation technique was used to map brewers’ desired organoleptic properties to their constrained ingredients to design novel recipes tailored for specific brews. While there exist several mathematical tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the predetermined quantities of ingredients, this work investigates an automated quantitative ingredient-selection approach. The process, which was applied to this problem for the first time, was investigated in a number of simulations by “cloning” several commercial brands with diverse properties. Additional experiments were conducted, demonstrating the system’s ability to deal with on-the-fly changes to users’ preferences during the optimisation process. The results of the experiments pave the way for the discovery of new recipes under varying preferences, therefore facilitating the personalisation and alternative high-fidelity reproduction of existing and new products

    Exploration and exploitation zones in a minimalist swarm optimiser

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    The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging

    Stochastic Diffusion Search Review

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    Abstract-Stochastic Diffusion Search (SDS), first incepted in 1989, belongs to the extended family of Swarm Intelligence algorithms. In contrast to many nature-inspired algorithms, SDS has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews the developments of this algorithm, which are shown to perform well in a variety of application domains

    Editorial

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