752 publications from this institution
In many design applications, designers often have to find the best geometrical configurations so as to achieve certain objectives with the minimum amount of materials used. Such shape or topology optimization problems are usually much harder to solve than nonlinear optimization problems in a fixed domain. In this paper, we use the recently developed bat algorithm to solve topology optimization problems. Results show that the distribution of different topological characteristics such as materials can be achieved efficiently. We have also tested the bat algorithm by solving nonlinear design benchmarks. Results suggest that bat algorithm is very efficient for solving nonlinear global optimization problems as well as topology optimization.
In modelling sediment compaction and mineral reactions, the rheological behaviour of sediments is typically considered as poroelastic or purely viscous. In fact, compaction due to pressure solution and mechanical processes in porous media is far more complicated. A generalised model of viscoelastic compaction and the smectite to illite mineral reaction in hydrocarbon basins is presented. A one-step dehydration model of the mineral reaction is assumed. The obtained non-linear governing equations are solved numerically and different combinations of physical parameters are used to simulate realistic situations in typical sedimentary basins. Comparison of numerical simulations with real data has shown very good agreement with respect to both the porosity profile and the mineral reaction.
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Compactional flow and temperature evolution in porous media is modelled as a two-phase deformation process. The mathematical model leads to a pair of nonlinear diffusion equations with a moving boundary. Two distinguished features of density-driven compaction and temperature profile are then analysed and different styles of behaviour are compared and shown in this paper.
This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT.
Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic tumor and one of the most malignant tumors worldwide. Circulating tumor DNA (ctDNA) has significant diagnostic and prognostic value for cancer patients.
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The cross-ambiguity function (CAF) is commonly used to analyze the delay-Doppler characteristics of signals in radar, sonar, and communication systems. Accordingly, a CAF relates to the correlation processing of signals in the presence of delays and Doppler shifts. In this paper, we use a metaheuristic approach to address the CAF synthesis problem by jointly designing a pair of waveforms. The CAF ofwaveforms designed in this way, approximates a desired pre-defined CAF. It turns out that the waveforms designed by this approach have the benefit of low peak-to-average power ratios. Numerical examples are presented to show that nature-inspired metaheuristic algorithms can be used as an effective tool to synthesize different types of CAFs.
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
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The influence of cross coupling terms in double porosity models for fractured reservoirs has been studied in this paper using numerical and perturbation methods. Attention is focused on the influence of the cross coupling coefficients on the transient solutions for the matrix and fissure pressures. Numerical simulations show that the addition of the cross coupling terms is equivalent to reducing the compressibility of the matrix block. A simple approximate solution is proposed for a quick estimation of the significance of the cross coupling effect in reservoir simulation, based on the solution of an uncoupled model.
Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution strategies. Besides showing very promising results of the standard benchmark functions, this hybridization also significantly improves the original bat algorithm.
