Cost, performance, and penalties are the key factors to revenue generation and customer satisfaction. They have a complex correlation, that gets more complicated when missing a proper framework that ...unambiguously defines these factors. Service-level agreement (SLA) is the initial document discussing selected parameters as a precondition to business initialisation. The clear definition and application of the SLA is of paramount importance as for modern as a Service online businesses no direct communication between provider and consumer is expected. For the proper implementation of SLA, there should be a satisfactory approach for measuring and monitoring quality of service metrics. This study investigated these issues and proposed performance-based SLA (PerSLA) framework for cost, performance, penalties and revenue optimisation. PerSLA optimises these parameters and maximises both provider revenue and customers satisfaction. Simulation results confirm that the proposed framework is adequate in revenue generation and customers satisfaction. Customers and providers monitor the business with respect to agreed terms and conditions. On violation, the provider is penalised. This agreement increases the trust in relationship between provider and consumer.
Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many ...research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes.
Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy.
This paper presents a new way of selecting an initialisation for the
k
-modes algorithm that allows for a notion of game theoretic fairness that classic initialisations, namely those by Huang and ...Cao, do not. Our new method utilises the hospital-resident assignment problem to find the set of initial cluster centroids which we compare with two classical initialisation methods for
k
-modes: the original presented by Huang and the next most popular method of Cao and co-authors. To highlight the merits of our proposed method, two stages of analysis are presented. It is demonstrated that the proposed method is often able to offer computational speed-up of the order of
50
%
. Improved clustering, in terms of a commonly used cost-function, was witnessed in several cases and can be of the order of
10
%
, particularly for more complex datasets.
We propose an initialisation scheme for weak tropical cyclones (TCs) in the South China Sea (SCS) by combining composite and analysis vortices to improve forecasts of weak TCs in the region. The ...composite vortex is obtained from good samples of analyses of TCs in the SCS, and the analysis vortex is obtained from either European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis data or an operational assimilation system. The new scheme has been applied to 57 cases of weak TCs, and the effect on the forecasting of four tropical storms has been studied in detail. It can be concluded that the proposed scheme can reduce track errors and intensity errors of weak TCs, compared with the control experiment. The possible reasons for these results are investigated in a case study for Linfa. Results of batch experiments show that the new method significantly improves TC track forecasting of tropical storms, severe tropical storms and typhoons, and demonstrates significant advantages in forecasting intensity of tropical storms, compared with the control experiment. It also performs better in predicting intensity of TCs in the SCS compared with ECMWF Atmospheric Model high resolution.
The authors propose an initialisation scheme for weak tropical cyclones (TCs) in the South China Sea (SCS) to improve the track and intensity forecasting of numerical models. It also performs better in predicting intensity of TCs in the SCS compared with ECMWF Atmospheric Model high resolution (HRES).
While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic frontends learnt jointly ...with a backend. In this study, we focus specifically on learnable filterbanks. Prior studies have reported that in frontends using learnable filterbanks initialised to a mel scale, the learned filters do not differ substantially from their initialisation. Using a Gabor-based filterbank, we investigate the sensitivity of a learnable filterbank to its initialisation using several initialisation strategies on two audio tasks: voice activity detection and bird species identification. We use the Jensen-Shannon Distance and analysis of the learned filters before and after training. We show that although performance is overall improved, the filterbanks exhibit strong sensitivity to their initialisation strategy. The limited movement from initialised values suggests that alternate optimisation strategies may allow a learnable frontend to reach better overall performance.
Segmentation of the left and right ventricles in cardiac MRI (Magnetic Resonance Imaging) is a prerequisite step for evaluating global and regional cardiac function. This work presents a novel and ...robust schema for MRI segmentation by combining the advantages of deep learning localization and 3D-ASM (3D Active Shape Model) restriction without any user interaction. Three fundamental techniques are exploited: (1) manual 2D contours are used to build distance maps to get 3D ground truth shape, (2) derived right ventricle points are employed to rotate the coarse initial shape for a refined bi-ventricle initial estimation, (3) segmentation results from deep learning are utilised to build distance maps for the 3D-ASM matching process to help image intensity modelling. The datasets used for experimenting the cine MRI data are 1000 cases from UK Biobank, 500 subjects are selected to train CNN (Convolution Neural Network) parameters, and the remaining 500 cases are adopted for validation. Specifically, cases are used to rebuild point distribution and image intensity models, and also utilized to train CNN. In addition, the left 500 cases are used to perform the validation experiments. For the segmentation of the RV (Right Ventricle) endocardial contour, LV (Left Ventricle) endo- and epicardial contours, overlap, Jaccard similarity index, Point-to-surface errors and cardiac functional parameters are calculated. Experimental results show that the proposed method has advantages over the previous approaches.
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•Manual 2D contours are used to build distance maps to get 3D ground truth shape.•Right ventricle points from manual and deep learning are employed to rotate the coarse initial shape for a refined bi-ventricle initial estimate.•Segmentation results from deep learning are utilized to build distance maps for the 3D-ASM matching process.
Band selection is an effective way to reduce the size of hyperspectral data and to overcome the “curse of
dimensionality” in ground object classification. This paper presents a band selection ...approach based on modified Cuckoo
Search (CS) optimisation with correlation-based initialisation. CS is a popular metaheuristic algorithm with efficient
optimisation capabilities for band selection. However, it can easily fall into local optimum solutions. To avoid falling into a
local optimum, an initialisation strategy based on correlation is adopted instead of random initialisation to initiate the location
of nests. Experimental results with Indian Pines, Salinas and Pavia University datasets show that the proposed approach
obtains overall accuracy of 82.83 %, 94.83 % and 91.79 %, respectively, which is higher than the original CS algorithm,
Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Gray Wolf Optimisation (GWO).
This study presents a novel approach to detect the coastline from single-polarisation synthetic aperture radar (SAR) images. The proposed method encompasses land/sea segmentation, coastline ...detection, and refinement. A novel spectral–textural segmentation framework (STSF) is proposed by using the spectral–textural features extracted from the input image patches. The STSF distinguishes various coastal/sea types and is robust to noise. Also, a hierarchical region-based level set method (LSM) is proposed to detect the coastline, accurately. The first LSM step applies global information for evolution. The LSM initialisation is performed using the obtained rough segmentation, which is very practical as the final LSM evolution depends on the initial value, particularly on complex SAR images. The global region-based LSM (GRB-LSM) step modifies the previous segmentation and approaches to the coastline. To improve accuracy, a local region-based LSM (LRB-LSM) is proposed. Therefore, in the second LSM step, the LRB-LSM applies to the results of GRB-LSM step. The LRB-LSM improves the accuracy of the detected coastline while ensuring its smoothness. To verify the performance of the proposed method, several high-resolution SAR images from different microwave bands and various coastal environments are used. The performance of the proposed method is confirmed by the given experiments.
Phasor measurement units (PMUs) are essential tools for monitoring, protection and control of power systems. The optimal PMU placement (OPP) problem refers to the determination of the minimal number ...of PMUs and their corresponding locations in order to achieve full network observability. This paper introduces a recursive Tabu search (RTS) method to solve the OPP problem. More specifically, the traditional Tabu search (TS) metaheuristic algorithm is executed multiple times, while in the initialisation of each TS the best solution found from all previous executions is used. The proposed RTS is found to be the best among three alternative TS initialisation schemes, in regard to the impact on the success rate of the algorithm. A numerical method is proposed for checking network observability, unlike most existing metaheuristic OPP methods, which are based on topological observability methods. The proposed RTS method is tested on the IEEE 14, 30, 57 and 118-bus test systems, on the New England 39-bus test system and on the 2383-bus power system. The obtained results are compared with other reported PMU placement methods. The simulation results show that the proposed RTS method finds the minimum number of PMUs, unlike earlier methods which may find either the same or even higher number of PMUs.
•Two dynamic optimisation methodologies are explored for beer fermentation temperature profile formulation.•The sequential strategy often cannot generate profitable manipulations without violating ...undesirable by-product concentration constraints.•The simultaneous strategy results in improved constraint fulfilment and production of superior temperature manipulation profiles.•The impact of initialisation on computed temperature manipulation profiles and process performance depends strongly on the solution strategy adopted.•The control profile discretisation level has significant influence on the temperature profile and process performance up to a point, after which its effect is negligible.
A wide range of optimisation methodologies exist for solving optimal control trajectory problems. With most approaches it is necessary to solve iteratively, starting from an initialising solution (often referred to as an initial guess). In this paper we investigate the performance of two different dynamic optimisation strategies for batch beer fermentation temperature control, using different initialisation profiles. A sequential method, Control Vector Parameterisation (CVP), is demonstrated as unable to produce solution profiles satisfying the industrially imposed undesirable by-product species concentration constraints. An alternative simultaneous method, Complete Parameterisation (CP), is employed in order to determine solutions satisfying these essential industrial production constraints. Blind initialisation guesses (isothermal profiles) have been shown to produce solution profiles not suitable for implementation on real fermentors; more promising candidate profile initialisations yield superior solutions. The use of a state-of-art NLP solver (IPOPT) with analytical first derivatives achieves remarkable solution robustness, eliminating initialisation and discretisation level dependence.
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