Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering ...applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.
Fixed speed induction machine‐based wind generators are widely used. Proper initialisation of the models representing fixed speed induction machine‐based wind generators connected to grid is of ...primary concern for stability analysis. The initialisation of fixed speed induction generators (FSIG) has been performed using conventional power flow techniques, but there exists a reactive power mismatch which makes initialisation imprecise. Moreover, turbine characteristics along with control mechanisms are not properly included for initialising the dynamics for stability analysis. A unified Newton–Raphson (NR) method was proposed in literature to include turbine characteristics and control methods for accurate initialisation of FSIG. However, this method creates an extra node for every induction machine causing an increase in the size of the Jacobian and time of execution of power flow algorithm. A new unified‐NR method, which does not need an extra node, for initialisation of grid‐connected FSIG is proposed in the present work. The proposed unified NR method has been validated on 14‐bus, 41‐bus, 203‐bus, and 1336‐bus test systems. The results of the proposed new unified‐NR method are compared with existing unified‐NR method. The results clearly demonstrate that the proposed method initialisation is accurate with considerably less computational time as compared to existing unified‐NR.
In this study, the authors construct two different distinguishers on Grain-v1 with 112 and 114 initialisation rounds. Their first distinguisher can distinguish Grain-v1 with 112 initialisation rounds ...from a uniform random source for 99% of the randomly chosen keys from full key space. The second one can distinguish Grain-v1 from a random source for 73% of the randomly chosen keys for one-fourth of the total key space (278 keys out of 280 keys). Our results improve upon the earlier distinguishers. The technique used for the distinguishers is conditional differential cryptanalysis. The existing works in this direction considered only one bit difference in the initialisation vector. However, for the first time, they could handle complicated conditions for the 2-bit difference to obtain better cryptanalytic results. Extending their technique by allowing the 1-bit difference in the pair of keys (i.e. related keys) and the 4-bit difference in IVs, they could observe the non-randomness till 116 initialisation rounds with a success in 62% cases.
Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative ...forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI.
•Population initialisation by DRs significantly improves the performance of GAs.•The convergence speed is largely increased by population initialisation with DR.•Population initialisation with DRs ...does not increase the run time of GAs.•The population diversity does not significantly influence the performance of the GA.
Scheduling is an important process that is present in many real world scenarios where it is essential to obtain the best possible results. The performance and execution time of algorithms that are used for solving scheduling problems are constantly improved. Although metaheuristic methods by themselves already obtain good results, many studies focus on improving their performance. One way of improvement is to generate an initial population consisting of individuals with better quality. For that purpose a variety of methods can be designed. The benefit of scheduling problems is that dispatching rules (DRs), which are simple heuristics that provide good solutions for scheduling problems in a small amount of time, can be used for that purpose. The goal of this paper is to analyse whether the performance of genetic algorithms can be improved by using such simple heuristics for initialising the starting population of the algorithm. For that purpose both manual and different kinds of automatically designed DRs were used to initialise the starting population of a genetic algorithm. In case of the manually designed DRs, all existing DRs for the unrelated machines environment were used, whereas the automatically designed DRs were generated by using genetic programming. The obtained results clearly demonstrate that using populations initialised by DRs leads to a significantly better performance of the genetic algorithm, especially when using automatically designed DRs. Furthermore, it is also evident that such a population initialisation strategy also improves the convergence speed of the algorithm, since it allows it to obtain significantly better results in the same amount of time. Additionally, the DRs have almost no influence on the execution speed of the genetic algorithm since they construct the schedule in time which is negligible when compared to the execution of the genetic algorithm. Based on the obtained results it can be concluded that initialising individuals by using DRs significantly improves both the convergence and performance of genetic algorithm, without the need of having to manually design new complicated initialisation procedures and without increasing the execution time of the genetic algorithm.
•We introduce a fast initialisation algorithm for hierarchical clustering.•It significantly reduces the number of iterations in the Ward clustering method.•We also introduce a variant of Ward more ...capable of dealing with noise in data sets.•We carry out several experiments with different noise models to demonstrate it.
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently large number of clusters. This allows the cluster merging process to start from this partition rather than from a trivial partition composed solely of singletons.
Our second contribution is an extension of the Ward and Wardp algorithms to the situation where the feature weight exponent can differ from the exponent of the Minkowski distance. This new method, called A-Wardpβ, is able to generate a much wider variety of clustering solutions. We also demonstrate that its parameters can be estimated reasonably well by using a cluster validity index.
We perform numerous experiments using data sets with two types of noise, insertion of noise features and blurring within-cluster values of some features. These experiments allow us to conclude: (i) our anomalous pattern initialisation method does indeed reduce the time a hierarchical clustering algorithm takes to complete, without negatively impacting its cluster recovery ability; (ii) A-Wardpβ provides better cluster recovery than both Ward and Wardp.
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more ...severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT-scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85% on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also, comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms.
•A two-stage pipeline for the detection of COVID-19 in CT-scan images is proposed.•A standard CNN, DenseNet, is trained as a feature extractor via transfer learning.•Feature selection is performed using the Harris Hawks Optimisation (HHO) algorithm.•HHO is combined with Simulated Annealing and Chaotic initialisation.•Evaluation on SARS-COV-2 CT-Scan Dataset gives better results than some past methods.
The economic dispatch (ED) of distributed energy resources (DERs) is an important and challenging problem in smart grid operation. Different frameworks are proposed for solving this issue, among ...which the virtual power plant (VPP) is considered as a promising means for ED in smart grids with DERs. This study presents a novel optimisation algorithm entitled sequence-based differential evolution (SDE) for solving generation dispatch among several DERs with VPPs. In the proposed method, a novel and efficient initialisation technique, namely sequence-based deterministic initialisation, is used to generate high-quality initial population. Besides, a self-adaptation mechanism is utilised to eliminate the difficulty of tuning the problem-specific control parameters of the algorithm. Subsequently, the SDE is applied for solving the ED model to obtain the optimal power generation share of the VPP. Case studies compare the solutions obtained by the proposed method with several state-of-the-art algorithms in the existing literature. It is validated the proposed method is advantageous for ED with VPPs in better solution accuracy, less computational burden, and faster convergence.
The activation function deployed in a deep neural network has great influence on the performance of the network at initialisation, which in turn has implications for training. In this paper we study ...how to avoid two problems at initialisation identified in prior works: rapid convergence of pairwise input correlations, and vanishing and exploding gradients. We prove that both these problems can be avoided by choosing an activation function possessing a sufficiently large linear region around the origin, relative to the bias variance σb2 of the network's random initialisation. We demonstrate empirically that using such activation functions leads to tangible benefits in practice, both in terms of test and training accuracy and in terms of training time. Furthermore, we observe that the shape of the nonlinear activation outside the linear region appears to have a relatively limited impact on training. Finally, our results also allow us to train networks in a new hyperparameter regime, with a much larger bias variance than has previously been possible.
Latitude on the choice of initialisation is a shared feature between one-step extended state-space and multi-step methods. The paper focuses on lattice Boltzmann schemes, which can be interpreted as ...examples of both previous categories of numerical schemes. We propose a modified equation analysis of the initialisation schemes for lattice Boltzmann methods, determined by the choice of initial data. These modified equations provide guidelines to devise and analyze the initialisation in terms of order of consistency with respect to the target Cauchy problem and time smoothness of the numerical solution. In detail, the larger the number of matched terms between modified equations for initialisation and bulk methods, the smoother the obtained numerical solution. This is particularly manifest for numerical dissipation. Starting from the constraints to achieve time smoothness, which can quickly become prohibitive for they have to take the parasitic modes into consideration, we explain how the distinct lack of observability for certain lattice Boltzmann schemes—seen as dynamical systems on a commutative ring—can yield rather simple conditions and be easily studied as far as their initialisation is concerned. This comes from the reduced number of initialisation schemes at the fully discrete level. These theoretical results are successfully assessed on several lattice Boltzmann methods.
•We study the initialization of general lattice Boltzmann methods introducing an ad hoc modified equation analysis.•We find the constraints to obtain consistent initialization schemes, preserving second-order for the overall method.•We finely describe initial boundary layers due to dissipation mismatches between bulk and initialization schemes.•We introduce the observability of a lattice Boltzmann scheme, characterizing those with easily-mastered initializations.•We test the introduced analytical tools and their effectiveness through several—very conclusive—numerical experiments.
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