Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the mutation operator. In this paper, we propose a novel DE algorithm, Clu-DE, that improves the efficacy of DE using a novel clustering-based mutation operator. First, we find, using a clustering algorithm, a winner cluster in search space and select the best candidate solution in this cluster as the base vector in the mutation operator. Then, an updating scheme is introduced to include new candidate solutions in the current population. Experimental results on CEC-2017 benchmark functions with dimensionalities of 30, 50 and 100 confirm that Clu-DE yields improved performance compared to DE.

Population-based metaheuristic algorithms are problem-independent approaches to solve global optimisation problems. The human mental search (HMS) algorithm is a powerful population-based metaheuristic algorithm that has been shown to yield competitive performance for a variety of optimisation problems. HMS comprises three main operators, mental search, grouping, and movement. Mental search explores the neighbourhood of candidate solutions based on a Levy flight distribution to allow for simultaneous exploration and exploitation. Grouping is used to cluster the current population in order to find a promising area in search space, while during movement, candidate solutions move towards the identified promising area. In this paper, we propose an improved HMS algorithm-HMS-IS-OSK - that introduces an adaptive selection of the number of mental processes to improve the exploitation ability of HMS, and a one-step k-means algorithm for grouping to decrease the computational complexity. To evaluate the proposed algorithm, we perform a set of experiments on the CEC 2017 bench-mark functions with dimensionalities of 30, 50, and 100. The obtained results show that HMS-IS-OSK outperforms standard HMS as well as other population-based metaheuristic algorithms including covariance matrix adaptation evolution strategy (CMAES), particle swarm optimisation (PSO), artificial bee colony algorithm (ABC), whale optimisation algorithm (WOA), grey wolf optimiser (GWO), and moth-flame optimisation (MFO).

Multi-layer feed-forward neural networks have been used to tackle many complex practical applications. Their performance is closely related to the success of training algorithms which adapt the weights in the network. Although conventional algorithms such as back-propagation are widely used, they suffer from drawbacks such as a tendency to get trapped in local optima. Stochastic optimisation algorithms, and in particular population-based metaheuristics, represent a useful alternative in this context. In this paper, we have proposed an effective hybrid algorithm, CLPSO-LM, which is based on particle swarm optimisation (PSO), a population-based metaheuristic algorithm, the Levenberg- Marquardt (LM) algorithm as a local search algorithm, and a comprehensive learning (CL) strategy. The CL strategy in our algorithm is responsible for improving the exploration ability of the algorithm and preventing premature convergence using neighbour candidate solutions in PSO. The best position found by comprehensive learning PSO is then used as the initial network weights for the LM algorithm. An extensive set of experiments on different classification benchmark datasets and comparison to various conventional and population-based algorithms shows CLPSO-LM to yield very competitive performance.

With the widespread use of image processing and computer vision applications, effective denoising methods are highly sought after, prompting the development of a variety of algorithms under different assumptions on noise and signal properties. However, most of these techniques are developed to deal with grayscale images, and are typically extended to colour images by processing each RGB channel separately. In this paper, we extend the curvelet power shrinkage algorithm, introduced previously for grayscale images, to colour image denoising, by applying the proposed method in the luminance/opponent-colour YCbCr colour space to take into consideration image inter-channel dependencies. The performance of the proposed algorithm on colour images corrupted by additive white Gaussian noise is evaluated in terms of both objective and subjective measures, and the obtained results show our method to be competitive to other methods including curvelet domain hard thresholding and MSt-SVD.

In the paper we described a method for video sequence processing, which is resistant to shifts of an optical source under noisy conditions of video sequence individual frames. The noisiness of frames means blurring of individual frames of a video sequence due to sharp shifts of an optical source, data transfer artifacts or zoom operation during autofocus and consequently - as a result some defocused images series can be obtained. The novelty of the method lies in the combination of the approach of analyzing descriptors of local features of the image of graph algorithms and extrapolating the values of the camera offset by the least squares method at the moments of noise in the individual frames of the video sequence. We have depicted several experimental research results of the proposed method and a numerical comparison of qualitative characteristics with the existing ones.

Human mental search (HMS) is a recent population-based metaheuristic inspired by the exploration manner in the bid space of online auctions. It has three main operators: (1) mental search which explores the vicinity of each candidate solution based on Levy flight, (2) grouping which is performed using a clustering algorithm to find a promising area, and (3) moving towards the promising area. HMS has shown competitive performance in solving various optimisation problems.In this paper, an improved HMS algorithm, Global-Best Human Mental Search with Random Clustering Strategy (GHMS-RCS) is proposed as a variant of HMS for global optimisation. GHMS-RCS benefits from the information of global best solutions to improve the exploitation of the HMS algorithm. Also, to reduce the time complexity and enhance exploration and exploitation, a new strategy named random clustering is introduced to improve the grouping operator in HMS. Experimental results show that GHMS-RCS outperforms standard HMS as well as other population-based algorithms including particle swarm optimisation (PSO), shuffled frog-leaping algorithm (SFLA), and biogeography-based optimisation (BBO).

Preserving the content of historic handwritten manuscripts is important for a variety of reasons. On the other hand, digital libraries are rapidly expanding and thus facilitate to store this information directly in digital form. For digitising text documents, a crucial step is to binarise the captured images to separate the text from the background. In this paper, we propose an effective approach for binarisation of handwritten Arabic manuscripts which employs a whale optimisation algorithm, incorporating a fuzzy c-means objective function, to obtain optimal thresholds. Experimental results confirm the effectiveness of the proposed approach compared to earlier methods.

Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. Here, HEp-2 cells are categorised into different groups, which allow to make implications about different autoimmune diseases. Traditionally, this categorisation is performed manually by an expert and is hence both subjective and time intensive. In this paper, we present an effective method for classification of HEp-2 cells in which we first extract local binary pattern (LBP) texture features in form of multi-dimensional LBP (MD-LBP) histograms and then employ a multiple kernel learning approach to classification that integrates a multitude of support vector kernels generated by sampling the feature space. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate that our employed texture features are indeed useful for the differentiation of HEp-2 cells and that our multiple kernel learning based classification approach outperforms single kernel classification schemes. Our algorithm is shown to provide super performance compared to all techniques that were entered in the competition and to rival results obtained by a human expert.

Investigating information credibility is an important problem in online social networks such as Twitter. Since misleading information can get easily propagated in Twitter, ranking tweets according to their credibility can help to detect rumors and identify misinformation. In this paper, we propose a Petri net model to visualise tweet credibility in Twitter. We consider the uniform resource locator (URL) as an effective feature in evaluating tweet credibility since it is used to identify the source of tweets, especially for newsworthy tweets. We perform an experimental evaluation on about 1000 tweets, and show that the proposed model is effective for assigning tweets to two classes: credible and incredible tweets, which each class being further divided into two sub-classes (“credible” and “seem credible” and “doubtful” and “incredible” tweets, respectively) based on appropriate features.