It is an irrefutable fact that the Differential Evolution (DE) algorithm is one of the most widely used stochastic algorithms for solving complex optimization problems. In recent decades, the DE algorithm has garnered significant interest from researchers due to its considerable potential. However, it is evident that the scientific community considers it necessary to modify and create variants of the original version to improve its performance. Within the DE algorithm, the mutation operator has been an essential component in improving performance and exploration/exploitation balance. The present manuscript proposes a novel approach to the mutation operator of the DE algorithm, grounded in the Mahalanobis distance. The Mahalanobis Distance Enhanced Differential Evolution (IMPDE) algorithm is an innovative methodology that aims to explore a new paradigm by measuring the distance of particles to the mean during each iteration. The enhancement of the algorithm is appraised by employing a series of CEC-2017 assessments and traditional optimization problems. Furthermore, a nonparametric Friedman test is conducted to refute the obtained results. This work represents a significant contribution to the ongoing efforts to enhance the optimization process of one of the most prevalent algorithms in recent times.

Efficient blood plasma distribution is essential for minimizing wastage and ensuring timely availability in blood banks. This study proposes an optimized assignment framework that enhances blood plasma management by maximizing utilization before expiration and ensuring Rhesus compatibility. We implement and compare three evolutionary optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), with enhancements to balance exploration and exploitation for optimal inventory management. Extensive experiments were conducted by varying key control parameters, such as population size and the number of generations or iterations, while maintaining consistency across all algorithms. Performance evaluation on real-world blood bank datasets demonstrates that DE outperforms GA and PSO, achieving the lowest blood importation and expiration rates. The results highlight the potential of evolutionary computation in optimizing healthcare inventory management problems, which offers practical strategies for improving blood bank efficiency, data-driven decision-making, and patient care outcomes.

Maintaining population diversity in evolutionary computation poses a significant challenge throughout the optimization process. A balanced exploration-exploitation dynamic is crucial for achieving optimal results, and a diverse population proportion plays a key role in achieving this balance. Over the years, various methods have been proposed to evaluate diversity. However, relying on a single value to gauge population diversity has its limitations. This paper explores the shortcomings of existing population diversity metrics through extensive experimentation with different population configurations. The findings reveal that some metrics may be misleading, as a lower diversity value does not necessarily imply poor performance in solving optimization problems. In response to these challenges, this paper introduces a new and improved diversity metric, building upon the Dimension-Wise Diversity notion. The proposed metric corrects the shortcomings associated with its predecessors, leading to a more accurate interpretation of diversity values.

Image processing is a fundamental field in computer science with applications across various real-life areas. Image enhancement in the preprocessing stage is crucial for tasks in computer vision. Contrast enhancement in images aims to improve visual quality by increasing contrast and highlighting significant details. Although classical contrast enhancement techniques are widely used, they often suffer from issues such as over-enhancement due to the lack of mechanisms to control this improvement. To improve the contrast, transformation functions assign new intensities to each pixel in the image. One of the main drawbacks of the transformation functions is tuning their parameters. On the other hand, most contrast-enhancement techniques are typically used to improve the contrast in color images. In this regard, this study examines the effectiveness of three color spaces-HSV, HSI, and CIELAB-in enhancing contrast, with the goal of identifying the most effective space for this purpose. Additionally, the performance of a widely used metaheuristic is evaluated in tuning the parameters of the transformation function. The model is evaluated using standard quality indicators on a public image dataset. Preliminary findings suggest that the HSI color space is better suited for optimization using metaheuristics and effectively improves image contrast.

Training artificial neural networks is a fundamental step in developing machine learning models, as it determines their ability to learn and generalise from data. While gradient-based methods such as stochastic gradient descent and its variants dominate training approaches, they are susceptible to issues like sensitivity to initialisation and convergence to local optima. To address these challenges, gradient-free metaheuristic algorithms, such as differential evolution (DE), are promising alternatives due to their ability to effectively explore complex optimisation landscapes. In this paper, we propose a novel DE-based algorithm, PruneClust-DE, for training multilayer neural networks. Our approach introduces two key strategies: (1) clustering-based interpolation, which partitions the population into clusters, identifies centroids, and generates new candidate solutions by interpolating between cluster centroids to balance exploration and exploitation, and (2) fitness-based pruning, a mechanism that retains only the fittest individuals after introducing new candidates, ensuring a constant yet high-quality population. We validate our proposed algorithm across diverse datasets and compare its performance with other state-of-the-art methods, demonstrating its superiority in achieving robust results.

Determining optimal weights and biases for neural networks is a critical task. While gradient-based methods are widely used for training, they are sensitive to initialisation and susceptible to local optima. Population-based metaheuristics, such as differential evolution (DE), can offer a reliable alternative. Recently, clustering-based DE approaches have been proposed to further improve this process. However, they suffer from increased complexity, particularly with growing network sizes, leading to longer computation times. In this paper, we introduce strategies to reduce the time complexity of clustering-based DE, including clustering in the objective space, a two-tier clustering period, and one-step k-means clustering. We select one of the recent training algorithms, C2L-DE, as a representative method to incorporate our proposed strategies, leading to a lightweight version, C2L-DE-Lite. We show that C2L-DE-Lite decreases the complexity from $O\left. {\left({\sqrt {{N_{pop}}} \cdot{N_{pop}}\cdot} \right.d\cdot I}\right)$, where Npopis the population size, d is the dimensionality, and I is the number of iterations, to $O\left({\frac{{{N_{pop}}\cdot\sqrt {{N_{pop}}} }}{{CP}}}\right)$, where CP is the clustering period. This means that the complexity remains constant for increasing sizes of networks. Extensive experiments demonstrate that while significantly reducing time complexity, C2L-DE-Lite maintains similar performance levels.

Optical Coherence Tomography (OCT) is an imaging technique that enables medical experts to obtain transverse scans of various small tissues, including relevant eye elements, such as the retina. The analysis of retinal OCT scans holds significant clinical relevance as it provides physicians with anatomical and pathological insights. However, speckle noise, inherent to OCT’s operational nature, negatively impacts image quality and hinders precise interpretation and diagnosis. In this regard, this paper presents the Metaheuristic-Driven Bayesian Speckle Denoising (MDBSD) framework: a fusion of a metaheuristic-based optimization using the Optimized Bayesian Non-local means with block selection (OBNLM) as the transformation function while introducing a specific fitness function for the evaluation of the potential solutions. Different classical metaheuristic algorithms are utilized and applied to a public retinal OCT dataset, followed by a numerical evaluation using contrast and denoising performance metrics to validate this framework. Regardless of the metaheuristic technique used for optimization, enhanced images present a notorious scan denoising while keeping retinal layers’ edges quite delimited, proving this framework’s efficiency. Additionally, future research directions are outlined for further advancement in this area.

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://github.com/AyushRoy2001/FA-Net

Differential Evolution (DE) represents an advanced evolutionary algorithm due to its continuous innovation and advances around its operators and applications. In general, the mutation operator is designed to enhance the performance of Differential Evolution (DE) algorithms. The mutation operator is a vital component in enhancing the effectiveness of DE algorithms. The proposed mutation operator, the Lissajous Mutation (LM) is specifically designed to improve the balance between exploration and exploitation through the geometric origin of the Lissajous curves. This paper introduces an up-to-date version of Differential Evolution with Lissajous Mutation, Multi-crossover, and Dynamic selection (IDEL). This new DE variation presents changes in all the fundamental stages, well-known as Mutation, Crossover, and Selection. Through a series of meticulously designed experiments and rigorous comparisons with existing DE variants, the effectiveness of IDEL is demon-strated. IDEL showcases notable improvements in convergence speed, exploration-exploitation equilibrium, and solution quality across diverse benchmark scenarios. This paper contributes to the ongoing growth of DE and presents a novel way to enhance its overall performance.

This paper introduces Dynamic Social Particle Swarm Optimization (DS-PSO), a novel adaptation of the traditional Particle Swarm Optimization (PSO) technique specifically engineered for complex optimization challenges. DS-PSO innovatively incorporates dynamic social interactions within the swarm, enhancing adaptability and addressing the typical limitations of premature convergence and limited exploration in conventional PSO. A key feature of DS-PSO is its ability to balance exploration and exploitation efficiently, making it particularly suitable for dynamic environments. The primary application highlighted in this study is automatic clustering, a crucial task in data analysis involving unsupervised data grouping without prior knowledge of cluster numbers. DS-PSO’s flexibility and improved search capability demonstrate its potential as an effective tool for automatic clustering, promising significant advancements in data-driven optimization and analysis.

In the context of evolutionary algorithms, having the ability to adapt to any search space within an optimization problem is an essential task. Appropriately adapting the population can lead to better solutions and more efficient use of function call resources. This article presents a renewed approach to population management inspired by modifying the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The proposed strategy aims to improve the algorithm’s population adaptability to the search space and optimize function evaluations. Statistically evaluated experimental test outcomes demonstrate significantly better performance on high-dimensional problems in comparison to the original CMA-ES and seven other known evolutionary algorithms in the literature.

Abstract An Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified ‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is .

Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it has shortcomings, such as premature convergence and stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global and local exploration and exploitation capability by determining the influence of the previous velocity on its current motion. Therefore, this article proposes a PSO with a Diversity-aware Inertia and Velocity Control (PSOIVC) algorithm to improve the PSO performance. The PSOIVC employs a novel diversity-aware inertia weight and velocity control approach to tune the parameters to produce a trade-off between exploration and exploitation of the algorithm using the dimension-wise diversity. The PSOIVC algorithm is compared with eight algorithms, including variants of the PSO, on a set of 30 benchmark functions for a single objective real parameter in 30 and 50 dimensions. Based on the results, the proposal presents significant outcomes according to the average values obtained for both comparisons; because it performed similarly or better than the other algorithms in 23/30 and 16/30 for 30 and 50 dimensions, respectively.

Particle swarm optimization (PSO) is essential to evolutionary computation algorithms (ECA). The PSO has some drawbacks as premature convergence and stagnation at local minima. Inertia weight is a parameter that controls the global and local exploration and exploitation capability in the PSO by determining the influence of the previous velocity on its current motion. This article proposes using a stagnation counter that verifies the times the PSO is stuck in the same fitness value. In the proposed fuzzy controlled PSO with stagnation coefficient (FCPSO), a fuzzy controller is designed to tune the inertia weight based on the population's diversity and the search's stagnation. This modification allows the PSO to escape from suboptimal values enhancing its search capabilities. The FCPSO is tested over 28 benchmark functions in 50 dimensions. Besides, it has been compared with nine optimization algorithms from the state-of-the-art. The experiments and comparisons suggest that the FCPSO is an interesting tool for solving complex optimization problems.

Hyper-heuristics (HH) are strategies that have a high-level mechanism to combine, select or generate heuristics at a low level to find solutions based on the information received during the search process. Typically, an HH approach involves evaluating several algorithms and constructing a priori a structure containing the information required to select, combine or generate the appropriate heuristic to solve a given problem using a usually probabilistic selection method. This paper considers a constructive online learning HH with an agent population selection and control strategy for each heuristic with a reward-punishment approach based on a mean absolute deviation criterion and exploitation-exploration stages to optimize these phases in the optimization process. We compare our proposal, denominated Hyper-heuristic based on the mean absolute deviation metric and the exploration-exploitation stages for the population management (HH-MAD) using uni-modal, multi-modal, composite, and shifted benchmark functions among different optimization algorithms. The experimental results validated the central tendency measures and the non-parametric tests, showing that HH-MAD is competitive, outperforming the other approaches.

Population size is an important variable in evolutionary algorithms (EA). Its proper configuration improves the performance of the search process not only in terms of the fitness function but also for the resources required. This article introduced a population management mechanism that includes different operators. Such operators are designed and applied based on the diversity of the population. In general terms, the operators address problems in EA regarding stagnation and the inefficient use of the function evaluations. As a case of study, the proposed method is applied in the Differential Evolution (DE) to provide it the ability to change its population size according to its needs. The experimental results and comparisons demonstrate greatly improved performance when compared to the unmodified DE, some of its most successful variants, and other much more complex algorithms from the state-of-the-art.

Plagiarism detection is a challenging task, aiming to identify similar items in two documents. In this paper, we present a novel approach to automatic plagiarism detection that combines BERT (bidirectional encoder representations from transformers) word embedding, attention mechanism-based long short-term memory (LSTM) networks, and an improved differential evolution (DE) algorithm for weight initialisation. BERT is used to pretrain deep bidirectional representations in all layers, while the pre-trained BERT model can be fine-tuned with only one extra output layer without significant changes in architecture. Deep learning algorithms often use the random weighting method for initialisation, followed by gradient-based optimisation algorithms such as back-propagation for training, making them susceptible to getting trapped in local optima. To address this, population- based metaheuristic algorithms such as DE can be used. We propose an improved DE algorithm with a clustering-based mutation operator, where first a winning cluster of candidate solutions is identified and a new updating strategy is then applied to include new candidate solutions in the current population. The proposed DE algorithm is used in LSTM, attention mechanism, and feed- forward neural networks to yield the initial seeds for subsequent gradient-based optimisation. We compare our proposed model with conventional and population-based approaches on three datasets (SNLI, MSRP and SemEval2014) and demonstrate it to give superior plagiarism detection performance.

The balance between exploration and exploitation is an important feature in Evolutionary Algorithms (EA). The use of different operators permits to explore the search space and exploit the most prominent regions. This article introduces a dynamic operator selection method that considers different criteria at the same time. The proposed approach uses a dynamic decision matrix (DyDM) to identify which operators must be used at each iteration based on how the algorithm behaves. The DyDM considers specific information as the diversity of the algorithm to avoid stagnation, the actual iteration to work accordingly, and the fitness to direct the search. The proposed approach is called Dynamic Decision Matrix Optimizer (DyDMO) and it has been compared with different well-known algorithms tested on the CEC 2017 benchmark functions. The comparative analysis and non-parametric statistical tests validate how DyDMO im-proves the quality of the solutions and is more stable than its comnetitors.

Training multi-layer neural networks (MLNNs), a challenging task, involves finding appropriate weights and biases. MLNN training is important since the performance of MLNNs is mainly dependent on these network parameters. However, conventional algorithms such as gradient-based methods, while extensively used for MLNN training, suffer from drawbacks such as a tendency to getting stuck in local optima. Population-based metaheuristic algorithms can be used to overcome these problems. In this paper, we propose a novel MLNN training algorithm, CenDE-DOBL, that is based on differential evolution (DE), a centroid-based strategy (Cen-S), and dynamic opposition-based learning (DOBL). The Cen-S approach employs the centroid of the best individuals as a member of population, while other members are updated using standard crossover and mutation operators. This improves exploitation since the new member is obtained based on the best individuals, while the employed DOBL strategy, which uses the opposite of an individual, leads to enhanced exploration. Our extensive experiments compare CenDE-DOBL to 26 conventional and population-based algorithms and confirm it to provide excellent MLNN training performance.

The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

Several metaheuristics have been developed for global optimization. Most of them are designed for solving a specific problem at hand, and their use on a new implementation is a challenging task. Hyper-heuristics are strategies that support these issues, combining a metaheuristic in a high-level for selecting or generating simple heuristics from a low-level. The aim is to find nearoptimal solutions based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, using a Probabilistic Graphical Model (PGM) to extract and represent interactions between its low-level heuristics to provide high-valued problem solutions. In this paper, we consider an EDA based on Bayesian networks as PGM on a hyper-heuristic context which encompasses a heuristic selection approach to find the best combinations of different known simple heuristics. We compare our proposed approach named Hyper-heuristic approach based on Bayesian Optimization Algorithm (HHBOA) using CEC'05 benchmark functions among 9 optimization algorithms. The experimental results show that HHBOA is competitive, outperforming the other approaches, especially in terms of convergence, on most of the functions considered in this paper.