The widespread adoption of electric vehicles (EVs) in urban logistics has led to the Electric Vehicle Routing Problem (EVRP) emerging as a prominent topic in combinatorial optimization research. To improve the efficiency of urban logistics distribution and minimize operational costs, the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (MDHOTDEVRP) is examined, where EVs may return to the nearest depot after completing deliveries instead of returning to their initial depot. This study proposes a Granular Ball-Ant Colony Optimization algorithm with Variable Neighborhood Search (GBACO-VNS) to address the MDHOTDEVRP. The approach extends the original GB-ACO framework by integrating a variable neighborhood search (VNS) mechanism to break the rigidity of routes and granular ball structures. The integration of VNS enhances exploration of the solution space and helps escape local optima. Furthermore, a range anxiety (RA) model is introduced to guide charging decisions based on drivers' anxiety levels during travel, which better reflects real-world operational conditions. The experimental results indicate that GBACO-VNS substantially improves quality of the solution compared to several optimization algorithms while maintaining computational efficiency. These findings confirm its effectiveness for the EVRP and establish an efficient algorithmic framework for addressing complex EVRPs.

Abstract This study focuses on an industrial facility’s time-slot allotment (TSA) problem, where loading and unloading docks are assigned to the incoming lorries, reducing the number of them waiting for service. Several constraints apply to the different dock stations, including disparate timetables and task duration, various capacities of simultaneous services, etc. Evolutionary algorithms cope with the enormous variability of the combinatorial problem, maximizing the number of accepted lorries while decreasing the queue at the entrance. Interestingly, the problem’s structure led to unconventional operator probabilities, which also analyzes the evolutionary techniques and operators included in this study. Comparative analysis with state-of-the-art methods highlights the algorithm’s effectiveness, though computational demands rise with population size.

Electric vehicles (EVs) are increasingly utilized in logistics and distribution to expedite achieving carbon peaking and neutrality goals, drawing considerable attention to the Electric Vehicle Routing Problem (EVRP). This study investigates the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (MDHOTDEVRP) and aims to improve coordination and distribution efficiency among logistics depots. This problem involves multiple depots, with EVs allowed to return to the nearest depot after completing their distribution tasks. We propose a hybrid method to solve the MDHOTDEVRP by integrating granular ball (GB) computing with the Ant Colony Optimization (ACO) algorithm. Firstly, enhanced k-means clustering is utilized to allocate customers to EVs. Then, customers within each cluster are subdivided into multiple GBs, with the paths of these GBs being scheduled. Finally, the ACO algorithm establishes routes within each GB. Experimental results indicate that the proposed GB-ACO algorithm efficiently allocates charging stations and plans distribution routes in scenarios with clustered distributions.

User interfaces look different across web, desktop, and mobile platforms — not just in layout, but in how buttons, icons, and text appear. This makes it hard for deep learning models trained on one platform to accurately detect UI components on another. In this paper, we evaluate the cross-domain generalization of three modern object detectors — YOLOv8, YOLOv9, and Faster R-CNN — trained on one or more GUI platforms using three datasets: GENGUI (web), UICVD (desktop), and VINS (mobile). We focus on three common UI classes: Text, Button, and Icon, and compare model performance across four scenarios: in-domain training, domain adaptation, fine-tuning, and combined training. Our results show that YOLOv9 consistently delivers the best cross-domain performance, especially when fine-tuned — achieving up to 95.5% mAP when adapted from desktop to web interfaces. We also fnd that Text is the most transferable class, while Button and Icon require adaptation to new visual styles. Fine-tuning emerges as the most effective strategy for improving generalization with limited data.

Function optimization represents a remarkable challenge in industry and society, aiming to find reasonable solutions –even if they are suboptimal– for everyday problems. Metaheuristics drive the optimization search towards the goals using a specific algorithm inspired by different concepts: from industrial processes to the behaviour of living beings in nature, from mathematical ideas to physics notions. This research proposes a new metaheuristic inspired by the Slime Mould and its foraging behaviours. On the one hand, an exploitation stage mimics the greedy amoeba’s conduct when food is plenty. On the other hand, an exploration stage copies the fruity aggregation of the cells and the subsequent spore dissemination. This study compares the most cited metaheuristics and the Slime Mould Optimization in two different experimentation stages: on the one hand, the optimization of standard benchmarking functions; on the other hand, solving the robot path planning problem. Moreover, a hybridization of the SMO and the WOA is presented, which keeps the SMO’s convergence speed and the WOA’s good performance in finding the best solutions.

Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location of the abnormality. We present a method to automatically define the foreground seeds used in GrowCut. For experiments, mammograms from mini-MIAS dataset are used and a precision of 93.63% for the foreground seeds masks is achieved, which leads to promising segmentation results.

The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining the region of interest (ROI) and segmenting the result image (using k-means algorithm), Gray-Level Run-Length Matrices (GLRLM) features are extracted from both the ROI and from the segmented image in four directions (horizontal, vertical, first- and second diagonals). To reduce the dimensionality of the input data composed from the GLRLM features of the ROI and its segmentation for different combination of directions (removing redundant information, selecting just the most essential ones) two methods are used: Principal Component Analysis (PCA), and genetic algorithm (GA) feature selection. For classification, two methods are used and compared, namely Decision Trees (DT) and Random Forest (RF). For experiments we used the Mammographic Image Analysis Society (MIAS) dataset to train and to test the classifiers. The best performance is obtained for GLRLM features calculated for directions 45◦, and 90◦, using PCA feature selection and RF with a 100% training accuracy and 70% test accuracy.

In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Most of the existing recommendation systems based their models on collaborative filtering approaches that make them simple to implement. However, performance of most of the existing collaborative filtering-based recommendation system suffers due to the challenges, such as: (a) cold start, (b) data sparseness, and (c) scalability. Moreover, recommendation problem is often characterized by the presence of many conflicting objectives or decision variables, such as users' preferences and venue closeness. In this paper, we proposed MobiContext, a hybrid cloud-based bi-objective recommendation framework (BORF) for mobile social networks. The MobiContext utilizes multi-objective optimization techniques to generate personalized recommendations. To address the issues pertaining to cold start and data sparseness, the BORF performs data preprocessing by using the Hub-Average (HA) inference model. Moreover, the Weighted Sum Approach (WSA) is implemented for scalar optimization and an evolutionary algorithm (NSGA-II) is applied for vector optimization to provide optimal suggestions to the users about a venue. The results of comprehensive experiments on a large-scale real dataset confirm the accuracy of the proposed recommendation framework.

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

In this paper, we describe a hybrid approach based on the use of genetic algorithms for solving the Clustered Vehicle Routing Problem, denoted by CluVRP. The problem studied in this work is a generalization of the classical Vehicle Routing Problem (VRP) and is closely related to the Generalized Vehicle Routing Problem (GVRP). Along with the genetic algorithm, we consider a local-global approach to the problem that is reducing considerably the size of the solutions space. The obtained computational results point out that our algorithm is an appropriate method to explore the search space of this complex problem and leads to good solutions in a reasonable amount of time.

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Gene expression microarray data provides simultaneous activity measurement of thousands of features facilitating a potential effective and reliable cancer diagnosis. An important and challenging task in microarray analysis refers to selecting the mos

The discovery and analysis of communities in networks is a topic of high interest in sociology, biology and computer science. Complex networks in nature and society range from the immune system and the brain to social, communication and transport networks. The key issue in the development of algorithms able to automatically detect communities in complex networks refers to a meaningful quality evaluation of a community structure. Given a certain grouping of nodes into communities, a good measure is needed to evaluate the quality of the community structure based on the definition that a strong community has dense intra-connections and sparse outside-community links. We propose a new fitness function for the assessment of community structures quality which is based on the number of nodes and their links inside a community versus the community size further reported to the size of the network. A novel aspect of the proposed fitness function refers to considering the way nodes connect to other nodes inside the same community making this second level of links contribute to the strength of the community. The introduced fitness function is tested inside a collaborative evolutionary algorithm specifically designed for the problem of community detection in complex networks. Computational experiments are performed for several real-world complex networks which have a known real community structure. This allows the direct verification of the quality of evolved communities via the proposed fitness function emphasizing extremely promising numerical results.

Problems exhibiting a building-block structure but lacking pairwise dependencies are hard for linkage learning mechanisms and consequently can be very hard to optimize. The current methods capable of building-block wise crossover begin the search for their models by exploiting dependencies between pairs of variables, thus fail to capture higher-order interactions that can not be easily decomposed into lower ones.

Resource-Constrained Project Scheduling is an NP-hard problem very attractive for researchers due to its large area of applications. This paper concentrates on the evolutionary approaches to Resource-Constrained Project Scheduling based on permutation encoded individuals. A new recombination operator is proposed and a comparative analysis of several recombination operators is presented based on computational experiments for several project instances. Numerical results emphasize a good performance of the proposed crossover scheme which takes into account information from the global best individual besides the genetic material from parents.

The prediction of minimum-energy protein structures starting from a sequence of amino acids is a computationally challenging problem even in simplified lattice protein models. A hybrid evolutionary model is designed and tested in the current paper to address this well-known NP-hard problem. Hill-climbing strategies are integrated in the search operators and a meaningful diversification of genetic material occurs during the population evolution. The main features of the proposed algorithm refer to a weak hill-climbing application of uniform crossover and pull move transformations and the randomization of genetic material based on the fingerprint of the protein conformations. Numerical experiments are performed for several difficult bidimensional instances from lattice models (the hydrophobic-polar model and functional model proteins). The results are competitive with those obtained by related population-based optimization algorithms.

The discovery of community structures in complex networks is a challenging problem intensively studied in recent years. This paper investigates the performance of evolutionary algorithms for the task of detecting overlapping communities. This task is of great importance as the membership of a node to more than one group is naturally occuring in many real-world networks from fields such as sociology, biology and computer science. One of the major challenges in designing evolutionary algorithms for overlapping community detection is the efficient assessment of the quality of any particular division of nodes into groups. We test four different fitness functions in an evolutionary approach to the problem using the same chromosome representation and search scheme. The performance of the resulting algorithms is tested in a set of computational experiments for some real-world networks. We show that none of the fitness functions used are able to guide the search process towards good partitions based on a measure of the normalized mutual information.

The real-world problem of Urban Bicycles Renting Systems (UBRS) in a city requires the optimization of vehicle routes connecting several bicycle base stations and storage centers. This problem can be modeled as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneaous need for pickup and delivery at each base station location. Based on the VRP model specification, an evolutionary approach is proposed to address the UBRS problem. Individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. Better-fitted offspring generated via order crossover or swap mutation are asynchronously inserted in the population. The proposed evolutionary algorithm is engaged for the UBRS problem using data from the city of Barcelona with promising results. Some relevant parameters that can enhance the proposed approach have been identified and analysed via the computational experiments.

Lattice-bound folding models are often used by computer scientists as a simplified instance of the chain folding problem (featuring, at the other end of the complexity spectrum, conformational sampling of biologically important molecules). Lattice-bound folding is relatively fast, thus well suited for benchmarking of nature-inspired optimization heuristics. Yet, it is not clear whether the benchmark-winning heuristics are necessarily the best suited for the more complex problems of atom-level resolution conformational sampling. This paper reports the design of - as far as possible - equivalent evolutionary operators and algorithms, for both chains and molecules, to explicitly check whether chain folding may serve as “training ground” for real sampling protocols.

A Geometric Collaborative Evolutionary (GCE) model is presented and studied. An asynchronous search process is facilitated through a gradual propagation of the fittest individuals' genetic material into the population. Recombination is guided by the geometrical structure of the population. The GCE model specifies three strategies for recombination corresponding to three subpopulations (societies of agents). Each individual in the population acts as an autonomous agent with the goal of optimizing its fitness being able to communicate and select a mate for recombination. Complex dynamics in the proposed system are investigated against the probability of dominance between agent societies. A significant emergent pattern and corresponding transition interval are emphasized in several experiments. Percolation-like behavior is also detected, suggesting the complete dominance of one agent society over the entire population under certain conditions. Furthermore, numerical results indicate a good performance of the proposed evolutionary asynchronous search model.

Ant models are investigated with the purpose of providing a high-quality performing heuristic for solving the linear ordering problem. Extending the Ant Colony System (ACS) model, the proposed Step-Back Sensitive Ant Model (SBSAM) allows agents to take a 'step back' if it reaches a virtual state modulated by various sensitivity levels to the pheromone trails. An effective exploration of the search space is performed particularly by agents having low pheromone sensitivity while the exploitation of intermediary solutions is facilitated by highly-sensitive ants. Both ACS and SB-SAM techniques compete with existing heuristic methods for linear ordering in terms of solution quality.

A new metaheuristic called Sensitive Ant Model (SAM) for solving combinatorial optimization problems is proposed. SAM improves and extends the Ant Colony System approach by enhancing each agent of the model with properties that induce heterogeneity. SAM agents are endowed with different pheromone sensitivity levels. Highly-sensitive agents are essentially influenced in the decision making process by stigmergic information and thus likely to select strong pheromone-marked moves. Search intensification can be therefore sustained. Agents with low sensitivity are biased towards random search inducing diversity for exploration of the environment. A heterogeneous agent model has the potential to cope with complex and/or dynamic search spaces. Sensitive agents (or ants) allow many types of reactions to a changing environment facilitating an efficient balance between exploration and exploitation.