Efficient intersection management remains a critical challenge for Connected and Autonomous Vehicles (CAVs), especially under dynamic traffic conditions that require balancing safety, throughput, and responsiveness. Existing cooperative protocols, such as virtual platooning and rule-based scheduling, offer decentralized control but typically rely on fixed synchronization points and static sequencing rules, limiting their adaptability in real-time environments. In this study, we propose PSO-DCPVP, a novel hybrid framework that integrates Particle Swarm Optimization (PSO) within the Distributed Clearing Policy for Virtual Platooning (DCPVP). The key innovation lies in dynamically computing mobile synchronization points for each vehicle based on local traffic states, enabling more flexible and context-aware platoon coordination. We evaluate the framework in a custom multi-agent simulation environment under three traffic scenarios: low, moderate, and high demand over a 600 s simulation horizon. Results demonstrate that PSO-DCPVP significantly increases intersection throughput, exceeding 2.1 pcu/s in congested settings while reducing average delay to below 0.03 s. Compared to baseline strategies such as FIFS and DCPVP, PSO-DCPVP demonstrates strong potential for real-world deployment in intelligent transportation systems.

Hybrid quantum-classical algorithms are the subject of intensive research in quantum computing and specifically in the domains of Machine Learning and Approximate Optimization. Simple distance-based learning classifiers such as kNN or k-means classifiers have already been the subject of many proposals, but most of their quantum implementation only consider a very small number of classes/clusters and only use metrics suitable for normalized unit length quantum data. Very few approaches combine the need for Euclidean distance of unnormalized data with an efficient quantum computation implementation. Based on two kNN and k-means proposals from the literature, this paper proposes their adaptation to the design of a Quantum Self-Organizing Map in order to solve the NP-hard Traveling Salesman Optimization Problem (TSP) specifically. Hence, we evaluate the hybrid quantum-classical approach to Euclidean TSP instances of sizes from 52 to 127 cities and present the properties, advantages, and limitations of the quantum implementation in solving the TSP optimization problem.

Advancements in technology hold significant promise for the future of smart agriculture. Drones, serving as innovative tools for data acquisition, play a pivotal role in this context. Traditional pollination drones, which rely on extensive spraying methods, suffer from poor efficiency. To address this challenge, our focus is on enhancing pollination through object detection technology, leading to the introduction of the YOLO-FT detection algorithm based on YOLOv7. To validate our algorithm, we have curated a dataset of fruit tree flowers. Our approach involves several key components. Firstly, we propose a lightweight adaptive backbone network, which effectively extracts feature information through partial convolution and seamlessly integrates feature data from diverse channels. Secondly, we introduce a non-parametric attention module into neck network, bolstering the fusion of critical feature information. Finally, we leverage a rapid convergence function based on centroid distance to enhance bounding box regression performance. Experimental results demonstrate the superiority of the YOLO-FT algorithm, achieving$mAP_{50}$of 92.8%,$mAP_{50-95}$of 48.8%, and F1 score of 0.90, marking improvements of 3.6%, 1.6%, and 0.17, respectively, over the baseline. Furthermore, YOLO-FT exhibits a significant reduction in model complexity, with parameters decreased by 13.4% and FLOPs by 17.8%. This research serves as a valuable theoretical reference for advancements in smart agriculture.

The goal of structured mesh is to generate a compressed representation of the 3D surface, where near objects are provided with more details than objects far from the camera, according to the disparity map. The solution is based on the Kohonens Self-Organizing Map algorithm for the benefits of its ability to generate a topological map according to a probability distribution and its potential to be a natural massive parallel algorithm. The disparity map, which stands for a density distribution that reflects the proximity of objects to the camera, is partitioned into an appropriate number of cell units, in such a way that each cell is associated to a processing unit and responsible of a certain area of the plane. The advantage of the proposed model is that it is decentralized and based on data decomposition. The required processing units and memory are with linearly increasing relationship to the problem size. Experimental results show that our GPU implementation is able to provide near real-time performance with small size disparity maps and the running time increases in a linear way with a very weak increasing coefficient. The proposed method is suitable to deal with large scale problems in a massively parallel way.

This paper presents a cellular GPU model for structured mesh generation according to an input stereo-matching disparity map. Here, the disparity map stands for a density distribution that reflects the proximity of objects to the camera in 3D space. The meshing process consists in covering such data density distribution with a topological structured hexagonal grid that adapts itself and deforms according to the density values. The goal is to generate a compressed mesh where the nearest objects are provided with more details than objects which are far from the camera. The solution we propose is based on the Kohonen's Self-Organizing Map learning algorithm for the benefit of its ability to generate a topological map according to a probability distribution and its ability to be a natural massive parallel algorithm. We propose a GPU parallel model and its implantation of the SOM standard algorithm, and present experiments on a set of standard stereo-matching disparity map benchmarks.

This paper presents a coalition-based metaheuristic (CBM) to solve the uncapacitated facility location problem. CBM is a population-based metaheuristic where individuals encapsulate a single solution and are considered as agents. In comparison to classical evolutionary algorithms, these agents have additional capacities of decision, learning and cooperation. Our approach is also a case study to present how concepts from multiagent systems' domain may contribute to the design of new metaheuristics. The tackled problem is a well-known combinatorial optimization problem, namely the uncapacitated facility location problem, that consists in determining the sites in which some facilities must be set up to satisfy the requirements of a client set at minimum cost. A computational experiment is conducted to test the performance of learning mechanisms and to compare our approach with several existing metaheuristics. The results showed that CBM is competitive with powerful heuristics approaches and presents several advantages in terms of flexibility and modularity.

This paper presents a population based Metaheuristic adopting the metaphor of social autonomous agents. In this context, agents cooperate and self-adapt in order to collectively solve a given optimization problem. From an evolutionary computation point of view, mechanisms driving the search consist of combining intensification operators and diversification operators, such as local search and mutation or recombination. The multiagent paradigm mainly focuses on the adaptive capabilities of individual agents evolving in a context of decentralized control and asynchronous communication. In the proposed metaheuristic, the agentpsilas behavior is guided by a decision process for the operatorspsilachoice which is dynamically adapted during the search using reinforcement learning and mimetism learning between agents. The approach is called Coalition-Based Metaheuristic (CBM) to refer to the strong autonomy conferred to the agents. This approach is applied to the Vehicle Routing Problem to emphasize the performance of learning and cooperation mechanisms.

We focus on the dimensioning process of cellular networks that addresses the evaluation of equipment global costs to cover a city. To deal with frequency assignment, that constitutes the most critical resource in mobile systems, the network is usually modeled as a pattern of regular hexagonal cells. Each cell represents the area covered by the signal of a transmitter or base station (BS). Our work emphasizes on the design of irregular hexagonal cells in an adaptive way. Hexagons transform themselves and adapt their shapes according to a traffic density map and to geometrical constraints. This process, called adaptive meshing (AM), may be seen as a solution to minimize the required number of BS to cover a region and to propose a basis for transmitter positioning. The solution we present to the mesh generation problem for mobile network dimensioning is based on the use of an evolutionary algorithm. This algorithm, called hybrid island evolutionary strategy (HIES), performs distributed computation. It allows the user to tackle problem instances with large traffic density map requiring several hundreds of cells. HIES combines local search fast computation on individuals, incorporated into a global island-like strategy. Experiments are done on one real case representing the mobile traffic load of the second French city of Lyon and on several other traffic maps from urban fictive data sets.