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This research presents an intelligent, environment-aware navigation framework for smart electric vehicles (EVs), focusing on multi-agent mapping, real-time obstacle recognition, and adaptive route optimization. Unlike traditional navigation systems that primarily minimize cost and distance, this research emphasizes how EVs perceive, map, and interact with their surroundings. Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. The system intelligently balances delivery speed and energy efficiency by dynamically adjusting routes based on urgency, congestion, and battery constraints. When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. This dynamic decision making ensures optimal fleet performance by minimizing energy waste and reducing emissions. The framework further enhances sustainability by integrating an adaptive optimization model that continuously refines EV paths in response to real-time changes in traffic flow and charging station availability. By seamlessly combining real-time route adaptation with energy-efficient decision making, the proposed system supports scalable and sustainable EV fleet operations. The ability to dynamically optimize travel paths ensures minimal energy consumption while maintaining high operational efficiency. Experimental validation confirms that this approach not only improves EV navigation and obstacle avoidance but also significantly contributes to reducing emissions and enhancing the long-term viability of smart EV fleets in rapidly changing environments.

The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city’s limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only consider the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.

The Equilibrium Optimizer (EO) is an optimization algorithm inspired by a physical law called mass balance, which represents the amount of mass entering, leaving, and being produced in a control volume. Although the EO is a well-accepted and successful algorithm in the literature, it needs improvements in the search, exploration, and exploitation phases. Its main problems include low convergence, getting stuck in local minima, and imbalance between the exploration and exploitation phases. This paper introduces the Boosted Equilibrium Optimizer (BEO) algorithm, where improvements are proposed to solve these problems and improve the performance of the EO algorithm. New methods are proposed for the three important phases of the algorithm: initial population, candidate pool generation, and updating. In the proposed algorithm, the exploration phase is strengthened by using a uniformly distributed random initial population instead of the traditional random initial population and a versatile concentration pool strategy. Furthermore, the balance between the exploration and exploitation phases is improved with two new approaches proposed for the updating phase. These novel methods enhance the algorithm’s performance by more effectively balancing exploration and exploitation. The proposed algorithm is tested using a total of 23 standard test functions, including unimodal, multimodal, and fixed-size multimodal. The results are supported by numerical values and graphs. In addition, the proposed BEO algorithm is applied to solve real-world engineering design problems. The BEO outperforms the original EO algorithm on all problems.

In the face of increasingly diverse demands from tourists, traditional methods for scenic route planning often struggle to meet these varied needs. To address this challenge and enhance the overall service quality of tourist destinations, as well as to better understand individualized preferences of visitors, this study proposes a novel approach to scenic route planning and itinerary customization based on multi-layered mixed hypernetwork optimization. Firstly, an adaptive multi-route feature extraction method is introduced to capture personalized demands of tourists. Subsequently, a personalized tourist inference method based on a multi-layered mixed network is presented, utilizing the extracted personalized features to infer the true intentions of the tourists. Lastly, we propose a hypernetwork optimized route planning method, incorporating the inference results and personalized features to tailor the optimal touring paths for visitors. The results of our experiments underscore the efficacy of our methodology, attaining an accuracy score of 0.877 and an mAP score of 0.881 and outperforming strong competitors and facilitating the design of optimal paths for tourists.

Wireless sensor networks (WSNs) and Internet of Things (IoT) are essential for numerous applications. WSN nodes often operate on limited battery capacity, so energy efficiency is a significant problem for clustering and routing. In addition to these limitations, one of the primary issues of WSNs is achieving reliability and security of transmitted data in vulnerable environments to prevent malicious node attacks. This work aims to develop a secure and energy-efficient routing protocol for fault data prediction to enhance WSNs network lifespan and data reliability. The proposed technique has three major phases: cluster construction, optimal route selection, and intrusion detection. The adaptive shark smell optimization (ASSO) technique was initially used with three input parameters for CH selection. These parameters are the residual energy, the distance to the BS, and the node density. After clustering, salp swarm optimization (SSO) is used to select the optimum path for data transmission between clusters, resulting in an energy-efficient WSN. Finally, to ensure the security of cluster-based WSNs, an effective intrusion detection system based on a modified Elman recurrent neural network (MERNN) is implemented to detect the presence of intrusions in the network. The experimental results show that it outperforms the competing methods in various performance metrics. The performance results of quality of service (QoS) parameters are expressed as dispersion value (0.8072), packet delivery rate (98%), average delay (160 ms), network lifetime (3200 rounds), and the accuracy of this method is 99.2%. Compared to the SVM, ELM, HMM, and MK-ELM protocols, the proposed protocol increases network lifetime by 77%, 60%, 45.4%, and 14.2%, respectively.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV’s flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

Nonlinear process systems, such as conical tanks, pose significant control challenges owing to their varying cross-sectional geometry, which causes the dynamic behavior to change with the liquid height. Traditional fixed-parameter PID controllers often fail to deliver consistent performance across the entire operating range. To address this, a Flamingo Search-based Model Reference Adaptive PID (MRAC-PID) controller is proposed to adaptively tune the PID gains. In contrast to traditional metaheuristic optimizers, e.g., Particle Swarm or Genetic Algorithms, which require extensive computing and considerable time to converge, the Flamingo Search Algorithm (FSA) is optimized quickly and with no substantial computing cost, and thus is applicable to embedded implementation. The controller was implemented on a system based on ESP32-Jetson Nano hardware and tested experimentally in different operating conditions. The findings prove the innovativeness of the integration of the FSA in an MRAC framework of nonlinear liquid-level control, resulting in a lowest rise time of 8 s, settling times of 30–45 s, and a minimum overshoot of 2–5%, whilst steady-state errors are kept under 0.5 cm. Robustness analysis further confirmed phase margins of 85–90° and gain margins exceeding 20 dB. The proposed method had lower overshoot, faster settling, and better disturbance rejection compared to Ziegler-Nichols and Cohen-Coon tuned PID controllers. The results prove the proposed framework as a computationally efficient, robust, and scalable approach to real-time nonlinear process control.

Effective waste management is one of the major elements of urban sustainability, more so in rapidly growing cities like Dubai. This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. The system has four major components: (1) a mobile field application to add and modify collection points in real time; (2) a route optimization module that minimizes travel distance and CO₂ emissions while accounting for real-world constraints; (3) an interactive dashboard for decision-makers to monitor analytics, visualize routes, and make real-time adjustments; and (4) a navigator app for truck drivers to follow optimized routes seamlessly. Furthermore, the system includes a new adaptive waste demand management module, which dynamically updates the demand for each collection point using real-time usage data, rather than being based on static assumptions of capacity. The effectiveness of the system was tested on a sample of 110 collection bins located in three different areas in Dubai. Preliminary results indicate that route optimization alone has achieved a reduction of 19.1% in CO₂ emissions, and further improvement is expected with full implementation of the adaptive demand management module. The findings highlight the potential of intelligent systems to significantly reduce the environmental and financial costs associated with municipal waste collection, paving the way for scalable deployment in other urban environments.

Port coal transportation scheduling faces significant challenges with slow response times and low efficiency, especially under dynamic disruptions. To address these issues, this research proposes a novel hybrid meta-heuristic algorithm that deeply integrates the improved Ant Colony Optimization (IACO) and the Greedy Randomized Adaptive Search Procedure (GRASP). The proposed IACO-GRASP algorithm is designed to enhance collaborative scheduling by effectively coordinating global exploration and local optimization. The major findings from a case study at Qinhuangdao Port demonstrate that the algorithm increases ship turnover efficiency by 36.25% and reduces the empty running rate by 25.18%, leading to an estimated annual cost saving of 18.95 million yuan. Comparative experiments also show that the algorithm’s convergence speed is significantly improved over other hybrid methods. These findings highlight the potential of IACO-GRASP as a robust and efficient decision-support tool for building responsive, resource-optimized, and intelligent port operations.

Many applications of stochastic optimization involve making sequential decisions until some stopping criterion is satisfied. For example, in medical diagnosis, a doctor needs to perform an adaptive sequence of tests on a patient in order to diagnose a disease. Being adaptive allows the doctor to choose the next test based on the outcomes of prior tests. Given an a priori probability distribution over diseases, the goal is to minimize the expected cost of tests. In “Adaptive Submodular Ranking and Routing,” Navidi, Kambadur, and Nagarajan formulate a general stochastic optimization problem in which the stopping criterion corresponds to covering a submodular function. Such problems arise in many applications, including active learning, robotics, and disaster management. The authors obtain efficient algorithms with best possible performance guarantees. These results also extend to a vehicle-routing setting, in which one needs to plan an adaptive route based on information observed at nodes in the network. The authors also present experimental results on a data set arising in the identification of toxic chemicals, thereby demonstrating the practical applicability of their algorithm. We study a general stochastic ranking problem in which an algorithm needs to adaptively select a sequence of elements so as to “cover” a random scenario (drawn from a known distribution) at minimum expected cost. The coverage of each scenario is captured by an individual submodular function, in which the scenario is said to be covered when its function value goes above a given threshold. We obtain a logarithmic factor approximation algorithm for this adaptive ranking problem, which is the best possible (unless P = NP ). This problem unifies and generalizes many previously studied problems with applications in search ranking and active learning. The approximation ratio of our algorithm either matches or improves the best result known in each of these special cases. Furthermore, we extend our results to an adaptive vehicle-routing problem, in which costs are determined by an underlying metric. This routing problem is a significant generalization of the previously studied adaptive traveling salesman and traveling repairman problems. Our approximation ratio nearly matches the best bound known for these special cases. Finally, we present experimental results for some applications of adaptive ranking.