Search Results (337)

Unmanned aerial vehicles (UAVs) play an increasingly vital role in wilderness search and rescue (SAR) operations by enhancing situational awareness and extending the capabilities of human teams. Yet, a lack of standardized benchmarks has impeded the systematic evaluation of single- and multi-agent path-planning algorithms. This paper introduces an open-source dataset and evaluation framework to address this gap. The framework comprises 60 geospatial scenarios across four distinct European environments, featuring high-resolution probability maps. We present a lost person probabilistic model derived from statistical models of lost person behavior. We provide a suite of tools for evaluating search paths against four baseline methods: Concentric Circles, Pizza Zigzag, Greedy, and Random Exploration, using three quantitative metrics: Accumulated probability of detection, time-discounted probability of detection, and lost person discovery score. We provide an evaluation framework to facilitate the comparative analysis of single- and multi-agent path-planning algorithms, supporting both the baseline methods presented and custom user-defined path generators. By providing a structured and extensible framework, this work establishes a foundation for the rigorous and reproducible assessment of UAV search strategies in complex wilderness environments. Full article

The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G Cognitive Radio Networks (CRNs). This paper introduces a novel hybrid framework combing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Proximal Policy Optimisation (PPO) for multi-objective optimisation in spectrum management. The proposed model balances spectrum efficiency, interference mitigation, energy conservation, collision rate reduction, and QoS maintenance. Evaluation on synthetic and ns-3 datasets shows that the NSGA-II and PPO hybrid consistently outperforms the random, greedy, and stand-alone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s Fairness Index = 1.0), robust hypervolume convergence (65.1%), up to 12% reduction in PU collision rate, 20% lower interference, and approximately 40% improvement in energy efficiency. These findings validate the framework’s effectiveness in promoting fairness, reliability, and efficiency in 6G wireless communication systems. Full article

This study proposes a biomimetic optimization approach for maritime Search and Rescue (SAR) planning using a Genetic Algorithm (GA). The goal is to maximize the number of detected drifting targets by optimally deploying both official and civilian Search and Rescue Units (SRUs). The proposed method incorporates a POD-adjusted fitness function with collision-avoidance constraints and is enhanced by a greedy initialization strategy. To validate its effectiveness, we compare the GA against a baseline method (EAGD) that combines a (1 + 1)-Evolutionary Algorithm with greedy deployment, across 24 experiments involving 2 realistic maritime scenarios and 12 coverage conditions. Results show that GA consistently achieves higher average fitness and stability, particularly under stress-test settings involving only civilian vessels. The findings underscore the potential of biomimetic algorithms for real-time, flexible, and scalable SAR planning, while highlighting the value of civilian participation in emergency maritime operations. Full article

Optimizing metadata indexing remains critical for enhancing distributed file system performance. The Traditional Log-Structured Merge-Trees (LSM-Trees) architecture, while effective for write-intensive operations, exhibits significant limitations when handling massive metadata workloads, particularly manifesting as suboptimal read performance and substantial indexing overhead. Although existing learned indexes perform well on read-only workloads, they struggle to support modifications such as inserts and updates effectively. This paper proposes SwiftKV, a novel metadata indexing scheme that combines LSM-Tree and learned indexes to address these issues. Firstly, SwiftKV employs a dynamic partition strategy to narrow the metadata search range. Secondly, a two-level learned index block, consisting of Greedy Piecewise Linear Regression (Greedy-PLR) and Linear Regression (LR) models, is leveraged to replace the typical Sorted String Table (SSTable) index block for faster location prediction than binary search. Thirdly, SwiftKV incorporates a load-aware construction mechanism and parallel optimization to minimize training overhead and enhance efficiency. This work bridges the gap between LSM-Trees’ write efficiency and learned indexes’ query performance, offering a scalable and high-performance solution for modern distributed file systems. This paper implements the prototype of SwiftKV based on RocksDB. The experimental results show that it narrows the memory usage of index blocks by 30.06% and reduces read latency by 1.19×~1.60× without affecting write performance. Furthermore, SwiftKV’s two-level learned index achieves a 15.13% reduction in query latency and a 44.03% reduction in memory overhead compared to a single-level model. For all YCSB workloads, SwiftKV outperforms other schemes. Full article

Human–machine collaborative scheduling has been widely applied in the modern manufacturing industry. Traditional scheduling algorithms often rely on frequent rescheduling when new jobs arrive, resulting in low responsiveness and difficulty in meeting the demands of high-paced production scenarios. Aiming at the hybrid human–machine collaborative flexible job shop scheduling problem (HHCFJSP) with random job arrivals, this paper proposes a hybrid algorithm based on improved job insertion strategy (HAIJI) dedicated to coping with sudden job insertion demands during the scheduling process. The algorithm constructs a two-dimensional evaluation vector based on minimum scheduling delay and residual scheduling flexibility to jointly assess potential insertion positions for each operation. A non-dominated sorting mechanism is employed to identify a set of promising insertion candidates, which are further evaluated using a tailored evaluation function. During the construction of the insertion plan, an A*-inspired greedy search strategy is adopted to guide the search process, followed by a backtracking mechanism to recover the globally optimal insertion sequence. Finally, the proposed algorithm is applied to the pre-scheduling phase and the dynamic rescheduling phase of a hybrid human–machine collaborative flexible job shop. Experimental results demonstrate that the proposed method achieves higher scheduling efficiency and stability in both stages and outperforms benchmark algorithms in terms of makespan and response time. Full article

To address the logistical challenges of traffic congestion and environmental concerns associated with carbon emissions in last-mile delivery, this paper explores the potential of vehicle–drone cooperative delivery. The existing studies are predominantly confined to single-drone scenarios, failing to simultaneously consider the constraints of drone payload capacity and endurance. This limitation leads to task allocation imbalance in large-scale customer deliveries and low distribution efficiency. Firstly, a mathematical model for vehicle–multi-drone collaborative delivery with payload and endurance constraint (VMDCD-PEC) is proposed. Secondly, an improved genetic algorithm (IGA) is developed, as follows: 1. designing a hybrid selection strategy to achieve symmetrical equilibrium between exploration and exploitation by adjusting the weights of dynamic fitness–distance balance, greedy selection, and random selection; and 2. introducing the local search operator composed of gene sequence reversal, single-gene slide-down, and random half-swap to improve the neighborhood quality solution mining efficiency. Finally, the experimental results show that compared with a traditional genetic algorithm (GA) and adaptive large neighborhood search (ALNS), the IGA requires less time to find solutions in various test cases and reduces the average cost of the optimal solution by up to 30%. In addition, an analysis of drone payload sensitivity showed that drone payload capacity is negatively correlated with delivery time, and that larger customer sizes corresponded to higher sensitivity. Full article

Efficient production scheduling is a key challenge in industrial operations and continues to attract significant interest within the field of operations research. This paper investigates a range of methodological approaches designed to solve the permutation flow shop scheduling problem (PFSP) with sequence-dependent setup times (SDST). The main objective is to minimize the total weighted flow time (TWFT) while ensuring a no-wait production environment. The proposed solution strategy is based on using algorithms with a mixed integer linear programming (MILP) formulation, heuristics, and their combination. The heuristics utilized in this paper include an advanced greedy randomized adaptive search procedure (GRASP) based on a priority rule and Hybrid-GRASP-NEH (HGRASP), where Nawaz-Enscore-Ham (NEH) takes place to initiate solutions, based on iterative global and local search methods to refine exploration capabilities and improve solution quality. These approaches were validated using a comprehensive set of experiments across diverse instance sizes that proved the efficiency of HGRASP, with the results showing a high-performance level that closely matched that of the exact MILP approach. Statistical analysis via the Friedman test (χ² = 46.75, p = 7.04 × 10⁻¹¹) confirmed significant performance differences among MILP, GRASP, and HGRASP. While MILP guarantees theoretical optimality, its practical effectiveness was limited by imposed computational time constraints, and HGRASP consistently achieved near-optimal solutions with superior computational efficiency, as demonstrated across diverse instance sizes. Full article

Distributed permutation flowshop scheduling is an NP-hard problem that has become a hot research topic in the fields of optimization and manufacturing in recent years. Multimodal optimization finds multiple global and local optimal solutions of a function. This study proposes a harmony search algorithm with iterative optimization operators to solve the NP-hard problem for multimodal optimization with the objective of makespan minimization. First, the initial solution set is constructed by using a distributed NEH operator. Second, after generating new candidate solutions, efficient iterative optimization operations are applied to optimize these solutions, and the worst solutions in the harmony memory (HM) are replaced. Finally, the solutions that satisfy multimodal optimization of the harmony memory are obtained when the stopping condition of the algorithm is met. The constructed algorithm is compared with three meta-heuristics: the iterative greedy meta-heuristic algorithm with a bounded search strategy, the improved Jaya algorithm, and the novel evolutionary algorithm, on 600 newly generated datasets. The results show that the proposed method outperforms the three compared algorithms and is applicable to solving distributed permutation flowshop scheduling problems in practice. Full article

This study tackles scheduling challenges in multi-product assembly within distributed manufacturing, where components are produced simultaneously at dedicated factories (single capacity per site) and assembled centrally upon completion. To minimize makespan and maximum tardiness, we design a symmetry-exploiting enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) integrated with Q-learning. Our approach systematically explores the solution space using dual symmetric variable neighborhood search (VNS) strategies and two novel crossover operators that enhance solution-space symmetry and genetic diversity. An ε-greedy policy leveraging maximum Q-values guides the symmetry-aware search toward optimality while enabling strategic exploration. We validate an MILP model (Gurobi-implemented) and present our symmetry-refined algorithm against six heuristics. Multi-scale experiments confirm superiority, with Friedman tests demonstrating statistically significant gains over benchmarks, providing actionable insights for efficient distributed manufacturing scheduling. Full article

This paper presents a comprehensive deep reinforcement learning (DRL) framework for developing strategic trading models tailored to the Indian NIFTY 50 index, leveraging the temporal and nonlinear nature of financial markets. Three advanced DRL architectures deep Q-network (DQN), double deep Q-network (DDQN), and dueling double deep Q-network (Dueling DDQN) were implemented and empirically evaluated. Using a decade-long dataset of 15-min interval OHLC data enriched with technical indicators such as the exponential moving average (EMA), pivot points, and multiple supertrend configurations, the models were trained using prioritized experience replay, epsilon-greedy exploration strategies, and softmax sampling mechanisms. A test set comprising one year of unseen data (May 2024–April 2025) was used to assess generalization performance across key financial metrics, including Sharpe ratio, profit factor, win rate, and trade frequency. Each architecture was analyzed in three progressively sophisticated variants incorporating enhancements in reward shaping, exploration–exploitation balancing, and penalty-based trade constraints. DDQN V3 achieved a Sharpe ratio of 0.7394, a 73.33% win rate, and a 16.58 profit factor across 15 trades, indicating strong volatility-adjusted suitability for real-world deployment. In contrast, the Dueling DDQN V3 achieved a high Sharpe ratio of 1.2278 and a 100% win rate but with only three trades, indicating an excessive conservatism. The DQN V1 model served as a strong baseline, outperforming passive strategies but exhibiting limitations due to Q-value overestimation. The novelty of this work lies in its systematic exploration of DRL variants integrated with enhanced exploration mechanisms and reward–penalty structures, rigorously applied to high-frequency trading on the NIFTY 50 index within an emerging market context. Our findings underscore the critical importance of architectural refinements, dynamic exploration strategies, and trade regularization in stabilizing learning and enhancing profitability in DRL-based intelligent trading systems. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

The Slime Mould Algorithm (SMA) is a widely used swarm intelligence algorithm. Encouraged by the theory of no free lunch and the inherent shortcomings of the SMA, this work proposes a new variant of the SMA, called the BWSMA, in which three improvement mechanisms are integrated. The adaptive greedy mechanism is used to accelerate the convergence of the algorithm and avoid ineffective updates. The best–worst management strategy improves the quality of the population and increases its search capability. The stagnant replacement mechanism prevents the algorithm from falling into a local optimum by replacing stalled individuals. In order to verify the effectiveness of the proposed method, this paper conducts a full range of experiments on the CEC2018 test suite and the CEC2022 test suite and compares BWSMA with three derived algorithms, eight SMA variants, and eight other improved algorithms. The experimental results are analyzed using the Wilcoxon rank-sum test, the Friedman test, and the Nemenyi test. The results indicate that the BWSMA significantly outperforms these compared algorithms. In the comparison with the SMA variants, the BWSMA obtained average rankings of 1.414, 1.138, 1.069, and 1.414. In comparison with other improved algorithms, the BWSMA obtained average rankings of 2.583 and 1.833. Finally, the applicability of the BWSMA is further validated through two structural optimization problems. In conclusion, the proposed BWSMA is a promising algorithm with excellent search accuracy and robustness. Full article

Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. Full article

This study presents a graph knowledge-enhanced iterated greedy algorithm that incorporates dual directional decoding strategies, disjunctive graphs, neighborhood structures, and a rapid evaluation method to demonstrate its superior performance for the hybrid flowshop scheduling problem (HFSP). The proposed algorithm addresses the trade-off between the finite solution space corresponding to solution representation and the search space for the optimal solution, as well as constructs a decision mechanism to determine which search operator should be used in different search stages to minimize the occurrence of futile searching and the low computational efficiency caused by individuals conducting unordered neighborhood searches. The algorithm employs dual decoding with a novel disturbance operation to generate initial solutions and expand the search space. The derivation of the critical path and the design of neighborhood structures based on it provide a clear direction for identifying and prioritizing operations that have a significant impact on the objective. The use of a disjunctive graph provides a clear depiction of the detailed changes in the job sequence both before and after the neighborhood searches, providing a comprehensive view of the operational sequence transformations. By integrating the rapid evaluation technique, it becomes feasible to identify promising regions within a constrained timeframe. The numerical evaluation with well-known benchmarks verifies that the performance of the graph knowledge-enhanced algorithm is superior to that of a prior algorithm, and seeks new best solutions for 183 hard instances. Full article