Layout Optimization of Irregular Construction Sites Based on SLP and Improved NSGA-II

Improved SA algorithm for masonry layout optimization of building infill walls

This paper proposes a hybrid optimization algorithm that fuses multiple methods to address the weak global exploration ability, frequent local optima, and poor engineering adaptability in masonry layout optimization of building infill walls. The method builds a multilayer cooperative framework. It first uses the Genetic Algorithm to create a diverse population. It then applies Simulated Annealing to perform probabilistic jumping optimization. After that, it introduces sparse A search to verify topological feasibility. It finally relies on a cooperative mechanism of Adaptive Whale Optimization and iterative local search to explore the solution space in depth. Experiments on the simultaneous localization and mapping–building information modeling coupled dataset and the building information modeling component multimodal dataset show that the algorithm reaches a standard block utilization rate of 98.76 percent. It also keeps the cutting loss rate as low as 2.79%and achieves a peak stagger-joint compliance rate of 97.11%. In irregular wall scenarios, it reduces cost by up to 33.87%. The results show that this algorithm improves the optimization quality and engineering applicability of masonry layout and provides reliable technical support for precise construction and efficient material use of building infill walls.

Path Planning Research for Textile Warehouse AGV Based on Integrated Improved A* and DWA Algorithms

To address the limitations of traditional A* algorithms in textile warehouse automated guided vehicle (AGV) navigation, including paths too close to obstacles, low search efficiency caused by redundant nodes, and frequent turning that reduces motion smoothness, this paper proposes an integrated path planning scheme combining an improved A* algorithm and an improved dynamic window approach (DWA). The method is designed for AGV navigation in apparel, silk, and fabric warehousing environments where dense storage layouts, dynamic obstacles, and electromagnetic or wireless sensing constraints require safe and stable motion planning. First, the evaluation function of the A* algorithm is improved by introducing an obstacle-density factor and kinematic constraints, enabling adaptive heuristic weighting, safer child-node screening, and smoother global reference paths. Second, the DWA evaluation function is modified by incorporating global path guidance, obstacle clearance, velocity, and smoothness-related weights, improving local obstacle avoidance decisions under dynamic conditions. Finally, a global-local coordination mechanism is developed so that the improved A* algorithm provides the optimized path skeleton and the improved DWA performs real-time local tracking and dynamic avoidance. Simulation experiments in typical indoor grid environments containing static and dynamic obstacles show that the proposed algorithm reduces planning time by approximately 17%, improves average motion smoothness by about 49%, reduces cumulative turning angles, and maintains safe obstacle clearance. The results demonstrate that the integrated method improves the efficiency, safety, and trajectory quality of textile warehouse AGV navigation.

Station Deployment and Unmanned Aerial Vehicles Path Planning for Wind Farm Inspection Using Adaptive Bees Optimization Algorithm

Beyond a Single Plan: Genetic Algorithms for Flexible Maritime Search and Rescue

In maritime search and rescue (SAR) operations, the estimated location of survivors spreads over time, causing the search area to expand continuously. Deploying limited search and rescue units (SRUs) efficiently is critical, but as the search area grows, the number of possible deployment combinations increases exponentially, making exhaustive search impractical. This study proposes a two-stage metaheuristic-based optimization framework that balances computational efficiency and solution quality. In the first stage, the search area is discretized into a grid using simulated particle diffusion results, and grid cell importance is estimated based on particle distribution to assign limited SRUs to high-priority cells first. In the second stage, SRU deployment within each selected cell is reformulated as a permutation-based matching problem to refine resource allocation. The proposed framework was evaluated across 72 scenarios using genetic algorithm, simulated annealing, particle swarm optimization, and differential evolution. Results show that genetic algorithm consistently outperformed other algorithms under resource-constrained conditions while maintaining high solution diversity, providing multiple high-quality alternatives for practical SAR decision-making.

Exact and Heuristic Methods for Capacitated Path Planning for Agricultural Field Operations

This paper addresses the Capacitated Coverage Path Planning Problem (CPP) arising in agricultural field operations. It aims to determine an efficient sequence of field tracks to be serviced by an agricultural machine subject to limited onboard capacity and refilling constraints, while minimizing non-working distance. The non-working distance is the length traveled by the machinery not performing productive fieldwork. Optimizing how agricultural machinery maneuvers can help reduce operational costs and greenhouse gas (GHG) emissions, while increasing productivity. The problem is formulated as a variant of the constrained Vehicle Routing Problem (VRP), which is NP-hard. A novel exact Integer Linear Programming (ILP) formulation is proposed. We employ a commercial optimizer, Gurobi, to obtain optimal reference solutions for smaller instances. To overcome scalability limitations, a metaheuristic based on "simulated annealing (SA) methodology" is proposed. Our algorithm incorporates customized neighborhood search operators and a capacity-aware route-splitting mechanism to explicitly handle refilling operations. Computational experiments are conducted on real-world field instances. The proposed metaheuristic is evaluated against the exact formulation. The results indicate that SA consistently produces near-optimal solutions with substantially reduced computation times.

Full-Coverage Path Planning for Heterogeneous UUVs Using a Hybrid Detection Point Layout and a Dual-Chromosome Co-Evolutionary Genetic Algorithm

To address the issue of unbalanced path allocation in multi-UUV cooperative operations under inhomogeneous ocean environments during full-coverage search missions, this paper proposes a heterogeneous UUV path planning method that integrates a hybrid waypoint deployment strategy with a dual-chromosome co-evolutionary genetic algorithm. First, heterogeneous UUVs are adaptively assigned to sub-regions according to the search value of the sea area, and a combination of Poisson sampling and Voronoi iterative refinement is adopted to complete the layout of detection points. Subsequently, connectivity-constrained K-means clustering is introduced to decompose the multi-traveling salesman problem (MTSP) into several independent TSP sub-problems. Finally, a dual-chromosome encoding scheme for task sequences and split points is designed, and a penalty matrix is incorporated into the fitness function to account for obstacle avoidance constraints, thereby establishing an integrated genetic-algorithm-based solution framework that incorporates both decomposition and obstacle avoidance. Simulation results demonstrate that the proposed method reduces the number of planned detection points by 12.4%, 12.8%, and 9.3% compared with baseline methods in circular, rectangular, and irregular sea areas, respectively, while the optimal path lengths are shortened by 5.8%, 4.5%, and 5.9%. Moreover, the cooperative mission time with four UUVs is reduced by 73.9%, 72.7%, and 71.2% relative to a single UUV, demonstrating an approximately linear speedup relative to the number of UUVs. Convergence analysis and extended experiments on 15 instances further confirm the algorithm’s solution stability and robustness under varying regional scales, shapes, and obstacle configurations. These results validate that the proposed approach not only reduces the number of deployment points and path cost, but also effectively balances obstacle avoidance and multi-robot load distribution.