An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning

Biomimetics, Год журнала: 2025, Номер 10(1), С. 23 - 23

Опубликована: Янв. 3, 2025

To address the challenges of slow convergence speed, poor precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, mathematical model is used to construct terrain environment, multi-constraint cost established, framing as multidimensional function optimization problem. Second, recognizing sensitivity population diversity Logistic Chaotic Mapping traditional (HEOA), opposition-based learning strategy employed uniformly initialize distribution, thereby enhancing algorithm’s global capability. Additionally, guidance factor introduced into leader role during development stage, providing clear directionality search process, which increases probability selecting optimal paths accelerates speed. Furthermore, loser update strategy, adaptive t-distribution perturbation utilized its small mutation amplitude, enhances capability robustness algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance precision algorithm stability, with IHEOA, integrates multiple strategies, performing particularly well. Experimental comparative research three different environments five algorithms shows IHEOA not only exhibits excellent performance terms speed but also generates superior while demonstrating exceptional complex environments. These results validate significant advantages proposed improved addressing UAV challenges.

Язык: Английский

---

Reinforcement learning guided auto-select optimization algorithm for feature selection

Expert Systems with Applications, Год журнала: 2025, Номер 268, С. 126320 - 126320

Опубликована: Янв. 5, 2025

Язык: Английский

---

A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network

Sensors, Год журнала: 2025, Номер 25(10), С. 3151 - 3151

Опубликована: Май 16, 2025

Combustible gas leakage remains a critical safety concern in industrial and indoor environments, necessitating the development of detection systems that are both accurate practically deployable. This study presents wireless system integrates sensor array, low-power microcontroller with Zigbee-based communication, Back Propagation (BP) neural network optimized via sequential hybrid strategy. Specifically, Particle Swarm Optimization (PSO) is employed for global parameter initialization, followed by Dung Beetle (DBO) local refinement, jointly enhancing network’s convergence speed predictive precision. Experimental results confirm proposed PSO-DBO-BP model achieves high correlation coefficients (above 0.997) low mean relative errors (below 0.25%) all monitored gases, including hydrogen, carbon monoxide, alkanes, smog. The exhibits strong robustness handling nonlinear responses cross-sensitivity effects across multiple sensors, demonstrating its effectiveness complex scenarios under laboratory conditions within embedded networks.

Язык: Английский

---

Improved Sparrow Search Algorithm Based on Multistrategy Collaborative Optimization Performance and Path Planning Applications

Processes, Год журнала: 2024, Номер 12(12), С. 2775 - 2775

Опубликована: Дек. 5, 2024

To address the problems of limited population diversity and a tendency to converge prematurely local optima in original sparrow search algorithm (SSA), an improved (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies enhance performance: introducing one-dimensional composite chaotic mapping SPM generate initial population, thus enriching diversity; dung beetle dancing behavior strategy strengthen algorithm’s ability jump out optima; integrating adaptive t-variation improvement balance global exploration exploitation capabilities. Through experiments with 23 benchmark test functions comparison algorithms such as PSO, GWO, WOA, SSA, advantages convergence speed accuracy are verified. In application robot path planning, compared exhibits shorter lengths, fewer turnings, higher planning efficiency both single-target point multi-target planning. Especially obstacle rate increases, can more effectively find shortest path. Its traversal order different from that making planned smoother intersections. The results show has significant superiority performance applications.

Язык: Английский

---

Optimizing time–cost in construction projects using modified quasi-opposition learning-based multi-objective Jaya optimizer and multi-criteria decision-making methods

Asian Journal of Civil Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 15, 2024

Язык: Английский

---