Simulation experiments comparing AACOES with particle swarm optimization, genetic algorithms, and traditional ant colony algorithms demonstrate its significant advantages in optimizing both single-unit performance and total flight distance, coupled with more stable convergence.
Abstract
This paper addresses the complex scheduling optimization problem in multi-UAV collaborative power line inspection by proposing an Adaptive Ant Colony Optimization Algorithm with Elite Strategy (AACOES). The study comprehensively considers multiple practical constraints, including UAV flight characteristics, battery endurance, and external wind conditions, to construct a scheduling optimization model closely aligned with real-world inspection operations. To overcome limitations in convergence speed and global search capability inherent in traditional ant colony algorithms, the proposed method incorporates an elite strategy and adaptive adjustment factors. It optimizes pheromone update rules, effectively enhancing colony diversity and accelerating convergence. This enables efficient identification of near-optimal solutions under multiple constraints. Simulation experiments comparing AACOES with particle swarm optimization, genetic algorithms, and traditional ant colony algorithms demonstrate its significant advantages in optimizing both single-unit performance and total flight distance, coupled with more stable convergence. This validates its effectiveness and practicality for multi-UAV collaborative inspection scheduling in complex environments, providing an efficient and reliable technical approach for real-world applications such as power line inspections.
Simulation experiments demonstrate that SW-MSACO achieves improved Pareto solution quality and search stability compared with existing heuristic optimization approaches, particularly under large-scale and high-load scenarios, confirming the effectiveness of the proposed framework for complex UAV logistics optimization.
Xin-Yi Chen, Lin Shi, Jian-Yu Li· Algorithms· 0 citations
The dynamic scheduling of ready-mixed concrete constitutes a critical bottleneck in construction automation. Following the design science paradigm and informed by a systematic literature review, this study develops the multistrategy ant colony optimization (MSACO) algorithm, which integrates three mechanisms: adaptive pheromone evaporation, elite ant guidance, and genetic mutation. Empirical validation based on the road network of a major Chinese city (involving four batching plants, six customer sites, and a fleet of seven fuel vehicles and five electric vehicles) demonstrates that MSACO significantly outperforms algorithms including the genetic algorithm, ant colony optimization, particle swarm optimization, and multistrategy adaptive ant colony optimization in terms of solution accuracy, convergence speed, and stability. The proposed algorithm achieves an average reduction in distribution costs of 7.06%, with advantages reaching 10.7% under highly constrained conditions (
p
<
10
−
7
). The main contributions are threefold: it proposes a triple adaptive mechanism tailored for dynamic scheduling scenarios; formulates a mathematical model incorporating plant capacity, load limits, and electric vehicle range; and provides a quantifiable basis for the digital transformation of construction logistics.
Yang Guan, Ge Shi, Jie Yang et al.· Journal of construction engi...· 0 citations
3D path planning is a key technology in fields such as UAV navigation and intelligent inspection, and its planning performance directly impacts mission effectiveness. Traditional ant colony algorithms adopt fixed parameters, which frequently give rise to problems including slow convergence speed, a tendency to fall into local optimal solutions, and in sufficient global search capability in complex 3D environments. To overcome these limitations, this paper presents a triple-adaptive improved ant colony optimization algorithm. By adaptively adjusting the pheromone factor α and the heuristic factor β via a logarithmic function, and by designing a constrained adaptive pheromone evaporation coefficient ρ , the algorithm enhances both global search and local optimization capabilities. Experimental results demonstrate that, compared to the traditional algorithm, the improved algorithm generates smoother paths, with the optimal fitness improved by 15.54% and the algorithm runtime efficiency improved by 24.15%, demonstrating better comprehensive performance in both path quality and computational speed. This effectively overcomes the original deficiencies and meets the requirements of path planning in complex 3D environments.
Yunxiu Wang, Wei Gao, Ruopu Bai et al.· International Conference on...· 0 citations
To address the fundamental trade-off between real-time responsiveness to high-priority missions and long-term overall economic efficiency of the system in multi-UAV dynamic task assignment, we propose a hybrid intelligent scheduling algorithm abbreviated as DPSO-Greedy. The algorithm performs periodic global batch optimization for regular orders using an improved discrete particle swarm optimization (DPSO) method, and realizes instantaneous allocation of emergency orders via an adaptive multi-factor Greedy strategy, thus enabling efficient collaborative processing of differentiated tasks. Targeting the trade-off between real-time response and long-term system efficiency, this paper proposes a hybrid DPSO-Greedy algorithm with decoupled task scheduling mechanisms. Comparative simulation results demonstrate that compared with mainstream metaheuristic algorithms (Greedy, SSA, GWO and RHS), the proposed method reduces the average response time of emergency orders by 33.2–68.2%, achieves an emergency order completion rate exceeding 90%, and improves system load balancing performance by 24–35% in dynamic scenarios characterized by burst and tidal demands. This study provides a promising solution for dynamic UAV assignment problems and offers valuable insights for a broader range of real-time resource collaborative decision-making applications.
Mei You, Huihui Xu, Zhangsong Shi et al.· Drones· 0 citations
The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems.
