Aug 2026· Telecommunications Systems· Vol 89· 0 citations· 54 references
TL;DR
Findings prove that SBOA is an effective and scalable clustering platform that can be applied to real-time FANET deployments during disaster recovery, surveillance, and monitoring operations in large regions.
A multi-objective intelligent optimization algorithm, the wise wayfinding algorithm (WWA), which integrates mechanisms from non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (MOPSO) and exhibits favorable convergence and robust solution distribution on standard benchmark functions (ZDT, DTLZ, UF).
Wenguang Yang, Yi-Kang Du, Lianhai Lin· Memetic Computing· 0 citations
Heterogeneous Wireless Sensor Networks (HWSNs) face conspicuous challenges in maximizing network lifetime due to uncurbed power utilization depletion at sensor nodes (SNs) and cluster heads (CHs). This study presents an optimal Multi-Mobile Sink-based Clustering and Routing (MSCR) control strategy using the multi-objective Crow Search Algorithm (CSA) to address resource-constrained node energy-efficiency challenges. The proposed CSA-based MSCR model enhances deployed node’s performance by intelligently coordinating SNs, CHs, and mobile sink (MS) to optimize the data acquisition process while reducing energy consumption. The CSA strategy is deployed for two crucial optimization operations: constructing optimal mobile sink trajectories and selecting energy-efficient cluster heads under limited resources and harsh environmental circumstances. Performance analysis compares the CSA-based model against established traditional metaheuristic methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). Large-scale simulations conducted under various network and harsh environmental conditions demonstrate significant improvements in critical performance metrics. The CSA-based MSCR approach achieves a 36% reduction in power consumption, 49% accelerated cluster formation and cluster head (CH) selection, and 42% improvements in data delivery efficiency compared to conventional approaches. Furthermore, the proposed model extends average network lifetime by 45% while maintaining data accuracy above 97%. The results endorse the usefulness of the CSA optimization strategy in solving composite multi-objective optimization problems in wireless sensor networks. This work contributes a strong and scalable solution for next-generation IoT applications requiring energy-efficient data collection in challenging deployment environments. The outcome highlights the strengths of metaheuristic algorithms like CSA in advancing MSCR control for WSNs, offering a promising alternative to traditional approaches for improving the data delivery and lifetime of the sensor networks.
Sagar Mekala, Shahu Chatrapati· ITEGAM- Journal of Engineeri...· 0 citations
The adoption of a new communication paradigm is getting attention in the research world, where Flying Ad Hoc Networks (FANETs) have been deemed a viable approach for supporting coordinated operations of multiple Unmanned Aerial Vehicles (UAVs) in situations characterized by dynamic environments and the absence of infrastructure. Taking into consideration these drawbacks, in this paper, a novel and up-to-date AI-Based Mobility and Topology Management Framework for Flying Ad Hoc Networks via Hybrid Bio-Inspired Optimization is proposed. The proposed systems combine a Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) inspired model, introducing a novel hybrid model, with Artificial Intelligence techniques to provide a dynamic framework for optimizing UAV mobility patterns, topology formation, and communication paths within the proposed framework. Predictive mobility analysis using AI to make networks more adaptable and minimize topology changes. In addition, the hybrid optimization method will optimize the routing efficiency, reduce the communication overhead, and increase the packet delivery efficiency between nodes in the highly dynamic FANET environment. Results of experimental analysis prove that the proposed scheme has a better PDR of 96.4%, lower EED or end-to-end delay of 31%, and better topology stability that performs better than the traditional mobility management approaches with respect to reducing energy consumption.
Anshu Vashisth, Gagandeep Kaur, Ruhi Saxena et al.· 2026 7th International Confe...· 0 citations
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
In Mobile Ad Hoc Networks (MANETs), choosing an efficient cluster-head (CH) is very important to ensure network stability, scalability and communication efficiency. However, existing clustering algorithms are prone to premature convergence, unstable cluster formation, high re-affiliation rates and high control-message overhead under dynamic network conditions. In order to solve these problems, this paper presents a Chaos-Simplex Elephant Herding Optimization (CS-EHO) algorithm for the cluster-head selection in MANETs. The proposed approach employs chaotic population initialization to increase the diversity of search and avoid the local optima, and the simplex-based local search to improve the exploitation capability and the convergence accuracy. The performance of the proposed CS-EHO is compared with GWOCA, EHO, HBA, MPSO, ABC and DGA using four clustering metrics, namely, average number of clusters, cluster lifetime, re-affiliation rate and control-message overhead, for different network sizes, node speeds and transmission ranges. The experimental results show that CS-EHO always outperforms all benchmark algorithms. For a network size of 500 nodes with a transmission range of 100 m, CS-EHO produced only 26 clusters in comparison with 32, 37, 54, 59, 65 and 102 clusters for GWOCA, EHO, HBA, MPSO, ABC and DGA respectively. CS-EHO achieved cluster lifetime of 79% and 80% for transmission ranges of 100 m, 200 m respectively with a low re-affiliation rate of 0.0372 and control overhead of 0.0032 under high mobility (80 km/h). The hybridization of chaotic exploration and simplex based exploitation significantly improves the cluster-head selection ability of EHO. The proposed CS-EHO framework gives rise to more stable clusters, reduces the cost of cluster maintenance, lowers the communication overhead and improves the scalability of the network. Therefore, CS-EHO is an effective and robust clustering solution for highly dynamic MANET scenarios.
U. S, P. K· International Research Journ...· 0 citations
To address the inherent limitations of existing metaheuristic algorithms in solving complex three-dimensional (3D) unmanned aerial vehicle (UAV) path planning problems, such as premature convergence and weak adaptability to multi-dimensional flight constraints, this paper proposes a novel Scallop Optimizer (SPO). Inspired by the composite survival behaviors of scallops, including filter-feeding, zigzag predation evasion, group defense, and byssus memory, SPO integrates five coordinated search modules: Adaptive Energy State Switching Mechanism (AESSM), Filter-Feeding Mechanism (FFM), Leaping-Zigzag Evasion Mechanism (LM-ZM), Group Defense-Dispersion Mechanism (GDDM), and Byssus Historical Optimal Memory Mechanism (BHM). AESSM dynamically adjusts the energy state of each individual to switch between global exploration and local exploitation; FFM enhances fine local search accuracy; LM-ZM improves the capability to escape local optima; GDDM maintains population diversity in real time; and BHM avoids redundant repeated search. Comprehensive numerical experiments on the IEEE CEC 2017 (D = 30) and CEC 2022 (D = 10 and 20) benchmark suites demonstrate that SPO achieves minimum average rankings of 1.14, 1.58, and 1.50 across three test sets, and significantly outperforms 14 mainstream metaheuristic algorithms, including PSO, DE, SHADE, and DBO, on most test functions. Further ablation experiments verify that the GDDM module contributes the most to performance improvement, with an average ranking degradation of 6.00 upon its removal. When applied to multi-constraint 3D UAV path planning, SPO obtains a minimal total flight cost of 1298.32, reducing the comprehensive path cost by 40.41% compared to the worst-performing optimizer, and generates collision-free, smooth trajectories with zero terrain and threat penalty costs. Statistical Wilcoxon signed-rank and Cohen’s d tests further validate the significant statistical superiority of SPO. The proposed SPO provides an effective alternative optimization tool for complex constrained engineering optimization tasks such as 3D UAV path planning.