Particle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. Nonetheless, PSO has its limitations, notably its propensity for slow convergence. Traditionally, PSO operates by guiding swarms through positions determined by their initial velocities and acceleration components, encompassing cognitive and social information. In pursuit of expedited convergence, we introduce a novel approach: the Cognitive and Social Information-Based Hyperbolic Tangent Particle Swarm Optimization (HT-PSO) algorithm. This innovation draws inspiration from the activation functions employed in neural networks, with the singular aim of accelerating convergence. To combat the issue of slow convergence, we reengineer the cognitive and social acceleration coefficients of the PSO algorithm, leveraging the power of the hyperbolic tangent function. This strategic adjustment fosters a dynamic balance between exploration and exploitation, unleashing PSO's full potential. Our experimental trials encompass thirteen benchmark functions spanning unimodal and multimodal landscapes. Besides that, the proposed algorithm is also applied to different UAV path planning scenarios, underscoring its real-world relevance. The outcomes underscore the prowess of HT-PSO, showcasing significantly better convergence rates compared to the state-of-the-art.

Path planning is a crucial technology and challenge in various fields, including robotics, autonomous systems, and intelligent transportation systems. The Particle Swarm Optimization (PSO) algorithm is widely used for optimization problems due to its simplicity and efficiency. However, despite its potential, PSO has notable limitations, such as slow convergence, susceptibility to local minima, and suboptimal efficiency, which restrict its application. This paper proposed a novel strategy called the Distance-Dependent Sigmoidal Inertia Weight PSO (DSI-PSO) algorithm to address slow convergence in path planning optimization. This innovative algorithm is inspired by neural network activation functions to achieve faster convergence. In DSI-PSO, each particle computes a distance metric and leverages a sigmoid function to adaptively update its inertia weight. Beyond improving convergence speed, this approach also addresses path-planning challenges in autonomous vehicles. In intelligent transportation systems, effective path planning enables smart vehicles to navigate, select optimal routes, and make informed decisions. The goal is to identify a collision-free path that satisfies key criteria such as shortest distance and smoothness. This methodology not only accelerates convergence but also maintains a balance between exploration and exploitation. The effectiveness of the DSI-PSO algorithm is tested using thirteen distinct unimodal and multimodal benchmark functions, serving as rigorous test cases. Additionally, the algorithm’s real-world applicability is evaluated through a smart vehicle simulation, assessing its ability to identify safe and efficient paths while minimizing overall path length. The results demonstrate the superiority of the DSI-PSO algorithm over conventional PSO approaches, with significantly enhanced convergence rates and robust optimization performance.

In IoT-based wireless sensor networks (WSNs), clustering is one of the most effective energy-saving approaches to optimizing the life cycle of networks. The primary challenge in the multi-hop technique arises when cluster heads (CHs) located near the base station quickly deplete their energy due to the high volume of inter-cluster relay traffic. Therefore, a clustering protocol needs to be both fault-tolerant and energy-efficient. This paper proposes a particle swarm optimization (PSO) based clustering algorithm that incorporates a double-exponential adaptive inertia weight update to dynamically balance global exploration and local exploitation. This adaptive mechanism mitigates premature convergence, improves the accuracy of CH selection, and enhances overall energy efficiency in the network. By classifying sensor nodes into cluster members, CHs, and free nodes, this intelligent swarm-based algorithm is used to maximize CH selection in IoT-WSNs. The residual energy of the nodes is utilized to dynamically determine the optimal number of clusters during cluster formation. Subsequently, the algorithm allocates nodes into clusters of uniform size, ensuring that high-energy nodes are efficiently assigned as CHs based on their location data. The proposed algorithm is evaluated across various network topologies and compared with four benchmark WSN routing algorithms: LEACH, LEACH-FL, LEACH-FC, and KM-PSO. The results indicate that the proposed algorithm outperforms LEACH by approximately 28%, LEACH-FL and LEACH-FC by around 20%, and KM-PSO by nearly 10% in terms of node survivability and network longevity. Besides that, the proposed algorithm enhances energy efficiency in IoT-based WSNs by selecting cluster heads with high neighborhood density and strategically using intermediate nodes, thereby reducing communication distances, balancing transmission load, and extending network lifetime. These findings highlight the effectiveness of the proposed algorithm in enhancing energy efficiency and prolonging the operational lifetime of IoT-enabled WSNs.

Optimizing sensor node coverage remains a central challenge in wireless sensor networks (WSNs), where premature convergence and suboptimal solutions in traditional optimization methods often lead to coverage gaps and uneven node distribution. To address these issues, this paper presents a novel velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO) algorithm that integrates dynamic mechanisms to enhance population diversity, guide the search process more effectively, and reduce uncovered areas. The proposed algorithm is evaluated through extensive simulations across multiple WSN deployment scenarios with varying node densities, sensing ranges, and monitoring area sizes. Comparative results demonstrate that the approach consistently outperforms several widely used metaheuristic algorithms, achieving faster convergence, better global exploration, and significantly improved coverage performance. On average, the proposed method yields up to 14.71% higher coverage rates than baseline techniques. These findings underscore the algorithm’s robustness and suitability for efficient and scalable WSN deployments.

As cellular networks evolve into the backbone of global connectivity for 95% of the population, their ubiquity creates an unprecedented surface for sophisticated aerial disruption. This paper dissects the strategic deployment of Aerial Jammers (AJs) a rising security threat capable of severely degrading terrestrial cellular performance. We propose a novel tactical framework to intensify AJ interference within environments comprising multiple ground based base stations (BSs) and users. Central to our study is the introduction of network throughput loss as a formal metric for quantifying adversarial impact. We formulate a joint optimization problem to determine the AJ’s optimal spatial coordinates and power allocation required to maximize this loss. To navigate the resulting non-convex landscape, we develop a Particle Swarm Optimization (PSO) based algorithm that efficiently identifies the most disruptive jammer configurations. Comparative analysis reveals that this joint optimization approach significantly outperforms conventional interference strategies, achieving up to a 26% increase in throughput loss at peak user transmit power. These results expose critical vulnerabilities in modern cellular architectures and provide a rigorous benchmark for future network defense.

Beyond Line of Sight (BLOS) communication stands as an indispensable element within defense communication strategies, facilitating information exchange in scenarios where traditional Line of Sight (LOS) methodologies encounter obstruction. This article delves into the forefront of technologies driving BLOS communication, emphasizing advanced systems like phantom networks, nanonetworks, aerial relays, and satellite-based defense communication. Moreover, we present a practical use case of UAV path planning using optimization techniques amidst radar-threat war zones that add concrete relevance, underscoring the tangible applications of BLOS defense communication systems. Additionally, we present several future research directions for BLOS communication in defense systems, such as resilience enhancement, the integration of heterogeneous networks, management of contested spectrums, advancements in multimedia communication, adaptive methodologies, and the burgeoning domain of the Internet of Military Things (IoMT). This exploration of BLOS technologies and their applications lays the groundwork for synergistic collaboration between industry and academia, fostering innovation in defense communication paradigms.