Mosquitoes mostly mate in the context of swarms: to facilitate encounters with females, males form disordered aggregations over a visual marker, which serves as a positional reference. While the relevance of this visual marker for swarming activity has been largely addressed, it is still poorly understood whether, in addition to an individual's response to environmental stimuli, insects in a swarm interact with each other, giving rise to a collective behavior. Here, with a dataset comprising three-dimensional trajectories of 30 laboratory swarms of different sizes (ranging from 80 to 400 mosquitoes), we investigate swarming behavior of Anopheles gambiae mosquitoes. We find that individual speed fluctuations are strongly correlated in space, meaning that mosquitoes in close proximity tend to display similar deviations from the group average, effectively flying at a similar speed, although no such correlation is observed in the flight direction. With a series of targeted tests, we prove that this correlation is not compatible with a random arrangement of individuals, nor with random fluctuations of individual speeds, thereby providing empirical evidence of an effective male-male interaction at play in our swarms.
Marine and coastal ecosystems are among the least observable yet most rapidly changing environments, where climate impacts, pollution, and biodiversity loss demand monitoring and intervention at scales that manual sampling and single-robot deployments cannot sustain. This paper argues for a conceptual shift in ecological monitoring and restoration toward networked robotic ecosystems, adopting cooperative swarms of autonomous aquatic robots coupled to in-situ digital twins and human-in-the-loop supervision. We use the REMORA project as a concrete instantiation of this paradigm, outlining an integrated architecture in which multiple low-cost, persistent robots perform distributed sensing and targeted interventions, while a continuously updated digital twin fuses multi-source data to support predictive assessment, "what-if" scenario exploration, and decision support. A dedicated human-swarm interaction layer enables non-roboticist stakeholders (e.g., environmental managers and aquaculture operators) to specify intent, manage exceptions, and maintain trust without micromanaging individual units. By linking advances in swarm intelligence, sensor integration, AI-enabled analysis, and interactive decision-making, the proposed approach targets durable, scalable, and socially relevant solutions for ecological monitoring and ecosystem management, spanning aquaculture, marinas, and broader coastal environments, and provides a roadmap for translating these technologies from controlled trials to sustained field operations.
Enzymatic nanomotors (EMNMs) represent an emerging class of intelligent nanosystems that exploit enzymatic biocatalysis to generate autonomous motion within biological environments, including complex cellular and tissue contexts within living organisms. Owing to their ability to utilize endogenous biofuels, high biocompatibility, and capacity for targeted propulsion, EMNMs have demonstrated considerable potential in diverse biomedical applications. These include targeted drug delivery, cancer therapy, diagnostics, and bioimaging, as well as the traversal of biological barriers. This review comprehensively discusses the mechanisms underlying enzyme-driven propulsion, nanomotor design strategies, and their current and prospective applications in medicine, while also addressing major challenges associated with enzymatic stability, biocompatibility, motion control, and clinical safety. Furthermore, future perspectives are highlighted, including enzyme cascade systems, intelligent nanomotor swarms, biodegradable materials, and strategies facilitating clinical translation. As a representative example of practical application, curcumin was employed as a model therapeutic agent due to its well-established anticancer, anti-inflammatory, and antioxidant properties, enabling evaluation of the nanomotors' capability for controlled, pH-responsive release of therapeutic cargo. Nanophytomedicine enhances the therapeutic efficacy of phytochemicals by improving their stability, bioavailability, and targeted delivery through nanocarrier systems. The integration of phytotherapy with nanotechnology offers promising opportunities for the development of safer and more effective therapeutic strategies.
Rorqual whales (Balaenopteridae) grow to exceptional size by lunging to engulf entire swarms of fish or krill, then rapidly concentrating prey using a baleen filter. A 20-m-long fin whale can engulf 60 m3 of water and 144 kg of krill in a single gulp and expel the water in 31 s, allowing the animal to make six lunges in a single 8 min dive. But there is a problem hidden in this apparently efficient scheme. Spread uniformly across the 2.9 m2 of the baleen window, 144 kg of krill forms a layer 6.3 cm thick. If the accumulated krill clog the filter, it could increase the time needed to expel water, decreasing the rate at which lunges could be made, and negatively impacting foraging efficiency. We devised a model that incorporates measurements of the hydraulic resistance of accumulating krill and the pressure applied to water in the buccal pouch by contraction of muscles and elastic rebound of blubber in the pouch's walls. Given the stress typical of mammalian muscle, the model suggests that approximately 16 min would be required to empty the buccal pouch of an average gulp of krill - twice the duration of typical foraging dives - severely reducing feeding efficiency. Filter time could be reduced to the observed 31 s if a fraction of the baleen window is maintained clear of krill, but it is unclear how that might be achieved. Important questions remain as to how baleen whales feed.
Wavelike motion mediated by chemotactic signaling occurs in various biological phenomena including neutrophil swarms, wound healing, and amoeba aggregates. However, the macroscopic transition from independent to collective cellular behavior remains unclear, including how to quantify the response of individual cells to a developing chemotactic wave. Recent advances in molecular imaging allow concurrent observation of cyclic adenosine monophosphate (cAMP) concentrations and cell movement at individual resolution. Employing particle image velocimetry (PIV), a scheme to extract Eulerian velocity vector fields in fluids, we derived velocity fields at different Gaussian blurring levels and found that while original fluorescent images reflect cell movement, blurred versions highlight cAMP wave propagation. We identified the phase of cAMP signal wave dynamics and analyzed the interplay between single-cell motility and cAMP wave development. The extracted velocity fields at single-cell resolution show an almost antipodal relationship to those of the cAMP wave characterized by the blurred image, with an angle close to 180° during the rise of the wave when the spiral wave is well developed. Furthermore, single-cell dynamics collectively move toward the crest of the coming cAMP wave but rest (with randomized directionality) in the troughs between waves, akin to "surfing of the collectives".
Aiming at the problem of matching scarce resources among donors, recipients, and medical institutions in organ transplantation, a stable three-sided matching method is proposed. Firstly, in view of the preference structure characteristics of the problems in the context of organ transplantation, a mixed preference structure from the three-sided matching problem is introduced for description, and the stability conditions under this structure are also provided, intuitionistic fuzzy information was utilized to quantify uncertain indicators during organ transplantation. The Erlang distribution was introduced to describe the randomness of the occurrence of organ donors. Coping strategies for possible false reporting behaviors of organ recipients were designed, and a three-sided matching model of donors, recipients, and medical institutions was constructed. And proved the stability of this matching; Secondly, based on the NSGA-III algorithm, the initial search strategy of the Bird Swarm algorithm was introduced, a new mutation method was designed, and its feasibility was theoretically analyzed. Finally, the feasibility of the model and algorithm was verified through simulation examples in the context of organ transplantation, and the performance of the algorithm was analyzed and verified in combination with algorithm complexity, effectiveness, and ablation experiments. The experimental results show that the model and algorithm can effectively improve the stability of the matching results and increase the efficiency of resource allocation.
In cage aquaculture, precise estimation of fish biomass is critically important for determining appropriate feeding strategies and evaluating production capacity. Currently, prevailing fish counting approaches heavily rely on acoustic or optical technologies. However, the accuracy and reliability of the obtained data are largely compromised by factors such as fish occlusion and water turbidity in practical cage farming environments. To address this limitation, this study proposed a novel method for estimating fish population size deduced from dynamic feeding information, based on the model integrated environmental and biological factors, feed intake and biomass. A 10-week feeding experiment was carried out to collect multidimensional data including feed intake, growth parameters, and environmental variables to construct a dataset correlating feeding amount with primary influential factors. Herein a bioenergetics-informed radial basis function neural network, optimized via particle swarm optimization (BE-PSO-RBF), was developed based on those empirical data. Model validation using 47 independent test samples showed that the hybrid model achieved a mean absolute error (MAE) of 26.82, a root mean square error (RMSE) of 35.62, and a mean absolute percentage error (MAPE) of 4.14%, confirming its robust generalization performance. These findings suggest that feed-intake-based population estimation may provide a feasible complementary approach for fish population assessment under cage farming conditions similar to those investigated in this study.
This paper proposes a day-ahead scheduling framework to analyze and optimize the impact of the coordinated active and reactive power management of wind turbines (WTs) and battery energy storage systems (BESSs) on the energy losses and CO2 emissions of AC microgrids (MGs). Within this framework, the BESS plays a central role by absorbing surplus renewable generation, mitigating curtailment, supporting voltage regulation, and ensuring a stable and reliable dispatch over a 24-hour horizon. A population-based genetic algorithm (PGA) is proposed as the main solution methodology, while particle swarm optimization (PSO) and the multiverse optimizer (MVO) are employed as benchmark methods for comparison. To ensure a fair assessment, all optimization techniques are implemented under the same parallel processing scheme, using the same decision-variable encoding, feasibility correction procedure, and hourly sequential AC power-flow method. The objective is to minimize network energy losses and CO2 emissions under both grid-connected and islanded operating modes. The proposed methodology is validated on 33-node and 69-node MGs, both evaluated under variable demand and wind-generation scenarios to capture the uncertainty and temporal variability associated with renewable production and load behavior. In addition, the BESS model includes charging/discharging efficiency, self-discharge effects, and battery lifetime assessment under the proposed operating scenarios, allowing a more realistic representation of storage performance. The optimization methods are evaluated over 100 independent runs using the best solution, average solution, standard deviation, and computational time as performance indicators. The results show that the proposed PGA provides the most robust and repeatable performance, while also highlighting the operational contribution of the BESS, reducing renewable curtailment, and guaranteeing compliance with all technical constraints, under deterministic baseline operation and under uncertain time-varying operating conditions in both test systems.
Over geological time, the growth of the ocean floor involves magmatic and tectonic extension1 at mid-ocean ridges (MORs). Because seismogeodetic monitoring of these submarine plate boundaries remains challenging2-7, little is known about how these systems operate on yearly timescales. Here we report the first, to our knowledge, in situ observation of a rifting event at a MOR segment that combines hydroacoustic, direct-path ranging and bottom-pressure measurements, with repeated seafloor mapping. This event started on 26 April 2024 at the axis of the Southeast Indian Ridge (SEIR) near 37° S, two months after instruments had been deployed across the ridge axis and nearby Amsterdam transform fault (TF). The event began as a rapidly migrating swarm of extensional seismicity along the axial valley. It caused 4 m of subsidence of the valley floor and more than a metre of horizontal extension across the valley. We interpret this as the deflation of a sill-like reservoir feeding propagating dykes along the ridge axis. The dykes eventually led to the outpouring of about 160 million m3 of lava at the seafloor in about 16 days, while inducing both seismic and aseismic slip on valley-bounding normal faults and finally triggering seismic activity on the abutting TFs. Large-scale aseismic slip induced by magmatic processes could therefore be the primary mechanism by which MOR normal faults accrue their displacement, which would account for their well-documented seismic deficit8,9.
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.
Exposure to redox‑active metals and sub‑inhibitory antibiotics represents a significant selective pressure shaping bacterial physiology and antimicrobial susceptibility. Here, we investigated how four genetically and phenotypically distinct Pseudomonas aeruginosa strains respond to sub‑MIC copper (Cu) and gentamicin (GE) stress, individually and in combination (Cu + GE). Across all strains, Cu acted as the dominant envelope‑active stressor, increasing biofilm biomass and extracellular DNA (eDNA) release, suppressing twitching and swarming motility, reducing quorum‑sensing‑linked protease activity, and enhancing pyomelanin production. GE alone produced limited physiological changes but modulated Cu‑driven outputs during co‑exposure, including attenuation of Cu‑induced eDNA release and strain‑specific shifts in motility and pigmentation. Early transcriptional profiling revealed consistent Cu‑dependent repression of lasI and mvfR, induction of the metal‑responsive regulator czcR, and downregulation of oprD under Cu or Cu + GE, corresponding to reduced imipenem inhibition zones when Cu was present during susceptibility testing. Combined Cu + GE exposure produced non‑additive, emergent effects that diverged from single‑stressor responses and varied across strains. These findings demonstrate that sub‑inhibitory Cu creates an envelope‑centered regulatory landscape into which gentamicin‑derived signals are integrated, generating heterogeneous and context‑dependent phenotypes. This work underscores the importance of metal-antibiotic interactions in shaping bacterial adaptation and highlights limitations of single‑stressor models for predicting antimicrobial behavior in combination.
Hybrid rocket motors with aerodynamic-throat nozzles can mitigate the chamber-pressure drop during deep throttling by using secondary injection to modify the effective throat area, without moving mechanical parts. However, rapid thrust prediction remains challenging because of the nonlinear interaction between secondary injection and the main combustion flow. This study develops a computationally efficient thrust-prediction method based on a radial basis function (RBF) neural network, whose spread factor is optimized by particle swarm optimization (PSO) combined with 5-fold cross-validation. A validated quasi-steady computational fluid dynamics (CFD) dataset covering 100 operating conditions is used for model training and testing, with oxidizer mass flow rate, secondary-injection mass flow rate, and fuel port diameter as inputs. The final model is constructed using all 70 training samples as RBF neuron centres and achieves a root mean square error (RMSE) of 6.86 N and a coefficient of determination (R2) of 0.9959 on 30 independent test samples, indicating accurate in-domain reproduction of the CFD-based thrust map. The trained model is applied to a variable-thrust hot-fire test, where it captures the magnitude and trend of the thrust response, with relative errors generally within 5% during stable operating phases. For a 20-second firing sequence with 1000 prediction steps, the calculation requires only 1000 scalar port-diameter updates and 7 × 104 radial-basis evaluations with output-weight accumulations, making it suitable for rapid design iteration and real-time prediction scenarios. The model is intended for in-domain prediction, and transient deviations may occur during rapid modulation because feed-system dynamics are not explicitly included.
Recently, UAV path planning in 3D complex environments has attracted increasing attention due to its significance in UAV motion control systems. However, the NP-hard nature of this problem poses significant challenges in generating a high-quality path. To address this issue, this paper proposes an improved self-adaptive particle swarm optimization (ISAPSO) algorithm by integrating the standard PSO 2011 with evolutionary game theory (EGT). Firstly, a novel self-adaptive parameter updating strategy is proposed, which combines the evolutionary stable strategy in EGT with hyperbolic tangent function to balance the exploration and exploitation capabilities of ISAPSO. Subsequently, an ISAPSO-based path planning approach is developed to generate optimal 3D path for UAV in an obstacle-rich environment. To efficiently handle constraints, a novel self-adaptive constraint handling technology is proposed in the developed path planner. Finally, the performance of the proposed ISAPSO is evaluated against six state-of-the-art evolutionary algorithms using 20 test functions. Following the benchmark study, the ISAPSO-based path planner is is validated in different scenarios against six well-known counterparts. The simulation results confirm that the proposed ISPASO outperforms its competitors in the benchmark study at a 90% confidence level. Moreover, the ISPASO-based path planning method dominates its contenders in terms of the path optimality. Therefore, the proposed method could be regarded as a vital alternative in the area of path planning.
Carbon fiber reinforced polymer (CFRP) is widely used in aerospace applications owing to its superior material properties. The development of efficient and reliable non-destructive testing methods is crucial for structural health monitoring of CFRP. This study proposes a novel non-contact eddy current testing (ECT) approach utilizing a transmit-receive (T-R) probe equipped with a figure-eight transmitter coil to mitigate lift-off interference. To achieve simultaneous enhancement of spatial resolution and detection sensitivity, a multi-objective optimization framework for the probe structure is established. Due to the lack of an explicit mathematical model for the induced-voltage amplitude of the receiver coil, this study develops a Back Propagation_Kriging predictive model to accurately approximate the response. Guided by this model, an improved multi-objective particle swarm optimization algorithm combined with an entropy-weighted technique for order preference by a similarity to ideal solution scheme enables an effective balance between spatial resolution and detection sensitivity. Simulation analyses combined with experimental measurements show that the optimized probe delivers finer spatial resolution and a pronounced improvement in detection sensitivity, demonstrating the effectiveness and robustness of the proposed optimization framework for advanced ECT probe design.
This article studies a swarm-to-swarm interception problem, where a swarm of intercept uncrewed aerial vehicles (UAVs) attempt to intercept a swarm of target UAVs based on pure vision-feedback. The proposed interception strategy employs a hierarchical structure consisting of three parts, namely, image processing, target allocation, and motion planning. As the core of the interception strategy, the target allocation model is trained by a novel dynamic sampling multiagent deep deterministic policy gradient (DS-MADDPG) algorithm, which, different from other classic MADDPG algorithms, features three specialties: the introduction of independent replay buffers and dynamic sampling networks to optimize learning efficiency; the incorporation of multidimensional convolution structures and a multihead attention mechanism into the policy network to capture the spatial and temporal relationships between agents and extract features; the design of a TD3-inspired twin critic architecture to mitigate overestimation bias in action-value functions. The performance of the proposed interception strategy is verified by comprehensive case studies in comparison with other existing algorithms, and the results validate the efficiency and effectiveness of the proposed strategy in terms of multiple performance indices.
Distributed optical fiber acoustic sensing (DAS) has become an important technology for production logging because it can record dense strain or strain-rate responses along an optical fiber under high-temperature, high-pressure, and corrosive downhole conditions. This single-well field case study investigated a hybrid low-frequency DAS processing framework for distributed optical fiber production logging. First, a finite impulse response (FIR)-based preprocessing step was used for low-pass smoothing before an F-K domain analysis. The DAS records were then transformed into the frequency-wavenumber (F-K) domain, where particle swarm optimization (PSO) was used to tune the nu parameter of a one-class support vector machine (OCSVM) for automatic feature extraction. Rule-based feature enhancement and a small-sample support vector classifier (SVC) were then applied to suppress residual F-K domain noise and retain the V-shaped features associated with upgoing and downgoing waves. Finally, linear regression was applied to the enhanced F-K domain branches to estimate the apparent propagation velocities and derive the flow velocity through the field interpretation relationship. The workflow was demonstrated using 15 s field DAS segments from an oil-water two-phase production well, and the six-window validation showed errors below 3.13% relative to the field-reference values. These results demonstrate the feasibility of the proposed workflow for the investigated well, but do not constitute general validation across different wells or acquisition conditions.
Salmonella Enteritidis (S. Enteritidis) is a persistent challenge within the poultry industry. The silent colonization allows S. Enteritidis establish themselves in the gut and reproductive tract, leading to persist, spread, and contaminate eggs and meat. This study investigated the anti-virulence potential of a sweet cherry pomace extract (CPE) as a potential natural decontaminant to disrupt S. Enteritidis colonization. The total phenolic content (TPC) and composition of ethanolic extracts from CPE were investigated via HPLC. Antimicrobial activities of CPE were evaluated by minimal inhibitory (MIC) and minimal bactericidal (MBC) concentrations determinations, as well as by growth kinetics and time-kill assays. Anti-virulence at sub-inhibitory concentrations (sub-MICs) was evaluated through swimming/swarming motility, biofilm formation (crystal violet), and auto-aggregation. The efficacy of CPE in reducing S. Enteritidis colonization was validated on chicken skin and visualized through scanning electron microscopy (SEM). The studied CPE was rich in phenolic compounds, predominantly anthocyanins; and exhibited an inhibitory effect against S. Enteritidis at a MIC of 64 mg/mL and MBC of 128 mg/mL. Concentrations ≤8 mg/mL did not significantly affect growth kinetics or viability. Importantly, sub-MIC levels significantly reduced swarming and swimming motility as well as biofilm formation, and auto-aggregation in a dose-dependent manner. SEM topographic analysis confirmed that CPE reduced S. Enteritidis colonization on treated chicken skin. These findings demonstrate that at sub-MICs, CPE functions as a non-bactericidal anti-virulence agent that interferes with S. Enteritidis colonization mechanisms, supporting its potential as a natural decontaminant for poultry processing.
Multi-UAV swarm systems have attracted significant attention in recent years due to their wide range of applications, including surveillance, disaster management, search and rescue, agriculture, infrastructure inspection, and autonomous transportation. In such systems, maintaining formation integrity, achieving accurate trajectory tracking, and ensuring safe obstacle avoidance under environmental and communication disturbances remain challenging research problems. Existing studies generally investigate wind effects, sensor noise, or communication delays separately and often lack a unified stability framework capable of characterizing their combined influence on formation performance. This study aims to develop a unified formation control and obstacle avoidance framework for multi-UAV systems operating under simultaneous wind disturbances, sensor noise, and communication delays. The proposed framework seeks to ensure stable formation regulation, centroid trajectory tracking, inter-agent collision prevention, and obstacle avoidance while providing formal robustness guarantees through an input-to-state stability (ISS) analysis. A consensus-based Laplacian formation controller was integrated with centroid tracking and artificial-potential-field-based obstacle and collision avoidance mechanisms. The Crazyflie 2.0 Nano-Quadrotor model was employed, and the six-degree-of-freedom dynamics were simplified into planar motion for swarm-level analysis. Stability of the nominal system was investigated using Lyapunov theory, while the disturbed system was analyzed within an ISS framework to derive explicit steady-state tracking error bounds under bounded disturbances. Numerical simulations were conducted in MATLAB for different swarm formations and disturbance scenarios. Simulation results demonstrated successful formation acquisition, centroid tracking, obstacle avoidance, and collision-free navigation under unified wind, sensor noise, and delay disturbances. Both hexagonal and line formations maintained stability while navigating toward desired target positions in the presence of static obstacles. Temporary formation deformations caused by avoidance maneuvers were effectively corrected, and the swarm recovered the desired geometry after disturbance effects diminished. Furthermore, the steady-state tracking errors remained within the theoretical ISS bounds, confirming consistency between the analytical results and simulation outcomes. The proposed framework provides a robust and unified solution for formation control and obstacle avoidance in disturbed multi-UAV systems. The integration of consensus-based control, artificial potential fields, and ISS-based robustness analysis enables reliable trajectory tracking and safe swarm coordination under realistic operating conditions. The obtained results indicate that the framework can serve as an effective foundation for future studies involving dynamic obstacles, three-dimensional swarm coordination, and real-world experimental validations.
How motile bacteria navigating external surfaces convert environmental cues into transitions from dispersed states to cohesive, multicellular assemblies is poorly understood. We identify RgzA, a multi-domain sensory kinase in the gliding bacterium Flavobacterium johnsoniae , that drives the formation of zorbs, which are motile, ball-like three-dimensional microcolonies of gliding cells. A point mutation in RgzA promotes zorb formation, suppresses swarming, and enhances biofilm development, whereas suppressor mutations across its sensory domains restore planktonic behavior. We further show that the response regulator RgzB forms a signaling circuit with RgzA and contributes to the regulation of zorbing. Transcriptomic and perturbation analyses link this transition to iron availability. Phenotypically, RgzAB establish a state in which cells form cohesive, biofilm-like microcolonies that retain motility over external surfaces thus shifting the population from alignment-driven swarming to zorb-based exploration. In mixed populations, RgzA*-derived co-zorbs encapsulate wild-type cells, whose internal organization exhibits a density-dependent percolation-like transition that generates connected cellular networks. Together, these findings show that sensory signaling governs both the formation and spatial organization of motile multicellular assemblies, establishing a link between sensory transduction and different states of collective organization in living systems.
For Industrial Wireless Sensor Networks (IWSNs) serving industrial environmental monitoring tasks, clustering optimization, the core technology for network performance tuning, is a well-recognized NP-hard problem that directly determines the energy efficiency and communication reliability of the entire system. Such IWSNs are typically deployed in large-scale, unattended industrial fields to collect real-time, high-precision environmental data including air quality, water pollution levels and soil parameters. However, inherent constraints like limited node energy supply and unstable wireless links in these scenarios often lead to incomplete data collection and delayed early warning, which directly undermine the reliability of environmental monitoring. To address this challenge, this paper proposes CCNCSO-CRP, a novel energy-efficient clustering routing protocol based on a multi-objective clustering model that jointly considers four key metrics: total network residual energy, average transmission delay, packet loss rate, and the distance from cluster heads to the base station. The protocol is built on the newly designed Chaotic Clonal Niche Cockroach Swarm Optimization (CCNCSO) algorithm, which integrates chaotic initialization and evolutionary strategies including clonal selection and niche preservation to enhance population diversity and convergence speed. Extensive experimental validations are conducted on the CEC2008 and CEC2020 benchmark test suites, where the CCNCSO algorithm outperforms classical meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Osprey Optimization Algorithm (OFA), Komodo Mlipir Algorithm (KMA), Grey Wolf Optimizer (GWO), and Artificial Bee Colony (ABC). Furthermore, experimental evaluations under various IWSN environmental monitoring scenarios show that CCNCSO-CRP outperforms state-of-the-art protocols including LEACH-C, VSSLS-SIACR, and FOAEAUC-SARP by at least 11.5% in extending network lifetime, reduces average delay by no less than 9.5%, and cuts packet loss rate by a minimum of 40.9%. These results validate the effectiveness and superiority of the proposed protocol in improving clustering efficiency and network stability for environmental monitoring IWSNs, and provide reliable technical support for high-performance environmental parameter detection and intelligent early warning systems.