Pompilidae Research Articles

Artificial intelligence-based optimization techniques for optimal reactive power dispatch problem: a contemporary survey, experiments, and analysis

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems).

Advancing Iot Security: A Novel Intrusion Detection System for Evolving Threats In Industry 4.0 Using Optimized Convolutional Sparse Ficks Law Graph Point Trans-Net

With the rapid advancement of Industry 4.0, the integration of Internet of Things (IoT) strategies in industrial environments has increased exponentially. While this integration enhances productivity and efficiency, it also introduces significant security vulnerabilities. Previous research has employed several deep learning approaches for intrusion detection; however, these methods often suffer from insufficient accuracy, increased computational time, complexity, and higher error rates. To address these issues, this work proposes an innovative solution: "Advancing IoT Security: A Novel Intrusion Detection System (IDS) for Evolving Threats in Industry 4.0 using optimized Convolutional Sparse Fick's Law Graph Pointtrans-Net (CSFLGPtrans-Net)." The proposed system utilizes a comprehensive intrusion detection dataset composed of four different datasets: ToN-IoT, NSL-KDD, CSE‑CIC‑IDS2018, and IoT_bot. Initially, the input data undergoes a pre-processing stage that includes cleaning columns and rows, encoding features, and normalizing data. Following this, a hybrid optimization method, combining the Fire Hawk Optimizer with the Spider Wasp Optimizer, is applied for feature selection. This step is crucial for identifying the most significant features to enhance classification accuracy. The refined data is then classified using the CSFLGPtrans-Net model. To ensure secure data transfer, Fuzzy-based Elliptic Curve Cryptography (FECC) is employed. Experimental simulations conducted on the Python platform demonstrate that the proposed method outperforms existing approaches across various performance metrics, achieving a higher accuracy of 98% and a recall of 0.993. These results highlight the method's superior efficiency and potential for further advancement in securing Industry 4.0 environments.

Spider Wasp Optimizer-Optimized Cascaded Fractional-Order Controller for Load Frequency Control in a Photovoltaic-Integrated Two-Area System

The integration of photovoltaic (PV) systems into traditional power grids introduces significant challenges in maintaining system stability, particularly in multi-area power systems. This study proposes a novel approach to load frequency control (LFC) in a two-area power system, where one area is powered by a PV grid and the other by a thermal generator. To enhance system performance, a cascaded control strategy combining a fractional-order proportional–integral (FOPI) controller and a proportional–derivative with filter (PDN) controller, FOPI(1+PDN), is introduced. The controller parameters are optimized using the spider wasp optimizer (SWO). Extensive simulations are conducted to validate the effectiveness of the SWO-tuned FOPI(1+PDN) controller. The proposed method demonstrates superior performance in reducing frequency deviations and tie-line power fluctuations under various disturbances. The results are compared against other advanced optimization algorithms, each applied to the FOPI(1+PDN) controller. Additionally, this study benchmarks the SWO-tuned controller against recently reported control strategies that were optimized using different algorithms. The SWO-tuned FOPI(1+PDN) controller demonstrates superior performance in terms of faster response, reduced overshoot and undershoot, and better error minimization.

Two new species of the spider wasp genus Paracyphononyx Gribodo, 1884 (Hymenoptera, Pompilidae) in China, with a key to Chinese species

The taxonomy of the genus Paracyphononyx Gribodo, 1884 from China is studied. Two new species, P. pilisquamatus Liu & Ma, sp. nov. and P. noncarinulatus Liu & Ma, sp. nov., from Yunnan, Hainan, and Guangdong Provinces, are described and illustrated. A new combination is proposed for Paracyphononyx smithi (Banks, 1934), comb. nov. A key to the Chinese species of Paracyphononyx is provided.

First record of the Taiwanese spider wasp Minotocyphus formosanus (Tsuneki) (Hymenoptera: Pompilidae) as a koinobiont ectoparasitoid of the funnel-web spider Macrothele hungae Lin and Li (Mygalomorphae: Macrothelidae)

ABSTRACT The life history of the Taiwanese spider wasp Minotocyphus formosanus (Pompilidae: Pepsinae) is reported, including the developmental process of the immature stages. The larva was attached by the spider wasp to the abdomen of the funnel-web spider Macrothele hungae (Mygalomorphae: Macrothelidae). In terms of mode of life, M. formosanus was established as a koinobiont ectoparasitoid. These biological findings are the first reports for not only the species M. formosanus, but also the genus Minotocyphus.

AEEFCSR: an adaptive ensemble empirical feed-forward cascade stochastic resonance system for weak signal detection

Abstract A novel adaptive ensemble empirical feed-forward cascade stochastic resonance (AEEFCSR) method is proposed in this study for the challenges of detecting target signals from intense background noise. At first, we create an unsaturated piecewise self-adaptive variable-stable potential function to overcome the limitations of traditional potential functions. Subsequently, based on the foundation of a feed-forward cascaded stochastic resonance method, a novel weighted function and system architecture is created, which effectively addresses the issue of low-frequency noise enrichment through ensemble empirical mode decomposition. Lastly, inspired by the spider wasp algorithm and nutcracker optimization algorithm, the spider wasp nutcracker optimization algorithm is proposed to optimize the system parameters and overcome the problem of relying on manual experience. In this paper, to evaluate its performance, the output signal-to-noise ratio (SNR), spectral sub-peak difference, and time-domain recovery capability are used as evaluation metrics. The AEEFCSR method is demonstrated through theoretical analysis. To further illustrate the performance of the AEEFCSR method, Validate the adoption of multiple engineering datasets. The results show that compared with the compared algorithms, the output SNR of the AEEFCSR method is at least 6.2801 dB higher, the spectral subpeak difference is more than 0.25 higher, and the time-domain recovery effect is more excellent. In summary, the AEEFCSR method has great potential for weak signal detection in complex environments.

Joint Optimization-Based QoS and PAPR Reduction Technique for Energy-Efficient Massive MIMO System

Massive multiple input multiple output (MIMO) is an innovative wireless communication technology that significantly enhances the capacity and efficiency of data transmission in modern networks. Massive multiple input multiple output, despite benefits, faces difficulties in managing numerous antennas affecting signal quality, peak-to-average power ratio (PAPR), and energy efficiency due to its scale and complexity. However, overcoming these complexities is vital for unlocking massive multiple input multiple output’s potential in high-quality, energy-efficient wireless communication. A proposed joint optimization-based technique aims to address these issues in massive MIMO systems. In phase 1, joint optimization with quality of service maximizes capacity via power distribution, employing the hybrid spider wasp Fick’s law algorithm. In phase 2, zero-forcing with symbol-level linear precoding, especially the stacked convolutional sparse bidirectional long short-term memory autoencoder (SCS–BiLSTMAE) network, efficiently reduces peak-to-average power ratio, collaborating for reduced bit rate error and peak-to-average power ratio through adaptive mapping. Refining the model involves precise hyperparameter tuning using the enhanced influencer buddy optimization algorithm. The proposed framework not only demonstrates exceptional performance, but the metrics also underscore the model's effectiveness in addressing diverse challenges, establishing it as a robust solution for quality enhancement, peak-to-average power ratio reduction, and energy efficiency. Additionally, the proposed model attains 28.14% greater system capacity than existing methods.

Intelligent Health Assessment Based on Communication and AI Technologies for Public Health

Automating EEG pattern categorization in public health helps in the early detection of epilepsy and other brain illnesses. However, present methods have difficulties in reaching excellent accuracy and adaptability. This study utilizes a hybrid strategy that incorporates Spider wasp-tuned Convolutional neural network (SW-CNN). Convolutional neural networks (CNNs) use complex feature extraction from EEG data, and Spider wasp optimization (SWO) improves network parameters by comparing them to the pursuing and developing behaviors of spider wasps. By enhancing classification accuracy, precision, recall, and F1-score measures, the combined effect seeks to achieve significant improvements over conventional techniques. The research concludes that the SW-CNN hybrid model performs better at differentiating between non-seizure activity and epileptic seizures. This strategy improves diagnosis accuracy and efficiency in public health, resulting in better patient outcomes and treatment planning. Effective transmission of such results is critical for furthering clinical diagnosis and research in neurological diseases.

An enhanced spider wasp optimization algorithm for multilevel thresholding-based medical image segmentation

An enhanced spider wasp optimization algorithm for multilevel thresholding-based medical image segmentation

Insights into the Evolutionary Dynamics: Characterization of Disintegrin and Metalloproteinase Proteins in the Venom Gland Transcriptome of the Hemiscorpius lepturus Scorpion.

The Disintegrin and Metalloproteinase (ADAM) family, also known as the metalloproteinase/disintegrin/cysteine-rich (MDC) proteins, includes both secreted and transmembrane molecules involved in critical biological processes, such as cell migration, adhesion, and signaling. This study aimed to investigate the evolutionary relationships and structural characteristics of disintegrin and metalloproteinase proteins identified in the venom gland transcriptome of the scorpion Hemiscorpius lepturus. Using bioinformatics tools, we analyzed the open reading frame, conserved motifs, and primary, secondary, and tertiary structures of these proteins. Five proteins, named HLDis- Met1, HLDisMet2, HLDisMet3, HLDisMet4, and HLDisMet5, were identified. Their predicted 3- D structures were within normal ranges (Z-score between -4 to -9). Phylogenetic analysis revealed that HLDisMet1 shares similarities with proteins from various spider species (Nephila pilipes, Argiope bruennichi, Araneus ventricosus, and Trichonephila inaurata madagascariensis), HLDisMet2 with the scorpion Centruroides sculpturatus, HLDis- Met4 with the scorpion Tityus serrulatus, and HLDisMet5 with several snake species (Python bivittatus, Vipera anatolica senliki, Protobothrops mucrosquamatus, and Naja naja). These findings highlight the significant similarities between HLDisMet proteins and those found in other venomous species, suggesting a complex and diverse evolutionary pathway for venom components. The cross-species conservation observed may indicate a convergent evolutionary strategy, where different species independently develop similar venom components to adapt to similar ecological niches or prey types. This study highlights the evolutionary significance of venom diversification and its potential applications in understanding venom biology across different species.

Biology and ecology of a deep cave nesting spider wasp, Ageniella evansi Townes, (Hymenoptera: Pompilidae), in Arizona

ABSTRACT Cavernicolous populations of the spider wasp Ageniella evansi Townes, 1957 (Hymenoptera: Pompilidae) nest deep within limestone caves at Colossal Cave Mountain Park near Tucson, Arizona, USA. These remarkable populations are the only truly cavernicolous pompilid wasps currently known. As many as 79 females were documented nesting simultaneously within Arkenstone Cave. The facts that the wasps do not nest near cave entrances but only deep within the interior of the caves, are philopatric to each occupied cave and have been present each year for decades confirm their cavernicolous behaviour as subtroglophiles. Studies conducted for over 30 years revealed many insights into the behaviour of these wasps in addition to their nesting deep within limestone caves, including: intraspecific and interspecific competition, plasticity in host selection, precision solar navigation, evidence that some form of communal nesting may be present and behaviour suggestive of rudimentary cognition in the species. This study demonstrates the importance of assessing all animals found in caves as potential ecological elements and not focusing solely on exotic troglobiotic species.

Use of a novel nesting material by the spider wasp Dipogon variegatus (Hymenoptera, Pompilidae)

A female spider wasp Dipogon variegatus was filmed stealing fragments of pollen-nectar provision from a solitary bee (Osmia cornuta) nest and using them for the construction of her nest. The female wasp applied the sticky fragments of the pollen-nectar provision to the outer surface of her closing nest plug, thus gluing together pieces of debris filling the nesting cavity. Previous descriptions of D. variegatus nests indicate that females of this species usually use spider silk to provide cohesion to the nest plug. Our observations provide an example of behavioural plasticity and innovation in the use of nesting materials. We describe the structure of the nest and the sequence of emergence of the progeny.

An efficient spider wasp optimizer-based tracker for enhancing the harvested power from thermoelectric generation sources

The distribution of heat at the cold and hot sides of a thermoelectric generator (TEG) is essential to its operation. Due to the inhomogeneous pattern, the TEG power-voltage (P–V) curve exhibits multiple peaks, which pose significant consequences for TEG generation. In order to increase the system conversion efficiency at this point, tracking global peak power (GPP) using a maximum power point tracker (MPPT) is crucial. The peak generation from TEG devices cannot be harvested by traditional algorithms such as hill climbing (HC) because they fall in local peaks. Therefore, the spider wasp optimizer (SWO), a new metaheristic-based tracker, is proposed in this paper to monitor the global power from TEG system operating at inhomogeneous heat distribution. In SWO, the iterative process of changing the population length aids in avoiding local solutions. The suggested SWO-based tracker is responsible for regulating the dc-dc boost converter linked to the TEG array terminals by giving its MOSFET the proper duty cycle. Seven inhomogeneous heat distributions are examined for two TEG arrays: symmetric 9 × 9 and asymmetric 10 × 15. These heat distributions are uneven row, uneven column, diagonal, outer, center, and random. The proposed approach is compared to artificial gorilla troops optimizer (GTO), gold rush optimizer (GRO), walrus optimization algorithm (WaOA), particle swarm optimizer (PSO), chef-based optimization approach (CBOA), cuckoo search (CS), Jellyfish search approach (JS), seagull optimization approach (SOA), and sine cosine approach (SCA). With regard to 9 × 9 array, the suggested SWO-tracker performed better than the published GTO method since it increased the generated power in the following scenarios: uneven row, uneven column, SN, diagonal, outer, center, and random patterns by 1.225 W, 0.4083 W, 19.5838 W, 2.4596 W, 1.394 W, and 2.4463 W, respectively. In asymmetric array, the suggested SWO performed better than all others, obtaining the least errors in relation to the Simulink GPPs of 0.016 W, 0.0822 W, 0.3615 W, 0.0603 W, 0.0569 W, 0.0081 W, and 0.0089 W. The obtained outcomes validated the proficiency and dependability of the suggested SWO-tracker in all examined cases.

Биотопическое распределение пауков-кругопрядов (Aranei: Araneidae) острова Аскольд (залив Петра Великого, Приморье)

The article presents the data on the species composition, abundance, spatial and biotopical distribution of orb-weaver spiders on Askold Island, located in Russia's Far East, in the Peter the Great Gulf (Primorsky Krai). The material was collected from 21 key sites of 3 x 25 m, by entomological sweeping and manually. A total of 507 spiders, belonging to 13 species from 8 genera, were captured. Of these, one species - Singa hamate - is indicated for the first time. On the island the most widespread spiders are of the genus Araneus, living in 11 out of 13 types of biotopes. For this genus, the most favorable are forest habitats, dominated by A. ishisawai and A. ventricosus. In open meadow biotopes, 8 species of spiders from 6 genera were recorded, with Neoscona adianta dominating: reaching the density of 29 specimens per 100 m2 in open plant communities with predominance of Artemisia gmelinii. In various parts of the island within linden and broad-leaved multi-species forests, the greatest diversity and abundance of orb-weaver spiders is recorded. There spiders from 3 genera (5 species of each) were caught. Their total density is 42.5 specimens per 100 m2. Stony beaches, stretching along the entire coast of the island, are characterized by the poorest faunal composition. One species encountered was Araneus ventricosus (4 specimens per 100 m2). For the Askold Island, a series of schematic distribution maps for the common species of orb-weaver spiders (A. ishisawai, A. ventricosus, A. macacus, Argiope bruennichi, Neoscona adianta, Plebs sachalinensis) has been compiled for the first time.

AIR POLLUTION PREDICTION USING MODIFIED GRU DEEP LEARNING MODEL

<p>In recent days air quality has been very poor in urban society due to a lot of industries, vehicles and so on. To overcome poor quality, an air quality prediction is much needed to manage an urban area and control pollution. In this article, deep learning (DL) namely a Modified Gated Recurrent Unit (GRU) neural network is used to forecast air pollution. Initially, a new activation function named Dual-Slope Leaky ReLU (DSL-ReLU) is introduced. The DSL-ReLU activation function includes two hyperparameters which control the slopes for positive and negative input values to achieve fine-tuned responses for varying data inputs. Then, the model parameters like learning rate, batch size, and dropout rates are tuned using the Spider Wasp Optimization (SWO) algorithm. The SWO algorithm is based on the female spider wasp’s hunting and nesting behaviour that is applied to solve the optimization issues. This SWO model is used to GRU model parameters and provides robustness in the predictive modelling of air quality monitoring. For validation, the Kaggle data set is used whereas the proposed optimized GRU model considerably improves the accuracy of air pollution forecasts</p>

Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network

In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm’s end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements.

Gene selection based on recursive spider wasp optimizer guided by marine predators algorithm

Detecting tumors using gene analysis in microarray data is a critical area of research in artificial intelligence and bioinformatics. However, due to the large number of genes compared to observations, feature selection is a central process in microarray analysis. While various gene selection methods have been developed to select the most relevant genes, these methods’ efficiency and reliability can be improved. This paper proposes a new two-phase gene selection method that combines the ReliefF filter method with a novel version of the spider wasp optimizer (SWO) called RSWO-MPA. In the first phase, the ReliefF filter method is utilized to reduce the number of genes to a reasonable number. In the second phase, RSWO-MPA applies a recursive spider wasp optimizer guided by the marine predators algorithm (MPA) to select the most informative genes from the previously selected ones. The MPA is used in the initialization step of recursive SWO to narrow down the search space to the most relevant and accurate genes. The proposed RSWO-MPA has been implemented and validated through extensive experimentation using eight microarray gene expression datasets. The enhanced RSWO-MPA is compared with seven widely used and recently developed meta-heuristic algorithms, including Kepler optimization algorithm (KOA), marine predators algorithm (MPA), social ski-driver optimization (SSD), whale optimization algorithm (WOA), Harris hawks optimization (HHO), artificial bee colony (ABC) algorithm, and original SWO. The experimental results demonstrate that the developed method yields the highest accuracy, selects fewer features, and exhibits more stability than other compared algorithms and cutting-edge methods for all the datasets used. Specifically, it achieved an accuracy of 100.00%, 94.51%, 98.13%, 95.63%, 100.00%, 100.00%, 92.97%, and 100.00% for Yeoh, West, Chiaretti, Burcyznski, leukemia, ovarian cancer, central nervous system, and SRBCT datasets, respectively.

Boosted Spider Wasp Optimizer for High-dimensional Feature Selection

Boosted Spider Wasp Optimizer for High-dimensional Feature Selection

A multi-strategy spider wasp optimizer based on grouping and dimensional symmetry method with a time-varying weight

A multi-strategy spider wasp optimizer based on grouping and dimensional symmetry method with a time-varying weight

New data on spider wasps of the subgenus Ammosphex Wilcke, 1942 (Hymenoptera, Pompilidae, Arachnospila Kincaid, 1900) from Western Siberia, Russia

The new species, Arachnospila (Ammosphex) altaica Loktionov, sp. nov., is described and illustrated from the Altai Republic, Western Siberia, Russia based on male specimens. The new species belongs to A. (Am.) abnormis species-group, whose males have a tuft of long bristles on hypopygium ventrally. The male of the new species is clearly distinguished from congeners by the unique shape of the hypopygium and the position of the bristles on it. Seven species of the subgenus Ammosphex Wilcke, 1942 from the genus Arachnospila Kincaid, 1900 are newly recorded from Western Siberia. The new species and new regional records raise the number of species known from Western Siberia to 58, and the total number of spider wasps in Russia to 251.