Metaheuristic algorithms are powerful tools for solving complex optimization problems where traditional methods fail. The Social Spider Optimization (SSO) algorithm, inspired by the cooperative foraging behavior of spiders, is a notable swarm intelligence technique. However, it can be prone to premature convergence. This paper presents an enhanced variant, the Elite Opposition-Based Social Spider Optimization (EOSSO) algorithm, which integrates an elite opposition-based learning (OBL) strategy and an elite selection mechanism into the standard SSO framework. This integration aims to improve population diversity, enhance global exploration, and accelerate convergence. The performance of EOSSO is rigorously evaluated on a comprehensive set of 23 benchmark functions, including unimodal, multimodal, and fixed-dimension multimodal problems. Experimental results demonstrate that EOSSO significantly outperforms the standard SSO and other well-known metaheuristics in terms of solution accuracy, convergence speed, and stability. The algorithm exhibits a remarkable ability to escape local optima and refine solutions efficiently, proving its robustness and effectiveness as a high-performance optimizer for complex landscapes.

In the origin of multicellular organisms or social groups, a dichotomy emerges between systems where the parts retain some autonomy, referred to as modular, and more integrated unitary systems. I explore the critical roles of ecology and geometry in determining whether groups form and the degree of integration they develop. I argue that these are essential considerations above and beyond whether groups originate from a single cell or a single inseminated female. I consider these points through the lens of social and colonial spiders, which represent early transitions to either unitary or modular systems, respectively. By allowing or constraining cooperation, I suggest that the geometry of their webs—irregular tridimensional versus orbicular—determines the degree of integration of their groups, their scaling properties, their population structure, and their long-term evolutionary fate. Ecology, on the other hand, determines the need or opportunity for groups to form. I extend these lessons to other social systems and levels of organization.

Improved MPPT technology for PV systems using Social Spider optimization (SSO): Efficient handling of partial shading and load variations

Cancer Classification through Gene Selection Using the Social Spider Optimization Algorithm

The social brain hypothesis predicts that the relative size of specific brain regions is driven by the cognitive capacity required to manage complex (social) situations. Spiders are intriguing models to test this hypothesis, as sociality is rare in this usually solitary and aggressive group. Here, we used microCT to compare the central nervous system and brain volumes between social and solitary females of the species in two taxonomic groups, huntsman and crab spiders. Overall, we found no difference in relative CNS and brain volume between social and solitary species. However, social huntsman spiders Delena cancerides had larger arcuate and mushroom bodies than the solitary huntsman species Isopeda villosa and Heteropoda jugulans. Social crab spiders Xysticus bimaculatus had larger visual neuropils than the solitary species Thomisus spectabilis and Tharrhalea evanida. Social huntsman spiders exhibit intricate social behavior, including prey sharing and kin recognition, which could explain the higher investment in brain structures that are related to cognitive integration. They also had smaller venom glands, possibly due to their prey-sharing behavior. In social crab spiders, the low-light leafnest may have driven enlarged visual neuropils. Some variations in specific brain regions between solitary and social species were consistent with the social brain hypothesis, but the patterns differed between lineages. Thus, it is likely that other ecological drivers affect the development of specific brain regions in spiders. Our study provides the essential knowledge platform to conduct experimental manipulations of social and environmental conditions on these spiders to directly test their impact on brain structures, coupled with tests of relevant behavior.

Optimization remains a cornerstone of modern engineering and computational intelligence, playing a vital role in the design, control, and ‎allocation of limited resources across industries ranging from logistics to structural engineering. Traditional optimization methods, such as ‎gradient-based and exact algorithms, often struggle with the nonlinear, multimodal, and constrained nature of real-world problems, necessitating the adoption of metaheuristic approaches. These biologically and physically inspired algorithms offer flexibility, scalability, and robustness in navigating complex search spaces.‎ This study presents a systematic categorization of optimization problems—including combinatorial, continuous, constrained, and multi-‎objective classes—followed by a rigorous comparative analysis of nine prominent metaheuristics: Ant Colony Optimization (ACO), Lion ‎Algorithm (LA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Vibrating Particles System (VPS), Social Spider Optimization (SSO), ‎Cat Swarm Optimization (CSO), Bat Algorithm (BA), and Artificial Bee Colony (ABC). The algorithms are evaluated across five representative benchmark problems: the Traveling Salesman Problem (TSP), Welded Beam Design (WBD), Pressure Vessel Design (PVD), ‎Tension/Compression Spring Design (TSD), and the Knapsack Problem (KP).‎ Key contributions include: 1)Domain-specific suitability analysis, revealing how algorithmic mechanisms align with problem structures.‎ ‎ 2) Performance benchmarking under standardized conditions, highlighting convergence speed, solution quality, and constraint-handling ‎efficacy. 3) Practical insights for practitioners on algorithm selection, hybridization potential, and adaptation challenges.‎ Results demonstrate that no single algorithm dominates universally; instead, problem characteristics dictate optimal choices. For instance, ‎ACO excels in discrete problems (TSP, KP), while GWO and BA outperform in continuous engineering designs (WBD, PVD). The study ‎concludes with recommendations for future research, including dynamic parameter tuning, hybrid models, and real-world scalability ‎assessments‎.

Thermal adaptation plays a crucial role in shaping the development, reproduction and population dynamics of ectothermic organisms. In this study, we compared thermal life history traits among three closely related social spider mites: Stigmaeopsis sabelisi, S. miscanthi high-aggression (HG) form, and their common ancestral group, S. miscanthi mild-aggression (ML) form. We investigated the minimum temperature thresholds for development by measuring the days required for egg hatching under five constant temperature conditions (15°C, 20°C, 25°C, 30°C, 32°C) and estimating the thresholds using linear and nonlinear regression models. Additionally, we assessed their reproductive diapause attributes. Our results revealed that the minimum development thresholds were slightly lower in S. sabelisi from colder regions compared to S. miscanthi HG form and S. miscanthi ML form distributed in warmer and subtropical regions. Notably, high-temperature stress negatively affected development only in S. sabelisi, suggesting local adaptation. Reproductive diapause attributes also varied: reproductive diapause was induced under short-day conditions in S. sabelisi, whereas the other two species lacked such diapause. Moreover, phenotypic variation in the number of days required for egg hatching was highest in S. miscanthi ML form, suggesting retained ancestral variability that may have facilitated subsequent divergence. These findings support the hypothesis that populations from colder environments exhibit lower thermal thresholds and more intense diapause than those from warmer environments, and also provide insights into the mechanisms driving local adaptation and speciation in the social spider mites.

Social Spider Enhanced Multi-Layered ANN Routing Scheme for Wireless Sensor Networks Utilizing Internet of Things and Blockchain Technology

This study presents a comprehensive comparative analysis of nine state-of-the-art metaheuristic optimization algorithms applied to the classical Traveling Salesman Problem (TSP), a fundamental benchmark in ‎combinatorial optimization. The selected algorithms—Ant Colony Optimization (ACO), Lion Algorithm ‎‎(LA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Vibrating Particles System (VPS), Social Spider ‎Optimization (SSO), Cat Swarm Optimization (CSO), Bat Algorithm (BA), and Artificial Bee Colony ‎‎(ABC)—are evaluated on three standardized TSPLIB benchmark instances: berlin52, eil76, and pr1002. ‎The evaluation framework encompasses multiple performance metrics, including best-found cost, mean ‎solution quality, standard deviation, and convergence behavior, over 30 independent runs per instance. ‎The results offer empirical insights into each algorithm’s strengths, limitations, and scalability across ‎problem sizes. Notably, ACO, GWO, and CSO demonstrate superior balance between solution accuracy ‎and robustness, making them promising candidates for large-scale combinatorial problems. This work not ‎only provides an up-to-date performance landscape of leading swarm-based and evolutionary metaheuristics but also guides algorithm selection for real-world optimization applications requiring adaptability ‎and computational efficiency‎.

Sociality in spiders has evolved independently multiple times with diverse expressions. Delena cancerides, an Australian huntsman spider, shows some sociality but has been classified variably as social, subsocial, or non-social. Previous classifications were based on evidence like outbreeding, balanced sex ratios, and colonies primarily consisting of one mother and her offspring. However, studies, including this one, have found colonies with multiple adult females, males, and juveniles at certain times of the year. The data show that D. cancerides colonies were more diverse in summer, with multiple adult females, males, and juveniles, compared with spring, when colonies mainly consisted of one adult female and juveniles. Although all huntsman spiderlings cohabit briefly before dispersing, D. cancerides spiderlings shared prey beyond this period, especially larger prey. This suggests that the species’ social structure is more complex than previously thought, varying with time and possibly related to colony composition, warranting further study.

In the digital age, innovative possibilities for artistic expression and audience interaction have been made possible by the intersection of art and technology. Interactive art, which actively involves spectators in the creative experience, has grown in popularity due to its ability to create dynamic, immersive, and participatory environments. By utilizing the Internet of Things (IoT) capabilities, interactive art design has seen a radical change that allows for smooth connections between digital platforms, sensors, and real-world products. The goal of the research is to develop interactive art design works that incorporate IoT technology for immersive and dynamic artistic experiences. This research analyzes how real-time data processing, sensors, and IoT devices enhance interaction and encourage more audience participation in art installations. The Social Spider Malleable Long Short-Term Memory (SS-MLSTM) is used to evaluate user experience and interactions, optimizing design efficiency and creative outcomes. Collected data were cleaned and preprocessed to eliminate noise and outliers. IoT technology integration enables robust interactive capabilities, providing designers and artists with innovative instruments for creative design processes and experimental production. The findings show that the proposed method enhances the artistic and user experience through real-time feedback and innovative design frameworks. The feedback values reflect user satisfaction in various aspects of design: overall experience indicates (95.2%) high satisfaction, engagement with interactive elements (91.6%), artistic immersion (93.4%), and feedback utilization (89.8%). The results emphasize the interaction between art, technology, and user-centered design techniques, highlighting the potential of IoT to redefine the parameters of conventional art.

DEEP LEARNING-BASED LIVER CT SCAN ANALYSIS WITH SOCIAL SPIDER OPTIMIZATION FOR FEATURE REDUCTION

Open-source software is gaining popularity in industrial projects due to its accessibility and cost-effectiveness. However, concerns persist about its quality and reliability. To assess software reliability quantitatively, software reliability models are utilized, with the unknown parameters of these models typically determined using statistical techniques. In many cases, these methods fail to converge to the global optimal solution of parameter estimation of nonlinear mathematical models and are quite sensitive to the initial guesses of unknown parameters. This necessitates employing a high-quality parameter estimation technique. The study demonstrates the potential application of nine nature-inspired swarm intelligence-based algorithms to address nonlinear parameter estimation problems and effectively identify the global optimal solution with high likelihood, irrespective of the initial guess. These typical algorithms are classified into several categories, including animal-inspired algorithms such as grey wolf optimizer, insect-inspired algorithms such as artificial bee colony, social spider optimization, firefly algorithm, and moth flame optimization, bird-inspired algorithms such as particle swarm optimization, sea creature-inspired algorithms such as whale optimization algorithm, and plant-inspired algorithms such as flower pollination algorithm and dandelion optimizer. Three real-world, open-source reliability datasets are utilized to assess the efficacy of these algorithms in estimating the parameters of two prominent non-homogeneous Poisson process models in software reliability.

Accurate short-term load forecasting (STLF) is critical for efficient energy management, especially in regions like Nigeria, where electricity demand fluctuates due to climatic and socio-economic factors. This study proposes a hybrid model combining Social Spider Optimisation (SSO) and African Vulture Optimisation Algorithm (AVOA) to optimise Artificial Neural Networks (ANN) for improved STLF accuracy. The model was trained and validated using actual load data from the Nigerian grid for February, March, May, and June 2021. Quantitative evaluation using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient, and Coefficient of Determination (R²) showed superior performance of the SSO-AVOA model. The most stable results were recorded in May 2021, with MAPE of 0.202%, MAE of 8.47 MW, RMSE of 28.83 MW, and R² of 0.999, indicating nearly perfect forecasting. February and June periods showed relatively higher errors (e.g., MAPE up to 1.043% in February), reflecting the difficulty of forecasting during seasonal transitions. Findings confirm the robustness and adaptability of the hybrid model, which consistently maintains high correlation between actual and forecasted loads. However, error patterns during volatile periods suggest potential for improvement. Future work should integrate weather and socio-economic indicators, apply dynamic seasonal adaptations, and validate the model across Nigeria’s geopolitical zones. This study demonstrates that hybrid bio-inspired algorithms like SSO-AVOA are practical, high-performing tools for real-world load forecasting in dynamic and complex environments.

Abstract: Parental care in spiders (Araneae) is a fascinating and diverse aspect of arachnid behavior, encompassing a range of strategies aimed at enhancing offspring survival. Unlike many invertebrates that produce numerous offspring with little to no post-oviposition involvement, several spider species have evolved intricate behaviors to protect and nurture their young ones. This care begins with the construction of protective egg sacs, which shield eggs from predation, desiccation, and environmental fluctuations. In some species, mothers go further by guarding these sacs vigilantly, fending off predators and ensuring favorable microclimatic conditions. Post-hatching care is equally remarkable. Certain species, such as those in the Lycosidae (wolf spiders) and Pisauridae (nursery web spiders) families, exhibit brood carrying, where spiderlings ride on the mother’s abdomen or cephalothorax, gaining mobility and protection. In other cases, such as in Stegodyphus (social spiders), females engage in regurgitation feeding, offering partially digested food to their offspring. Even more extreme is the phenomenon of matriphagy, observed in some species, wherein the mother sacrifices herself as a food source, ensuring the survival and nourishment of her young. Furthermore, spiders may provide indirect care by constructing nursery webs or creating secluded habitats where spiderlings can develop safely before dispersal. The extent and form of parental investment vary widely, influenced by ecological pressures, predation risks, and environmental factors. Such behaviors highlight the evolutionary significance of parental care in juvenile survival and population stability under variable environmental conditions. Overall, parental care in spiders represents a remarkable convergence of survival strategies, reflecting both ancestral traits and adaptive innovations in response to environmental pressures. This diverse spectrum of behaviors not only enhances offspring fitness but also provides critical insights into the evolution of sociality and reproductive investment in arachnids. Key Words: Parental Care, Spiders, Araneae, Matriphagy

Editor Note to “Novel methodology for the geophysical interpretation of magnetic anomalies due to simple geometrical bodies using social spider optimization (SSO) algorithm”

This article presents a novel multi-objective economic load dispatch solution with the bee colony optimization method. The purposes of this research are to find the lowest total power generation cost and the lowest total power loss at the transmission line. A swarm optimization method was used to consider the non-smooth fuel cost function characteristics of the generator. The constraints of economic load dispatch include the cost function, the limitations of generator operation, power losses, and load demand. The suggested approach evaluates an IEEE 5, 26, and 118 bus system with 3, 6, and 15 generating units at 300, 1,263, and 2,630 megawatt (MW) and uses a simulation running on the MATLAB software to confirm its effectiveness. The outcomes of the simulation are compared with those of the exchange market algorithm, the cuckoo search algorithm, the bat algorithm, the hybrid bee colony optimization, the multi-bee colony optimization, the decentralized approach, the differential evolution, the social spider optimization, and the grey wolf optimization. It demonstrates that the suggested approach may provide a better-quality result faster than the traditional approach.

The hazards associated with cyber security attacks have increased due to our increasing reliance on digital technologies. We suggest an ensemble strategy employing multiple models of machine learning to get over these restrictions. Our suggested approach detects an extra 141 dangerous URLs, outperforming the single model that is currently in use by 6%. We gathered a collection of malicious and benign URLs for this investigation. Applying Principal Component Analysis (PCA) to feature extraction. Subsequently advantageous features are selected and categorised using the Social Spider optimised Densely-connected Convolutional Caps net (SSO-DCN). The most effective detection frameworks and automated methods for identifying rogue web pages, including those distribute malicious malware, are included in the proposed technology. When comparing the proposed method to the existing approaches, the improvements are in mean accuracy (96.30%), precision (96.90%), F1-Measure (97.20%), recall (96.90%), FPR (3.50%) and FNR (3.90%).

In cooperatively breeding social animals, a few individuals account for all reproduction. In some taxa, sociality is accompanied by a transition from outcrossing to inbreeding. In concert, these traits reduce effective population size, potentially rendering transitions to sociality “evolutionarily dead-ends.” We addressed this hypothesis in a comparative genomic study in spiders, in which sociality has evolved independently at least 23 times, but social branches are recent and short. We present genomic evidence for the evolutionary dead-end hypothesis in a spider genus with three independent transitions to sociality. We assembled and annotated high-quality, chromosome-level reference genomes from three pairs of closely related social and subsocial Stegodyphus species. We timed the divergence between the social and subsocial species pairs to be from 1.3 million to 1.8 million years. Social evolution in spiders involves a shift from outcrossing to inbreeding and from an equal to a female-biased sex ratio, causing severe reductions in effective population size and decreased efficacy of selection. We show that transitions to sociality only had full effect on purifying selection at 119, 260, and 279 kya, respectively, and follow similar convergent trajectories of progressive loss of diversity and shifts to an increasingly female-biased sex ratio. This almost deterministic genomic response to sociality may explain why social spider lineages do not persist. What causes species extinction is not clear, but either could be selfish meiotic drive eliminating the production of males or could be an inability to retain genome integrity in the face of extremely reduced efficacy of selection.

Segmenting the individual word is the difficult stage in various data-extracting models for ancient handwritten papers. This involved visually seeking the query word and recognizing the text in every character. Segmenting the handwritten image document into words and text-line is challenging in visually recognizing the character. However, the handwritten document features are not regular and depend on the particular person, so it becomes a difficult issue. To tackle this challenging issue, we propose a new segmentation model for identifying the handwritten text style by utilizing the deep learning model. Initially, the handwritten text images are garnered from online databases based on the language of English, Greek, and Indian Bangla. After, the gathered image is given as input to the modified deep learning structure named Multi-Scale Adaptive TransRes- UNet (MSATResUNet) to segment the images very accurately. Here, the parameters present in the MSATResUNet are optimized using the Bald Eagle Search with Social Spider Optimization (BES-SSO) algorithm to maximize the segmentation performance. The segmentation results obtained from the developed model will be analyzed over various conventional segmentation approaches to validate the performance.