Search Results (117)

The recycling and reuse of discarded express delivery cartons can yield environmental, economic, and social benefits. A key factor influencing the volume of express packaging collected is the uncertainty in the total amount of such packaging within the service range of each collection point. Additional uncertainties include the costs associated with the construction of recycling stations, operational expenses, transportation costs, additional recycling fees, and government subsidies. To address the issue of express packaging recycling, a fuzzy integer programming model for the reverse logistics network of express packaging is constructed. The model aims to minimise the total network cost and maximise the total recycling rate while enabling decisions regarding the location of recycling facilities and the flow between facilities. Then, a memetic algorithm based on dynamic local search is designed. Several alternative solution approaches were considered to evaluate the proposed algorithm, including the precision optimization method (CPLEX) and a hybrid priority-based genetic algorithm. The results confirm the feasibility of the memetic algorithm. Finally, the applicability of this fuzzy programming model is analysed and validated by changing the confidence level. The case study results reveal quantifiable trade-offs: as the confidence level (α) increases from 0.75 to 0.90 under a fixed recycling rate threshold (ε = 80%), the total network cost rises approximately linearly, while the required number of recycling stations increases, with their average facility level upgrading accordingly. Variations in confidence levels and the degree of total recycling rate achievement can significantly influence the increase in target values. Moreover, the magnitude of this influence exhibits irregularity, indicating that changes in confidence levels entail a certain degree of risk. Full article

Polymer-based product use is rapidly increasing worldwide, resulting in critical social, environmental, ecological, economic, and health effects. Worldwide efforts have increasingly focused on solutions to the equilibrium consumption, production, and disposal of plastics to tackle these issues. The frontiers of biodegradable and bio-based polymers are continually advancing in pursuit of sustainability. Therefore, designing ecological bioplastics made of both biodegradable and bio-based polymers reveals chances to overcome plastic pollution and resource depletion. Polymeric materials are mainly used to manufacture different products at the beginning of their lifespans and which become waste after usage. Numerous sustainability strategies and polymer recycling methods are described and mostly classified into chemical, mechanical, and thermal recycling processes. This manuscript presents a New Polymers Frontier in Recycling and Sustainability Using an Ensemble of Deep Learning with a Heuristic Search Algorithm (NPFRS-EDLHSA). This work is devoted to computational polymer typology, which is based on machine learning algorithms applied to data on physicochemical properties. Although polymer classification can facilitate downstream materials research, the present study does not directly simulate recycling, environmental impacts, or sustainability. The main contributions made by this work include (i) an exploratory analysis of ensemble deep learning models to classify polymers by type on a small and unbalanced dataset; (ii) an evaluation of the effect of feature selection with a heuristic optimization methodology; and (iii) a comparison of the effects on classification performance under limited data conditions. This research sets out to provide a methodological explanation, not arguments for industrial-scale applicability. For the polymer-type classification process, the proposed NPFRS-EDLHSA model designs an ensemble of deep learning techniques, namely a bidirectional recurrent neural network (BiRNN) model, a bidirectional gated recurrent unit (BiGRU) method, and a graph autoencoder (GAE) technique. Finally, the grasshopper optimization algorithm (GOA) adjusts the hyperparameter values of the ensemble models optimally and results in an improved classification performance. A wide-ranging set of experiments was conducted to validate the performance of the NPFRS-EDLHSA method. The experimental results indicated that the NPFRS-EDLHSA technique achieved a better performance than an existing model. Full article

To enhance the performance of the Social Network Search (SNS) algorithm in solving complex numerical optimization problems, this paper proposes a Multi-strategy Enhanced Social Network Search (MESNS) algorithm. The original SNS simulates human social behaviors through four decision-making emotions—imitation, conversation, disputation, and innovation—to perform population-based search. However, its uniform emotion selection mechanism and purely random interaction strategy may reduce convergence efficiency and weaken exploitation capability, particularly in the later stages of optimization. To overcome these limitations, MESNS incorporates three improvement strategies. First, an adaptive decision-making emotion selection mechanism is developed to dynamically adjust the probabilities of exploration and exploitation behaviors according to the iteration progress, thereby promoting a more symmetric and coordinated search transition over time. Second, an elite-guided communication strategy is introduced to enhance information propagation by integrating high-quality individuals into the interaction process, which improves convergence while maintaining population diversity. Third, a dynamic interaction radius adjustment mechanism is designed to adaptively regulate the search step size, achieving a better balance and dynamic symmetry between global exploration and local refinement. Extensive experiments are conducted on the IEEE CEC2014, CEC2017, and CEC2022 benchmark suites under multiple dimensional settings. The results demonstrate that MESNS achieves superior optimization accuracy, faster convergence speed, and improved solution stability compared with several state-of-the-art metaheuristic algorithms. Furthermore, the proposed algorithm is successfully applied to the three-dimensional wireless sensor network deployment optimization problem, where it produces a more uniformly distributed and spatially balanced sensor layout, reduces coverage holes and redundant overlaps, and thus exhibits desirable symmetry in deployment structure and sensing coverage. These findings indicate that MESNS is an effective and competitive optimization framework for complex global optimization tasks with both theoretical significance and practical value from the perspective of symmetry. Full article

Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the IM problem is NP-hard, making exact solutions computationally infeasible for large-scale networks. Existing approximation methods typically rely either on static centrality heuristics, which often ignore global network structure, or on metaheuristic algorithms, which may suffer from slow convergence due to random initialization. This paper proposes a novel approach, termed Advanced Centrality-Driven Discrete Particle Swarm Optimization (DPSO), which integrates a weighted hybrid centrality score combining Degree, PageRank, and Betweenness centrality to guide the stochastic search process. In addition, a systematic grid search methodology is employed to determine the optimal weight configuration of the hybrid score. Experiments conducted on three real-world datasets (Twitter, ego-Facebook, and ca-HepTh) demonstrate that the optimal seeding strategy is strongly dependent on network topology. The results show that dense social networks favor popularity-based metrics such as Degree and PageRank, whereas sparse collaboration networks benefit significantly from bridge-oriented metrics such as Betweenness centrality. Overall, the proposed method achieves consistent improvements in influence spread across different network types, with the largest gains (up to 70%) observed in sparse network settings. Full article

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications. Full article

The multi-objective sustainable closed-loop supply chain network studied involves characteristics that produce high complexity due to the interaction of downstream and upstream strategic, tactical, and operational decisions, as well as sustainability elements. For this reason, a matheuristic algorithm, the Kernel search, is presented to solve large instances of the problem. After the algorithm parameter tuning, several instances are solved. A comparison with an augmented epsilon-constraint method is conducted in terms of speed and quality. The results show that the Kernel search matheuristic outperforms in the selected metrics, achieving an average improvement of 72% in computational time and from 0.47% to 28.18% in quality metrics. The solutions obtained deliver Pareto fronts in terms of economic, environmental, and social objectives. Full article

Feeder buses play an important role in supporting the accessibility of high-speed railway stations which leads to the improved efficiency of the transportation system. This paper proposes a new optimization technique for the design of feeder bus routes to the stations. It uses dynamic programming with a pulse algorithm seeking to maximize the number of serviced people considering the distance between the urban areas and high-speed railway station. The proposed algorithm was tested in a hypothetical network to find the optimum solutions and the running time needed. Moreover, the algorithm was applied to a real network as a case study in Aswan city, Egypt. Our results demonstrated significant improvements in the route design accuracy and efficiency. By applying the proposed algorithm, the potential demand values increased from 19.8% to 37.9% with a reasonable decrease in the running time compared to the literature. This research contributes to the advancement of transportation planning strategies by providing valuable insights into the optimization of feeder bus systems. The proposed model contributes to the scientific re-search and practical implementation by promoting a coordinated development of high-speed railway stations and urban areas. This may enhance the Egyptian high-speed railway technology, yielding substantial economic and social benefits. Full article

Artificial Intelligence (AI) has gained prominence on sustainability agendas while raising ethical, social, and environmental challenges. This study synthesizes evidence and maps the scientific production on Human-Centered AI (HCAI) at the interface with the Sustainable Development Goals (SDGs) for 2020–2024. Searches in Scopus and Web of Science (Boolean operators; thematic and temporal filters), followed by deduplication, yielded 265 articles, which were analyzed with Bibliometrix/Biblioshiny version 5.1.1 and VOSviewer version 1.6.20 (0) to generate term co-occurrence maps, collaboration networks, and bibliographic coupling. The results indicate accelerated growth and diffusion of the topic, with journals such as Sustainability, IEEE Access, and Applied Sciences standing out. Three interdependent axes were identified: (i) technical performance, with emphasis on machine learning and deep learning; (ii) explainability and human-centeredness (XAI, ethics, and algorithmic governance); and (iii) socio-environmental applications oriented toward the SDGs. Underrepresentation of the Global South, particularly Brazil, was observed. It is concluded that HCAI is being consolidated as an emerging interdisciplinary field with potential to accelerate the SDGs, although there remains a need to integrate ethical, regional, and impact-assessment dimensions more systematically to achieve global targets effectively. Full article

Personalized search and recommendation algorithms for multi-modal data have attracted widespread attention. However, existing methods often struggle with effectively integrating multi-source information and performing global search in complex optimization problems. To address these limitations, this paper proposed a denoising diffusion model-driven adaptive estimation of a distribution algorithm integrating multi-modal data. Multi-modal user-generated contents are extensively collected, such as users’ interaction behaviors, category tags, text comments, images, social network relationships, etc. A user interest preference model based on a denoising diffusion model is established by learning the fusion representation of multi-modal data, which extracts user preference features. The surrogate model based on user preferences and adaptive estimation of distribution strategies is presented in the framework of an estimation of distribution algorithm. A surrogate-driven adaptive estimation of distribution algorithm is designed to align with users’ cognitive experiences and behavioral patterns, thereby enhancing the optimization capability of the personalized search algorithm. Additionally, a dynamic model management mechanism is established to update the user interest preference model with new available modal information, which tracks the changes in users’ interest preferences in real-world scenarios. It assists users in efficiently filtering items that match their preferences from large-scale information sources. Extensive experiments on general public datasets demonstrate the feasibility, effectiveness, and superiority of the proposed algorithm, confirming its improvements in both search efficiency and recommendation performance for a personalized recommendation algorithm. Full article

To address real-world engineering design optimization problems, this study proposes a reinforcement learning enhanced multi-objective social network search algorithm (QMOSNS), which represents a novel approach for solving multi-objective optimization problems. QMOSNS utilizes Halton sequences for population initialization to enhance the diversity of the initial population. A multi-objective archive mechanism is implemented to store Pareto-optimal solutions and select parental individuals through a reassigned fitness evaluation strategy. Furthermore, Q-learning is incorporated to adaptively select mutation operators, thereby dynamically balancing the algorithm’s exploration and exploitation capabilities. QMOSNS was rigorously evaluated through 50 prominent case studies, including 22 unconstrained multi-objective benchmark problems, 18 constrained multi-objective benchmark problems, and 10 multi-objective engineering design problems, to comprehensively validate its computational capabilities and effectiveness. Moreover, statistical results obtained using consistent performance metrics were compared with those of other highly regarded algorithms to ensure a fair and objective performance assessment. The comparative results show that QMOSNS is robust and superior in handling a wide variety of multi-objective problems. This study underscores the efficacy of integrating reinforcement learning with social intelligence for tackling complex multi-objective optimization in engineering and computational domains. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

As a subclass of Mobile Ad hoc Networks (MANETs), Vehicle Ad hoc Networks (VANETs) possess multi-hop relay communication and dynamic topology reconstruction capabilities and are widely applied in various social activities. When they are used as clusters to perform various disaster search and rescue operations or communication relay, reliable, secure, and timely communication connectivity becomes particularly important. This paper focuses on the research of connectivity reliability in VANETs, emphasizing the impact of node movement characteristics and various failure modes on the connectivity reliability of VANETs: As a cluster, the nodes in VANETs have interactive relationships and no longer follow a random movement model, exhibiting regular movements of the network as a whole; the failure modes of nodes in VANETs include vehicular hardware/software failure, energy consumption failure, intentional attack, and isolation failure. Additionally, to optimize node communication energy consumption, the paper proposes a routing path identification algorithm. Finally, the paper presents a simulation algorithm for solving the connectivity reliability of VANETs. Through MATLAB simulation experiments, the effectiveness and correctness of the proposed algorithm are verified, and it is found that the attraction distance between nodes has a certain impact on the isolation failure mode and connectivity reliability. Full article

Phishing remains a persistent and evolving cyber threat, constantly adapting its tactics to bypass traditional security measures. The advent of Machine Learning (ML) and Neural Networks (NN) has significantly enhanced the capabilities of automated phishing detection systems. This comprehensive review systematically examines the landscape of ML- and NN-based approaches for identifying and mitigating phishing attacks. Our analysis, based on a rigorous search methodology, focuses on articles published between 2017 and 2024 across relevant subject areas in computer science and mathematics. We categorize existing research by phishing delivery channels, including websites, electronic mail, social networking, and malware. Furthermore, we delve into the specific machine learning models and techniques employed, such as various algorithms, classification and ensemble methods, neural network architectures (including deep learning), and feature engineering strategies. This review provides insights into the prevailing research trends, identifies key challenges, and highlights promising future directions in the application of machine learning and neural networks for robust phishing detection. Full article

Masonry construction remains labor-intensive, with current block placement predominantly dependent on workers’ empirical knowledge. Lack of systematic cutting plans induces substantial material waste and rework, adversely affecting sustainability. We propose a two-phase optimization framework to automate and enhance masonry block arrangement efficiency. Phase 1 decomposes masonry structures into optimizable subregions by geometric features, documenting each region’s geometry and position to generate optimization datasets. Phase 2 implements a computational module using the Social Network Search (SNS) algorithm to optimize subregion layouts, recording post-optimization block coordinates and dimensions. Finally, it materializes layout configurations and generates block quantity schedules to provide precise material demand data. An integrated prototype system was implemented in four specialized block arrangement scenarios and one building case study, validating both functionality and efficiency. Full article

The Parrot Optimization Algorithm (PO) represents a contemporary nature-inspired metaheuristic technique formulated through observations of Pyrrhura Molinae parrot behavioral patterns. PO exhibits effective optimization capabilities by achieving equilibrium between exploration and exploitation phases through mimicking foraging behaviors and social interactions. Nevertheless, during iterative progression, the algorithm encounters significant obstacles in preserving population diversity and experiences declining search effectiveness, resulting in early convergence and diminished capacity to identify optimal solutions within intricate optimization landscapes. To overcome these constraints, this work presents the Adaptive Differentiated Parrot Optimization Algorithm (ADPO), which constitutes a substantial enhancement over baseline PO through the implementation of three innovative mechanisms: Mean Differential Variation (MDV), Dimension Learning-Based Hunting (DLH), and Enhanced Adaptive Mutualism (EAM). The MDV mechanism strengthens the exploration capabilities by implementing dual-phase mutation strategies that facilitate extensive search during initial iterations while promoting intensive exploitation near promising solutions during later phases. Additionally, the DLH mechanism prevents premature convergence by enabling dimension-wise adaptive learning from spatial neighbors, expanding search diversity while maintaining coordinated optimization behavior. Finally, the EAM mechanism replaces rigid cooperation with fitness-guided interactions using flexible reference solutions, ensuring optimal balance between intensification and diversification throughout the optimization process. Collectively, these mechanisms significantly improve the algorithm’s exploration, exploitation, and convergence capabilities. Furthermore, ADPO’s effectiveness was comprehensively assessed using benchmark functions from the CEC2017 and CEC2022 suites, comparing performance against 12 advanced algorithms. The results demonstrate ADPO’s exceptional convergence speed, search efficiency, and solution precision. Additionally, ADPO was applied to wind power forecasting through integration with Long Short-Term Memory (LSTM) networks, achieving remarkable improvements over conventional approaches in real-world renewable energy prediction scenarios. Specifically, ADPO outperformed competing algorithms across multiple evaluation metrics, achieving average R² values of 0.9726 in testing phases with exceptional prediction stability. Moreover, ADPO obtained superior Friedman rankings across all comparative evaluations, with values ranging from 1.42 to 2.78, demonstrating clear superiority over classical, contemporary, and recent algorithms. These outcomes validate the proposed enhancements and establish ADPO’s robustness and effectiveness in addressing complex optimization challenges. Full article