Optimizing diverse team formation with swarm intelligence algorithms for enhancing organizational performance

Cable manufacturing is a typical hybrid production system characterized by the deep coupling of continuous processes and discrete logic. However, the unique “Reel-Splitting Constraint”—where continuous cables must be segmented into strictly sequenced sub-reels—along with high energy consumption and frequent dynamic disturbances render traditional Hybrid Flexible Flow Shop scheduling models ineffective in this context. To address these challenges, this paper proposes a novel Multi-Objective Dynamic Scheduling Framework tailored for the cable industry. First, a mathematical model is constructed that explicitly formalizes the rigid logic of sub-reel sequencing and continuous material flow, aiming to simultaneously minimize total energy consumption, makespan, and changeover times. Unlike generic models, this formulation introduces a constraint-handling mechanism to ensure the physical continuity of sub-reels during optimization. Second, a two-stage hybrid swarm intelligence algorithm is developed to solve this NP-hard problem. An improved Ant Colony Optimization (ACO) algorithm is employed for “population seeding” to generate feasible initial schedules and avoid deadlocks, while a Variable Neighborhood Search (VNS) executes deep evolutionary operations—such as setup reduction and critical operation insertion—to escape local optima. Case studies based on real-world industrial data demonstrate the superior performance of the proposed method. The hybrid strategy reduces the makespan by approximately 9.8% compared to traditional approaches and effectively mitigates energy waste in bottleneck processes. Furthermore, the proposed event-driven dynamic rescheduling mechanism exhibits exceptional responsiveness, reducing rescheduling time for unexpected equipment breakdowns from 18 h to 0.83 h, thereby enabling within-shift decision-making and robust operation in volatile manufacturing environments.

Wireless Sensor Networks (WSNs) are characterized by limited energy, and energy efficiency is one of thekey design issues for routing protocols. This research aims to enhance the routing efficiency of dragonflyswarm routing by optimizing route and cluster head selection through the integration of the latest SwarmIntelligence (SI) algorithm, specifically the Dragonfly Algorithm (DA). The proposed method wassystematically compared with the traditional Particle Swarm Optimization (PSO) by measuring energyefficiency, execution time, and packet delivery ratio. Simulation results showed that the DragonflyAlgorithm reduces energy consumption and prolongs network lifetime for classical methods. It exhibitsstrong adaptability to time-varying network topologies and is less likely to be trapped in a local optimum.These results illustrate that SI is a promising technique to help improve the quality of routing protocolsin WSN applied to critical scenarios and offer possibilities for future integration with, for example,machine learning techniques for achieving higher performance.

The efficacy of swarm intelligence algorithms in navigating high-dimensional, non-convex landscapes depends on the dynamic balance between global exploration and local exploitation. Drawing inspiration from the intricate social dynamics of Pyrrhura molinae, this study proposes a novel bio-inspired metaheuristic, the Comprehensive Learning Parrot Optimizer (CL-PO). While the original Parrot Optimizer (PO) simulates collective foraging and communication, it often suffers from population homogenization and entrapment in local optima due to its reliance on single-source social learning. To address these limitations, CL-PO incorporates a dimension-wise multi-exemplar social learning mechanism analogous to the cross-individual knowledge transfer observed in avian colonies. This strategy enables stagnant individuals to reconstruct their search trajectories by learning from multiple superior peers, thereby sustaining population diversity and facilitating adaptive exploration. Rigorous benchmarking on 29 test functions from the CEC 2017 suite reveals that CL-PO achieves statistically superior performance compared to nine state-of-the-art algorithms, securing a top-tier average Friedman rank of 1.28. Furthermore, the practical utility of CL-PO is substantiated through a complex reservoir production optimization task using the Egg benchmark model, where it consistently maximizes the net present value (NPV), reaching 9.625×108 USD. These findings demonstrate that CL-PO is a powerful and reliable solver for addressing large-scale engineering optimization problems under complex constraints.

Aiming at the drawbacks of slow convergence, proneness to local optima, and insufficient segmentation accuracy of traditional multi-threshold image segmentation algorithms in complex scenarios, an Improved Grey Wolf Optimizer (IGWO) integrated with opposition-based learning strategy and nonlinear dynamic convergence factor is proposed. Specifically, we use improved opposition-based learning for population initialization and iteration optimization. This step helps increase population diversity and speed up convergence. Instead of the traditional linear decreasing convergence factor, we adopt a nonlinear dynamic one. This change achieves an adaptive balance between global exploration and local exploitation of the algorithm. We take the maximum between-class variance (OTSU) as the fitness function and build a multi-threshold segmentation optimization model. We validate IGWO through 6 benchmark test functions and compare it with four advanced swarm intelligence algorithms. The results show that IGWO has obvious advantages in convergence speed, solution accuracy and stability. It also has a strong ability to avoid local optimal solutions. When applied to multi-threshold image segmentation, IGWO produces segmentation regions with clear boundaries and well-preserved details. This algorithm provides a new technical method for efficient and accurate segmentation of complex images. It can be used in fields such as computer vision and communication equipment fault detection.

Brain tumors often require treatment and multiple biopsies. They are the third most common cancer among young adults in both incidence and mortality. The expression of the O6-methylguanine-DNA methyltransferase (MGMT) gene plays an important role in predicting tumor behavior. It affects how patients respond to chemotherapy and may reduce the need for invasive procedures. Machine learning can help make accurate medical predictions, but it requires large and diverse patient datasets. These datasets are difficult to access due to privacy and legal restrictions. This article proposes a Federated Learning (FL) framework to address these challenges. FL allows different institutions to train a shared model without exchanging raw data. A hybrid deep learning model combining recurrent neural networks (RNNs) and convolutional neural networks (CNNs) is developed to analyze magnetic resonance imaging (MRI) scans from the BraTS 2021 dataset. The model aims to detect glioblastoma and predict MGMT gene expression. Two swarm intelligence algorithms, the Bayesian Search Optimization Algorithm and the Sparrow Search Optimization Algorithm, are used to optimize the model’s hyperparameters. The FL system was tested across ten universities. It performed similarly to models trained on centralized data. The proposed model, BrainGeneDeepNet, achieved high performance: 0.9758 accuracy, 0.0769 loss, 0.9980 AUC, 0.9770 recall, and 0.9782 precision. These results show that federated learning is a secure and effective approach for medical imaging and biomarker prediction.

Physics-Informed LSTM optimized by swarm intelligent algorithm for dynamic error compensation of ground vehicle gravity measurement

Abstract Addressing the susceptibility of multi-section rotors to critical resonance under high-speed conditions, as well as the high cost and low flexibility of traditional structural adjustments, this paper investigates control methods centered on support parameter optimization for a four-section rotor-bearing system. A dynamic model of the spring-damper support system is first established. Through a comparative analysis, the dynamic stiffness method is adopted to enhance the efficiency of numerical calculations. Furthermore, an improved hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) is designed. A fitness function is constructed targeting the amplitude at critical speeds, employing adaptive inertia weights and learning factors. The introduction of crossover and mutation operations is intended to ensure the maintenance of population diversity, whilst also enhancing global search capability and convergence accuracy. The findings demonstrate the efficacy of this method in suppressing critical amplitudes under complex constraints, while concomitantly enhancing convergence and stability. This study provides an intelligent solution for the dynamic optimization of four-section rotor-bearing systems, expands the application of swarm intelligence algorithms in mechanical system optimization, and possesses significant theoretical and engineering value.

Abstract Network Coverage is the significant problem of concern in Wireless Sensor Networks (WSNs) as it highly important in the rapidly evolving area of the Internet of Things (IoT). But network coverage performance optimization is highly challenging due to the complex geographical environments over which they are deployed for the objective of monitoring and achieving reactive decision making. The classical optimization algorithm faces the challenge of introducing coverage balance with respect to restricted energy resources. In this paper, Meerkat Optimizer-based Reliable Network Coverage Optimization Mechanism (MKARNCO) is proposed for improving network coverage. This adopted Meerkat Optimizer derives the merits from the hunting and alarm-raising behaviours of meerkats in nature. It specifically uses the mechanism of sentry for balancing the tradeoff between exploration and exploitation for the purpose of achieving better solution diversity and prevent local optima. The results of this MKARNCO approach in the obstacle free scenarios, conformed a coverage rate of 99.71%. It also confirmed that the proposed MKARNCO approach under the presence of obstacles confirmed a better coverage rate of 99.38%, compared to the baseline approaches.

In this work, a systematic analytical procedure of Tm-doped germanate glass optimization and corresponding fiber amplifier simulation is established. First, a method for adjusting the Tm-Tm distance in the glass matrix is proposed and validated by molecular dynamics combined with microscopic energy transfer parameters. The results show that the shortest Tm-Tm distance increases from 3.8 Å to 8.5 Å, leading to a weakening of the cross-relaxation (CR) process and an enhancement of the S-band emission of Tm. Secondly, a swarm intelligence algorithm that can derive both the forward and reverse macroscopic CR rates synchronously is established for the first time, to the best of our knowledge. The calculated results show that the forward and reverse macroscopic CR rates are regulated from 0.36(± 0.01) × 10-22 m3/s and 5.60(± 0.01) × 10-22 m3/s to 0.30(± 0.01) × 10-22 m3/s and 7.92(± 0.01) × 10-22 m3/s, respectively, while the reverse-forward ratio increases from 15.58 ± 0.01 to 26.36 ± 0.01. Finally, the numerical model of the Tm-doped fiber amplifier on the basis of the presented two kinds of germanate glasses is studied. The numerical results indicate that the ASE power in the 1800-nm band can be reduced by enlarging the reverse-forward CR rate ratio, while the S-band amplification effect is improved.

ABSTRACT With the advancement of science and technology and the rapid development of the socio‐economy, unmanned aerial vehicles (UAVs) are playing an increasingly important role in daily economic activities. In civilian applications, UAVs can perform tasks such as high‐voltage power line and oil pipeline inspections, as well as logistics transportation. In the military domain, they are widely used for logistics supply and delivery, battlefield situational monitoring, and reconnaissance in complex environments. Therefore, research on three‐dimensional (3D) obstacle‐avoidance autonomous path planning for UAVs has significant practical engineering applications. This paper proposes a deep reinforcement learning (DRL)–improved mayfly algorithm (IMA) for UAV autonomous obstacle‐avoidance path planning. Considering the limitations of the mayfly algorithm (MA), such as the high randomness in initial population generation and slow convergence speed during iteration, Halton sequences and an adaptive Gaussian–Cauchy mutation strategy are introduced to balance the global exploration and local optimization capabilities of the MA. Furthermore, recognizing that the IMA still cannot overcome inherent drawbacks of swarm intelligence algorithms, such as the random selection of mutation strategy probability distributions and the lack of individual strategy optimization due to simultaneous group strategy optimization, this paper applies the deep deterministic policy gradient (DDPG) algorithm from DRL to update the population positions in the IMA. This further enhances the algorithm's optimization performance, convergence ability, and computational efficiency, thereby achieving autonomous obstacle‐avoidance path planning for UAVs. The performance of the 3D UAV paths optimized by the traditional MA, the IMA, and the DRL–IMA is compared to validate the effectiveness of the proposed algorithm. The simulation's output indicates that the introduced DRL–IMA brings down the average fitness value to 0.05022 after 600 iterations and it takes just 72 generations to converge. This shows that the new method not only converges faster but also has better stability than both the old and modern MAs.

Swarm intelligence algorithms inspired by natural and artificial systems have demonstrated strong capability in solving complex optimiza-‎tion problems. This study proposes a novel population-based metaheuristic, termed the Urban Traffic Flow Optimization Algorithm ‎‎(UTFOA), which is inspired by adaptive decision making and self-organized traffic dynamics observed in modern urban environments. In ‎the proposed framework, each search agent is modeled as an autonomous driver navigating toward an optimal route under dynamically ‎evolving traffic conditions. The algorithm captures three fundamental traffic behaviors, namely route exploration, adaptive following, and ‎congestion avoidance, and formulates them as mathematical operators that jointly balance global exploration and local exploitation. In addition, traffic pressure and driver experience mechanisms are incorporated to regulate adaptive behavior throughout the iterative search process. ‎Theoretical analysis indicates that the proposed algorithm preserves population diversity, satisfies global convergence conditions under ‎Markov chain theory, and exhibits controllable computational complexity. The proposed model introduces a human-inspired perspective for ‎designing adaptive optimization algorithms‎.

Abstract Medical datasets often contain numerous redundant or noisy features, which can degrade classification performance and increase computational costs. Feature selection (FS) is therefore crucial for improving diagnostic accuracy and enhancing model interpretability in biomedical applications. Although the recently introduced enzyme action optimizer (EAO) has shown strong potential as a metaheuristic method, its effectiveness for FS and its behavior in high-dimensional spaces remain underexplored. Like many swarm intelligence (SI) algorithms, EAO faces challenges in maintaining population diversity, achieving a balanced exploration–exploitation process and avoiding premature convergence. To address these limitations, this study proposes an enhanced enzyme action optimizer (EEAO) for FS. The method integrates latin hypercube sampling to ensure diverse initialization, refraction learning to enhance global exploration and a crowding-distance mechanism to reduce solution clustering and improve search stability. A binary version of EEAO is further developed using an S-shaped transfer function to efficiently handle FS tasks. Due to the iterative nature of the algorithm and the high dimensionality of biomedical datasets, EEAO-FS is computationally intensive; implementing it using parallel, distributed or HPC-enabled frameworks can significantly accelerate convergence and enable scalable analysis of large datasets. The method is evaluated against eight state-of-the-art FS methods on twenty biomedical datasets using five performance metrics. Experimental results show that EEAO achieves the highest average accuracy (91.85%), selects substantially fewer features (approximately 60% reduction), converges more consistently and requires less execution time compared to competing methods. Statistical analysis confirms the significance of these improvements. EEAO’s global optimization performance is validated on eight benchmark functions, showing superior exploration, faster convergence and robustness. These results demonstrate its efficiency and reliability for high-dimensional medical FS while highlighting its suitability for high-performance and scalable computational frameworks.

With the rapid growth of vessel traffic and the widespread adoption of the Automatic Identification System (AIS) in recent years, analyzing maritime traffic flow characteristics has become an essential component of modern maritime supervision. Clustering analysis is one of the primary data-mining approaches used to extract traffic patterns from AIS data. Addressing the challenge of assigning appropriate weights to the multidimensional features in AIS trajectories, namely latitude and longitude, speed over ground (SOG), and course over ground (COG). This study introduces an adaptive parameter optimization mechanism based on evolutionary algorithms. Specifically, Particle Swarm Optimization (PSO), a representative swarm intelligence algorithm, is employed to automatically search for the optimal feature-distance weights and the core parameters of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling dynamic adjustment of clustering thresholds and global optimization of model performance. By designing a comprehensive clustering evaluation index as the objective function, the proposed method achieves optimal parameter allocation in a multidimensional similarity space, thereby uncovering maritime traffic clusters that may be overlooked when relying on single-dimensional features. The method is validated using AIS trajectory data from the Xiamen Port area, where 15 traffic clusters were successfully identified. Comparative experiments with two other clustering algorithms demonstrate the superior performance of the proposed approach in trajectory pattern analysis, providing valuable reference for maritime regulatory and traffic management applications.

The Tree-Seed Algorithm (TSA) is a swarm intelligence algorithm inspired by the propagation relationship between trees and seeds. However, the original TSA is prone to premature convergence and becomes trapped in local optima when addressing high-dimensional, complex optimization problems, limiting its practical efficacy. To overcome these limitations, this paper proposes an Adaptive and Migration-enhanced Tree Seed Algorithm (AMTSA), which integrates three key mechanisms to significantly enhance performance in solving complex optimization tasks. First, to effectively evade local optima, an adaptive tree migration mechanism is designed to dynamically adjust the search step-size and direction based on individual fitness, thereby improving global exploration. Second, to enhance the algorithm’s adaptability and efficiency across different search stages, an adaptive seed generation strategy based on the dynamic Weibull distribution is introduced. This strategy enables flexible control over the number of seeds and promotes a balanced search throughout the solution space. Third, to mitigate convergence oscillations during the global search, a nonlinear step-size adjustment function inspired by the GBO algorithm is incorporated, which effectively improves convergence stability by responding to the iteration progress. Rigorous testing on the IEEE CEC 2014 benchmark functions demonstrates that AMTSA’s overall performance surpasses not only state-of-the-art optimizers like JADE and LSHADE but also recent TSA variants, including STSA, fb-TSA, and MTSA. To further validate its robustness in high-dimensional spaces, AMTSA was tested on 30 benchmark functions at 30, 50, and 100 dimensions. Results show that AMTSA ranked first in the number of functions optimized best and exhibited the fastest convergence speed among all compared algorithms. In a real-world application, AMTSA was employed to optimize multi-threshold segmentation for lung cancer CT images. The resulting AMTSA-SVM classification model achieved an accuracy of 89.5%, significantly outperforming models such as standard SVM (76.22%), DE-SVM (82%), GA-SVM (79.33%), TSA-SVM (84.44%), and JADE-SVM (89.12%). In conclusion, the proposed AMTSA, by integrating adaptive migration, dynamic seed generation, and nonlinear step-size control, successfully addresses the inherent deficiencies of the native TSA, offering a more efficient and robust tool for solving high-dimensional, complex optimization problems. The AMTSA source code will be available at www.jianhuajiang.com.

The Tree-Seed Algorithm (TSA) is a swarm intelligence algorithm inspired by the propagation relationship between trees and seeds. However, the original TSA is prone to premature convergence and becomes trapped in local optima when addressing high-dimensional, complex optimization problems, limiting its practical efficacy. To overcome these limitations, this paper proposes an Adaptive and Migration-enhanced Tree Seed Algorithm (AMTSA), which integrates three key mechanisms to significantly enhance performance in solving complex optimization tasks. First, to effectively evade local optima, an adaptive tree migration mechanism is designed to dynamically adjust the search step-size and direction based on individual fitness, thereby improving global exploration. Second, to enhance the algorithm's adaptability and efficiency across different search stages, an adaptive seed generation strategy based on the dynamic Weibull distribution is introduced. This strategy enables flexible control over the number of seeds and promotes a balanced search throughout the solution space. Third, to mitigate convergence oscillations during the global search, a nonlinear step-size adjustment function inspired by the GBO algorithm is incorporated, which effectively improves convergence stability by responding to the iteration progress. Rigorous testing on the IEEE CEC 2014 benchmark functions demonstrates that AMTSA's overall performance surpasses not only state-of-the-art optimizers like JADE and LSHADE but also recent TSA variants, including STSA, fb-TSA, and MTSA. To further validate its robustness in high-dimensional spaces, AMTSA was tested on 30 benchmark functions at 30, 50, and 100 dimensions. Results show that AMTSA ranked first in the number of functions optimized best and exhibited the fastest convergence speed among all compared algorithms. In a real-world application, AMTSA was employed to optimize multi-threshold segmentation for lung cancer CT images. The resulting AMTSA-SVM classification model achieved an accuracy of 89.5%, significantly outperforming models such as standard SVM (76.22%), DE-SVM (82%), GA-SVM (79.33%), TSA-SVM (84.44%), and JADE-SVM (89.12%). In conclusion, the proposed AMTSA, by integrating adaptive migration, dynamic seed generation, and nonlinear step-size control, successfully addresses the inherent deficiencies of the native TSA, offering a more efficient and robust tool for solving high-dimensional, complex optimization problems. The AMTSA source code will be available at www.jianhuajiang.com.

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time () to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation.

As a non-conventional thermal material removal process, electro-discharge machining (EDM) is preferred when it comes to complex features and with high-precision contours as well as for materials that cannot be processed via conventional material removal operations. Nevertheless, several phenomena may adversely affect surface integrity of EDMed components, thus; they should be considered and experimentally investigated to optimize electro-discharge machining process. This paper experimentally examines surface integrity as regards surface roughness average Ra when using AA 5083 as a workpiece material and copper as electrode. Experiments were designed and carried out according to Taguchi L9 orthogonal design, setting three different levels of the process control parameters namely discharge (peak) current, pulse-on time and pulse-off time. Three algorithms namely moth-flame algorithm, dragonfly algorithm and whale optimization algorithm were employed to minimize the response. All algorithms performed adequately; however obvious differences in convergence speed and optimal solution results were found. The lowest result for surface roughness was found equal to 4.410 μm by DA and WOA algorithms at 35th and 5th iterations respectively, whereas DA was converged to its lowest score equal to 4.736 μm after 35 iterations.

This paper presents two novel hybrid iterative schemes that combine Newton’s method and its variant with the Tuna Swarm Optimization (TSO) algorithm, aimed at solving complex nonlinear equations with enhanced accuracy and efficiency. Newton’s method is renowned for its rapid convergence in root-finding problems, and it is integrated with TSO, a recent swarm intelligence algorithm that surpasses the complex behavior of tuna fish in order to optimize the search for superior solutions. These hybrid methods are reliable and efficient for solving challenging mathematical and applied science problems. Several numerical experiments and applications involving ordinary differential equations have been carried out to demonstrate the superiority of the proposed hybrid methods in terms of convergence rate, accuracy, and robustness compared to traditional optimization and iterative methods. The stability and efficiency of the proposed methods have also been verified. The results indicate that the hybrid approaches outperform traditional methods, making them a promising tool for solving a wide range of mathematical and engineering problems.

Aiming at the optimization problem of multiloitering munitions cooperatively surrounding moving targets, this paper proposes a cooperative surrounding and attack method based on the wolf pack algorithm (WPA). Firstly, the relative kinematics model between the munitions and the target is established, and the cooperative surrounding task flow is analyzed in detail. Secondly, leveraging the “wandering–summoning–besieging” mechanism of the WPA, a comprehensive fitness function incorporating multiple constraints is designed, including favorable attack radius, attack angle constraint, surrounding area constraint, intermunition collision avoidance, and desired surrounding points. This method utilizes the WPA to distributively solve for the optimal control command in each decision cycle, guiding the munition cluster to maneuver cooperatively and achieve an even distribution around the target. Simulation results demonstrate that compared with other swarm intelligence algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), and sparrow search algorithm (SSA), the proposed method exhibits superior performance in terms of convergence speed, solution accuracy (fitness value), and computational efficiency (CPU time). It effectively achieves cooperative surrounding and attack against both single and multiple targets, significantly enhancing combat effectiveness and providing a novel solution for the cooperative decision‐making of intelligent munition clusters.