Search Results (3,365)

This paper presents a symmetry-guided multi-elite Gekko Japonicus Algorithm, termed MIGJA, for global optimization and multi-threshold image segmentation. The method modifies the original GJA from three aspects. In the movement stage, a success-rate feedback mechanism is used to adapt the Lévy-flight probability and step-size coefficient according to recent search behavior, allowing the population to switch more flexibly between exploration and exploitation. In the guidance stage, several elite individuals are combined to form a weighted collaborative center, which reduces the excessive dependence on a single best solution and provides a more balanced search direction. In the reconstruction stage, historical memory and differential information are introduced into the tail reconstruction process to help inferior or stagnant individuals move out of local regions during the later search phase. The proposed MIGJA is tested on the CEC2017 and CEC2020 benchmark suites and further applied to Otsu-based multi-threshold image segmentation. The numerical results show that MIGJA performs competitively in terms of convergence accuracy and stability. According to the Friedman mean-rank results, MIGJA ranks first in all test settings, with mean-rank reductions of about 78.8–84.5% compared with the original GJA and 66.7–76.1% compared with the strongest competitor. In the segmentation experiments, MIGJA also obtains favorable objective function values and image quality metrics, including PSNR, FSIM, and SSIM. These findings suggest that the proposed algorithm is suitable for both benchmark optimization and multi-threshold image segmentation tasks. Full article

Existing metaheuristics for the permutation flow-shop scheduling problem primarily explore the search space directly. This study presents a new decomposition technique that, unlike those methods, repeatedly solves one-machine subproblems and extends their solutions to all machines in order to minimize the makespan objective. To this end, the proposed algorithm solves the one-machine problem using a novel forward-backward procedure and extends its solutions to all machines through recurrent displacement of jobs. The improvement of the current solution proceeds until no optimal permutation for a single machine can improve the overall permutation for all machines, gradually improving current solutions and directing the search towards obtaining high-quality solutions. To further improve the results, any solution proposed by the one-machine solution strategy is refined by a local search process. An innovative triangular mechanism is also proposed for constructing initial solutions, with the aim of providing a high-quality starting point for the algorithm. The results of computational experiments not only demonstrate the efficiency of the one-machine forward-backward technique, but also indicate that the algorithm is both robust and highly effective in solving the standard benchmark instances. Full article

In this paper, we propose the implementation of a Fuzzy Inference System (FIS) for the systematic evaluation of exploration and exploitation capabilities in population-based Metaheuristic Optimization Algorithms (MOAs). Our approach involves an efficient feature extraction scheme applied through different stages of an algorithm’s search process, from which Dimension-Wise Diversity (DD), Average Distance to Global Best (DB), and Fitness Diversity (FD) are extracted and utilized as inputs for the proposed Mamdani-style FIS. These metrics feature zero additional objective function evaluations and are processed in real time by our proposed Mamdani-style inference system using symmetric triangular membership functions. Since the proposed FIS-based evaluation scheme relies on static membership boundaries and elementary min-max aggregation operators, constant time complexity is ensured, which contrasts with the expensive matrix operations that are frequently required in probabilistic models. To validate this framework, a comprehensive experimental design was performed. Such experiments involve testing with MOAs proposing unique and distinctive search strategies, including well-known examples such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Differential Evolution (DE) and others. Our experiments also consider 14 benchmark functions (chosen by considering scalability and landscape variety) and three operational configurations (which consider varying numbers of decision variables, population size and iterations). Our results not only expose critical differences in exploration/exploitation capabilities among the compared strategies but also allow the visualization of underlying process characteristics, such as convergence speed and repeatability. Our results imply that the most successful MOAs are those which prioritize exploration by no more than 25% percent of the search process, while also manifesting a successful exchange from full exploration to full exploitation by the end of such process, with PSO and DE being among the most consistent examples. Full article

Soil temperature wireless sensor networks provide essential data for continuous soil temperature monitoring in cotton fields and support agricultural environmental regulation and crop growth management. However, conventional sensor node deployment methods often lead to coverage blind spots, redundant coverage, and insufficient utilization of sensing resources, which restrict network monitoring performance. To address these issues, this study proposes an Adaptive Chaotic Lévy Flight Prairie Dog Optimization algorithm, named ACLFPDO, for optimizing node deployment in soil temperature wireless sensor networks. The incremental novelty of ACLFPDO does not lie in the individual use of chaotic initialization, adaptive parameter adjustment, or Lévy-flight perturbation, which have been widely used in metaheuristic optimization, but in coupling these strategies with a stage-based Prairie Dog Optimization (PDO) position-updating framework and a coverage-oriented fitness design tailored to the STWSN area-coverage problem. An idealized two-dimensional simulation model was established, in which the cotton-field monitoring region was simplified as a regular square area and each sensor node was modeled using a fixed-radius circular binary sensing model. Coverage rate and node utilization efficiency were used as the main evaluation metrics. Comparative simulations were conducted against Prairie Dog Optimization, Snake Optimization (SO), and Whale Optimization algorithms (WO). Under a monitoring area side length of 50, sensing radius of 5, and 42 sensor nodes, ACLFPDO achieved a coverage rate of 98.49% and a node utilization efficiency of 74.65%. Compared with PDO, SO, and WO, the coverage rate increased by 16.88, 9.52, and 10.86 percentage points, respectively. The results indicate that ACLFPDO can improve coverage performance and sensing resource utilization under idealized simulation conditions. However, practical cotton-field deployment still requires further consideration of irregular boundaries, ridges, furrows, obstacles, burial-depth differences, communication connectivity, energy consumption, and soil spatial heterogeneity. Full article

Accurate power load forecasting is critical for the efficient operation of industrial microgrids. However, raw meteorological and consumption data typically exhibit non-stationary characteristics, complicating the hyperparameter tuning of deep learning models, and subsequently degrading the prediction accuracy of these frameworks. To address the aforementioned challenges, a new hierarchical forecasting structure denoted as INRBO-SSA-LSTM is proposed in this paper. First, Pearson correlation analysis is employed for feature reduction, identifying the four main factors to mitigate the dimensionality curse. Building upon this foundation, a refined Newton-Raphson-Based Optimizer (INRBO) is introduced, integrating a cosine adaptive t-distribution perturbation, a boundary-aware non-uniform steering scheme, and a fitness-aware hybrid perturbation mechanism. Evaluated against the CEC2022 benchmark suite, comprehensive evaluations reveal that the INRBO demonstrates superior global exploration and local refinement capabilities compared to baseline algorithms when assessed on the CEC2022 benchmark suite for foundational optimization performance. Furthermore, rigorous testing on the CEC2017 suite across 10, 30, and 50 dimensions successfully validates its exceptional robustness and search capabilities in high-dimensional spaces. INRBO functions as a dual-stage optimizer within the proposed framework; in the initial phase, it dynamically calibrates the parameters of Singular Spectrum Analysis (SSA) to extract deterministic load patterns, achieving a maximum signal-to-noise ratio of 15.87 dB; in the second phase, it optimizes the global hyperparameters of the Long Short-Term Memory (LSTM) network. Validated using actual industrial microgrid data in Jiangsu Province, China, the proposed method significantly outperforms traditional baseline models across all indicators; specifically, the prediction error (RMSE = 10.9764, MAPE = 3.7866%) is substantially minimized, and the coefficient of determination ($R^2$ = 0.9741) is highly optimal. This adaptable framework effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology. Full article

This paper proposes a Multi-Strategy Improved Connected Banking System Optimizer, named MICBSO, for numerical optimization and three-dimensional UAV path planning. MICBSO enhances the original CBSO through three coordinated strategies. First, a chaos–opposition learning initialization strategy is introduced to improve initial population quality and search coverage. Second, a Gaussian perturbation-based multi-elite guidance mechanism is designed to reduce dependence on a single best solution and strengthen the balance between exploration and exploitation. Third, a hybrid boundary control strategy combining reflective correction and random reinitialization is developed to improve solution feasibility and maintain population diversity. The proposed algorithm is evaluated on the CEC2017 benchmark suite and compared with 11 representative algorithms. Experimental results show that MICBSO achieves competitive convergence accuracy, stability, and robustness across different dimensional settings. In addition, MICBSO is applied to three-dimensional UAV path planning in four complex terrain scenarios. The results demonstrate that MICBSO can generate feasible and safe flight paths with lower comprehensive cost. Overall, the proposed method provides an effective optimization framework for both benchmark optimization and constrained UAV path planning tasks. Full article

Accurate prediction of battery thermal runaway (TR) is a critical requirement for electric vehicle (EV) battery management systems (BMSs), as TR remains one of the most severe failure modes in lithium-ion batteries. Conventional neural network training methods may suffer from local optimum entrapment, slow convergence, and unstable performance when applied to nonlinear battery safety data. To address these limitations, this paper proposes a Hybrid Particle Whale Optimization Algorithm-optimized feedforward neural network (HPWOA-FNN) for continuous TR probability prediction and binary high-risk event classification using multivariate EV charging sensor data. The proposed HPWOA combines the rapid convergence capability of Particle Swarm Optimization (PSO) during the initial exploration phase with the exploitation and refinement capability of the Whale Optimization Algorithm (WOA) during the second phase. A global-best transfer mechanism is introduced at the PSO-WOA phase boundary to preserve the best solution identified during exploration and initialize the WOA leader, thereby improving convergence continuity and reducing premature stagnation. The model is evaluated using a 500-sample EV battery-charging dataset containing 12 electrothermal, electrical, mechanical, and environmental features. The proposed HPWOA-FNN outperforms standalone PSO-, WOA-, and Stochastic Fractal Search Algorithm (SFSA)-optimized FNN models across all regression metrics, achieving MSE = 0.000989, RMSE = 0.031442, MAE = 0.027250, R² = 0.9702, and MAPE = 3.8075%. For binary high-risk event detection, HPWOA-FNN achieves the highest AUC of 0.9817 and the lowest false-negative count, reducing missed high-risk events to 7 compared with 9 for PSO, 12 for WOA, and 17 for SFSA. Feature-importance analysis identifies maximum temperature and internal resistance as the dominant predictors, consistent with established thermal runaway mechanisms. The results demonstrate that HPWOA-FNN provides an accurate, interpretable, and computationally practical framework for EV battery thermal runaway prediction and BMS decision support. Full article

Water scheduling is a complex optimization problem that requires efficient and adaptive solution approaches. Metaheuristic techniques, particularly swarm intelligence models, have increasingly been applied to address these challenges. This study presents a scoping review that maps and synthesizes the existing literature on the application of swarm intelligence in water scheduling. Guided by the PRISMA-ScR framework and the JBI Population–Concept–Context (PCC) model, relevant studies published between 2015 and 2025 were identified across multiple databases. From an initial pool of 1357 studies, only 23 met the inclusion criteria and were subjected to detailed analysis. The findings reveal a strong concentration of research on water distribution networks, coupled with limited methodological diversity across the reviewed studies. There is an absence of explicit focus on resource-constrained or arid environments contexts where water-scheduling challenges are often most acute. Geographically, the literature is heavily skewed toward Asia, with the majority of studies conducted in China (n = 7) and Iran (n = 6). In contrast, only one study originated from Africa and one from Australia despite the disproportionate severity of water scarcity challenges across the African continent. The review exposes a critical gap in the literature and underscores the need for more context-aware, hybrid swarm intelligence models that explicitly account for the socio-economic and environmental constraints of water-stressed regions. Full article

This study introduces the ’Black-Backed Jackal Optimization’ (BBJO), a nature-inspired meta-heuristic algorithm designed for complex, non-linear, and high-dimensional search spaces. The fundamental mathematical model of BBJO relies on the opportunistic hunting behavior and survivability strategies of the black-backed jackal (Lupulella mesomelas). We use non-linear energy decrease and adaptive Lévy flight to maintain the equilibrium of the search. This allows the algorithm to scan large areas first, then zoom in with a high degree of precision once it has identified a suitable location. This configuration prevents the algorithm from getting stuck on a suboptimal local solution, which is a frequent danger during searches in complex spaces. BBJO has been validated against 23 standard benchmark functions, demonstrating significantly greater accuracy than Particle Swarm Optimization (PSO) on complex and large-scale search spaces. On fixed-size domains ($F_{21}$–$F_{23}$), the BBJO algorithm achieved a 100% success rate with zero standard deviation, surpassing the Grey Wolf Optimizer (GWO) and Differential Evolution (DE), which frequently suffered from structural stagnation. Visual convergence study shows that BBJO efficiently identifies optimal search regions early in the iteration budget, saving time compared to traditional linear decay models. BBJO optimizes fuzzy inference systems (FISs) for two practical applications: autonomous car speed control and industrial furnace regulation. Experimental results indicate that BBJO significantly decreased cumulative penalties and improved steady-state error reduction compared to baseline configurations and established meta-heuristic methods. The results show that BBJO is a reliable and useful technique for engineering optimization. Full article

Metaheuristic algorithms remain a widely used class of solvers for solving complex, non-convex optimization problems where gradient information is unavailable, yet two failure modes continue to limit their practical reach: premature convergence caused by inadequate exploration diversity in late iterations and population stagnation that persists even when individual agents are nominally assigned to the exploration phase. This paper proposes the Stagnation-Aware Aquila Optimizer (SAAO), a hybrid algorithm that addresses both failure modes by embedding three targeted mechanisms into the Aquila Optimizer (AO) framework: (i) an adaptive exploration probability that responds to global fitness-improvement history; (ii) individual-level stagnation counters that force exploration re-entry for any agent that fails to improve for more than 30 consecutive iterations, regardless of the global phase schedule; and (iii) a diversity-maintenance module that reinitializes completely stagnant agents via random sampling or opposition-based learning. The biological repertoire of search operators is simultaneously enriched by incorporating four physics-grounded operators from the Animated Oat Optimization (AOO) algorithm centroid-guided dispersal, elite-guided dispersal, hygroscopic rolling, and spring ejection, alongside the original AO operators, yielding six complementary update rules partitioned equally between exploration and exploitation. The SAAO was evaluated against nine state-of-the-art algorithms on the CEC2015 benchmark and CEC2022 under identical experimental settings. The SAAO achieved the best Friedman mean rank on both suites and delivered competitive or superior performance against the nine baselines, with Wilcoxon rank-sum tests confirming statistically significant advantages over most competitors. On three classical engineering design problems, the SAAO achieved competitive outcomes. In a real-world equipment anomaly prediction task, an SAAO-optimized ensemble classifier attained 98.23% accuracy, surpassing the compared baseline models. These results establish SAAO as a robust and computationally tractable optimizer for both benchmark and applied settings. Full article

This paper presents a unified comparative study of various controllers, including proportional–integral–derivative (PID), Fractional-Order PID (FOPID), Internal Model Control (IMC) controllers, and Tilt–Integral–Derivative (TID) controllers, for the regulation of mean arterial blood pressure (MABP). The controllers are optimally tuned by using a single metaheuristic approach, namely the Pelican Optimization Algorithm (POA), ensuring a fair and consistent comparison. The POA optimizes the objective function using standard error indices (ITAE, IAE, and ISE) along with transient characteristics. The aforementioned controllers are then evaluated under varying patient conditions for different patient categories, including sensitive, nominal, and insensitive, and their performance is systematically compared with one another and with the reported methods from the existing literature. The simulation results demonstrate that IMC offers fast settling with minimal overshoot, FOPID improves robustness through fractional dynamics, and the TID controller provides the smoothest transient response and disturbance rejection across all patient categories. The results confirm the effectiveness of advanced control strategies over conventional PID and highlight the potential of POA-tuned TID control for reliable and patient-specific MABP regulation in critical care applications. Full article

Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evolution approach, reffered to as MuCDEA, to improve the adaptability and robustness of conventional Differential Evolution (DE) for UAVPP. MuCDEA integrates complementary mechanisms from JADE, CoDE, EPSDE, SaDE, MIDE, and SHADE through adaptive strategy selection and cooperative evolution. The optimization model combines path-length (fuel) cost and threat exposure with explicit pitch and yaw constraints that enforce actuator-feasible maneuvering bounds. The proposed framework is evaluated on 20 benchmark UAVPP cases covering 2D and 3D scenarios with varying obstacle distributions and pathh discretization levels, and it is compared against 11 state-of-the-art DE variants and several widely used optimization methods using the CEC-2022 ranking methodology. Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP. Full article

Maximizing the dominant Lyapunov exponent ${\lambda }_1$ of an incommensurate fractional-order chaotic system, while respecting the dynamical conditions for a strange attractor, is a non-convex, gradient-free problem on a history-dependent landscape. Existing metaheuristic studies typically use hard-cutoff penalties that distort the fitness landscape and integer-order Lyapunov estimators that can be biased for strongly fractional regimes. This paper presents a constraint-faithful optimization framework combining (i) subtractive-hinge penalties that vanish on the feasible set, (ii) a memory-consistent Grünwald–Letnikov variational Lyapunov estimator with adaptive tail-sum truncation, (iii) joint search over parameters and incommensurate orders by the Marine Predators Algorithm, and (iv) a fractional conditional Lyapunov exponent (FCLE) that recovers the integer-order limit. Applied with a fixed configuration to the fractional-order Lorenz, Ma–Chen financial, Iqbal–Wang, and Hyper–Chen systems, the framework converges to feasible attractors with enlarged Lyapunov spectra. Dissipativity is rigorously verified; all selected optima have strictly negative Lyapunov trace at the reported precision. FCLE analysis on the optimized Lorenz attractor recovers the integer-order identity $c_{min}={\lambda }_1$ under full-state coupling, and shows that single-state x-coupling raises the threshold to ≈9${\lambda }_1^{*}$. The optimized fractional-order Lorenz attractor is employed as the random-number generator of a recent chaos-based image-encryption scheme, where it yields strong statistical results across standard benchmarks. Full article

Urban flooding poses increasing risks globally due to climate change and urbanization, yet physics-based hydraulic models suffer from high computational costs that limit their application to flood analysis under various rainfall conditions. This study proposes an optimizer-improved tile-based convolutional neural network framework for efficient prediction of two-dimensional peak flooding maps under various rainfall scenarios. The framework integrates tile-based spatial learning for efficient localized flood-response learning and a combined Adam-VCA optimizer to mitigate local optimum convergence. The framework was applied to the Dorim basin in Seoul, South Korea, using 22 rainfall scenarios ranging from 10 to 800 mm, of which 20 scenarios were used for model training and 2 scenarios (500 mm and 700 mm) were reserved as independent test scenarios for performance evaluation. Prediction accuracy was evaluated using F1-score and critical success index (CSI), and Verification period based accuracy (VAC) and Peak flooding based accuracy (PAC) as study-specific depth-incorporated metrics. The proposed optimizer-improved CNN substantially outperformed the conventional CNN, achieving F1-score of 83.84%, CSI of 72.24%, VAC of 90.39%, and PAC of 70.93%, compared to 51.03%, 34.25%, 63.39%, and 47.29%, respectively. The results confirm the framework’s potential for efficient flooding assessment and urban flood risk management under diverse rainfall conditions. Full article

Cloud task scheduling is a critical optimization problem in cloud computing environments, aiming to allocate computational tasks to appropriate virtual machines while reducing execution time, balancing resource load, and minimizing scheduling cost. However, due to the high dimensionality, nonlinear characteristics, and complex constraints of cloud scheduling scenarios, traditional optimization methods often struggle to obtain high-quality solutions efficiently. To address these challenges, this paper proposes a Multi-strategy Improved Golden Sine Optimization Algorithm (MIGoldSA) for global optimization and cloud task scheduling problems. First, an adaptive chaotic opposition initialization strategy is incorporated to improve the distribution quality and diversity of the initial population. Second, a dynamic elite-guided sine evolution strategy is designed to reduce the dependence on a single best individual and improve the coordination between global exploration and local exploitation. Third, an Economic Investment Strategy is introduced to adaptively allocate search efforts according to the optimization potential of individuals. To verify the effectiveness of MIGoldSA, extensive experiments are conducted on the IEEE CEC2017 and CEC2022 benchmark suites and compared with nine advanced optimization algorithms. The results show that MIGoldSA obtains the best or tied-best mean fitness values on 60 out of 84 benchmark cases, accounting for 71.43% of all test cases. In the Wilcoxon signed-rank test, MIGoldSA achieves 662 wins, 57 ties, and 37 losses among 756 pairwise comparisons, corresponding to an overall win rate of 87.57% and a non-inferiority rate of 95.11%. In addition, the Friedman mean ranks of MIGoldSA are 1.47, 2.00, 3.98, and 4.17 under the four benchmark settings, which are reduced by 85.26%, 79.94%, 45.25%, and 42.32%, respectively, compared with the original GoldSA. Furthermore, the proposed algorithm is applied to cloud task scheduling problems under different task scales. The experimental results show that MIGoldSA maintains competitive time-cost performance and achieves clear reductions in load cost, price cost, and comprehensive scheduling cost. Compared with the original GoldSA, the normalized comprehensive scheduling cost is reduced by approximately 9–14% in small-scale scenarios and approximately 18–21% in large-scale scenarios. Meanwhile, the normalized load cost and price cost are reduced by about 18–25% and 10–18%, respectively, and the time cost shows an approximately 8–12% reduction in large-scale scheduling scenarios. These quantitative results demonstrate that MIGoldSA can improve the optimization accuracy, statistical robustness, and overall scheduling cost efficiency of the original GoldSA on most tested problems. Full article