Search Results (231)

Numerous engineering problems can be represented as minimax optimization problems, including machine learning, classification, robust optimal control, signal processing, game theory, and more. Typically, minimax problems are considered challenging, especially constrained ones. The recently introduced artificial rabbits optimization (ARO) is inspired by the natural behaviour of rabbits. ARO exhibits robust effectiveness in tackling optimization challenges. Despite its advantages, ARO converges early to local optima, especially in complex or multi-modal optimization problems, and it struggles to balance exploration and exploitation, often leading to premature convergence and reduced accuracy. In this paper, we present a chaotic ARO that employs five maps exhibiting randomization behaviour to refresh candidate solutions. We assess the performance of the suggested CARO by applying it to 46 benchmark functions (25 unconstrained and 21 non-smooth minimax) and 15 constrained test functions with diverse characteristics. We evaluate its performance against six swarm intelligence algorithms. Also, we employ the chaotic maps to ARO and the six compared algorithms, and we perform a non-parametric statistical test, the Friedman test, on all outcomes. The findings show that the proposed algorithm can solve both unconstrained and constrained minimax problems more effectively and efficiently than other swarm intelligence methods. Full article

The increasing penetration of converter-interfaced renewable energy sources has led to reduced system inertia and increased frequency stability challenges in modern power systems. Battery energy storage systems (BESSs) provide fast active power support. However, their effectiveness depends on the installation location, power rating, and network characteristics. This paper proposes a power-flow-informed, sensitivity-based method for the optimal placement and sizing of distributed BESSs to improve the frequency nadir and rate of change of frequency (RoCoF). The method integrates marginal frequency sensitivity obtained from time-domain simulations with network coupling information derived from power-flow analysis within a constrained optimization framework solved using particle swarm optimization. The network coupling weight, derived from voltage sensitivity, represents the steady-state electrical connectivity and active power redistribution capability, rather than transient frequency dynamics. It is used in combination with frequency sensitivity to improve the discrimination of candidate buses. The method is evaluated on the IEEE 39-bus system under multiple generator outage contingencies. For the most severe contingency (G01), the baseline system exhibits a frequency nadir of 55.9230 Hz and an RoCoF of 0.2404 Hz/s. With the proposed method, the frequency nadir improves to 58.6561 Hz, corresponding to an increase of 2.7330 Hz (4.88%), while the RoCoF is reduced to 0.1224 Hz/s (49.17% reduction). The optimal solution distributes a total BESS capacity of 298 MW across multiple buses, with the largest allocation of 46 MW at Bus 36. Across additional contingencies, the proposed method consistently achieves higher frequency nadirs and lower RoCoFs compared with both the baseline system and benchmark placement methods. The results demonstrate that combining dynamic frequency sensitivity with power-flow-based network coupling provides a physically consistent and computationally efficient strategy for distributed BESS allocation in low-inertia power systems. Full article

Bond ETF rebalancing is difficult to describe with return and risk objectives alone, because a portfolio that looks attractive on paper may still be impractical if it requires large and unstable trades. This paper proposes a topology-aware multi-objective particle swarm optimization framework for bond ETF allocation under credit-risk-related constraints. The method jointly considers annualized return, CVaR, and diversification, while enforcing long-only, exposure, and hard maximum-step turnover constraints. The central idea is to treat the swarm as a communication graph: particles exchange information through an explicit topology, and this topology affects how feasible regions are explored and how leaders are selected. When a candidate portfolio update violates the turnover budget, it is repaired toward the feasible set before evaluation, so that the search remains tied to tradable rebalancing decisions. We test the framework in a walk-forward out-of-sample backtest on U.S. bond ETFs from 2008 to 2024. The empirical analysis compares stronger classical and evolutionary baselines, four communication topologies, hard-versus-soft turnover control, stress-period behavior, and a synthetic scalability proxy. The results suggest that hard turnover repair is effective in truncating extreme rebalancing events, while communication topology changes the return–risk–turnover profile. In our experiments, the ring topology gives the most stable default behavior. Overall, the evidence suggests that topology is not just an implementation detail in swarm-based portfolio search, but a design choice that affects constrained multi-objective allocation. Full article

This paper presents the Spike Processing Unit (SPU), a digital spiking neuron model based on a discrete-time second-order Infinite Impulse Response (IIR) filter. By constraining filter coefficients to powers of two, the SPU implements all internal operations via shift-and-add arithmetic on 6-bit signed integers, eliminating general-purpose multipliers. Unlike traditional models, computation in the SPU is fundamentally temporal; spike timing emerges from the interaction between input events and internal IIR dynamics rather than signal intensity accumulation. The model’s efficacy is evaluated through a temporal pattern discrimination task. Using Particle Swarm Optimization (PSO) within a hardware-constrained parameter space, a single SPU is optimized to emit pattern-specific spikes while remaining silent under stochastic noise. Results from cycle-accurate Python simulations and synthesizable VHDL implementations indicate that the learned temporal dynamics are preserved under hardware-constrained digital execution, supporting the feasibility of the proposed approach. This work demonstrates that discrete-time IIR-based neurons enable reliable temporal spike processing under strict quantization and arithmetic constraints. Full article

Surrogate-assisted evolutionary algorithms have become the mainstream approach for solving expensive constrained multi-objective optimization problems (ECMOPs). However, existing methods suffer from blind search issues, and their selection strategies fail to adapt to changes in evolutionary stages. To overcome these limitations, this paper proposes a Multi-directional Guided Dual-mode Kriging-assisted Competitive Particle Swarm Optimization (MGD-KCSO) algorithm. MGD-KCSO integrates three synergistic strategies: a multi-directional guided solution strategy that constructs four complementary search paths based on non-dominated solutions to effectively enhance convergence and diversity; a dual-population data selection strategy that separates unconstrained and constrained populations to perform objective-oriented and constraint-oriented optimization, respectively; and an adaptive infill sampling strategy that dynamically switches sampling modes by monitoring the change rate of the objective function of the ideal point. If this rate exceeds a predefined threshold, the algorithm executes unconstrained sampling to accelerate convergence; otherwise, it switches to constrained sampling to prioritize the exploration of feasible boundaries. To verify the effectiveness of MGD-KCSO, comprehensive experiments were conducted on 33 benchmark problems and two real-world engineering design problems (pressure vessel and disc brake design). MGD-KCSO was compared against eight classic algorithms and three state-of-the-art methods published in the past two years. Experimental results evaluated by inverted generational distance (IGD) and hypervolume (HV) metrics demonstrate that MGD-KCSO outperforms the comparative algorithms on most test instances, achieving superior performance in terms of convergence, diversity, and practical applicability. Full article

Coal preparation plants pursue maximum economic benefit, yet product structure optimization under fluctuating coal quality and changing market demand is a coupled decision-making problem involving the organization of primary products such as lump clean coal, clean coal, raw fine coal, coal slime, and gangue, together with commercial coal blending and process-scheme selection. Conventional optimization methods that focus on a single stage are often insufficient to address such complex coordinated decisions. To this end, a GPSOM–WOA nested optimization model was developed to achieve the coordinated optimization of primary product separation, commercial coal blending, and process-scheme selection under the objective of economic benefit maximization. In the outer layer, where process-scheme selection and primary product structure adjustment involve both discrete decisions and continuous variables, a simplified Group-based Particle Swarm Optimization with Multiple Strategies (GPSOM) was employed to search the primary product structure parameters and generate engineering-feasible primary product balance tables. In the inner layer, where the commercial coal blending problem is subject to multiple constraints, including ash content, moisture, calorific value, and supply demand, the Whale Optimization Algorithm (WOA) was adopted to optimize blending ratios within a restricted feasible region. A piecewise penalty function was introduced for quality-limit violations to support profit-oriented constrained optimization. Subject to commercial coal quality constraints on ash content, moisture, and calorific value, a case study of a coal preparation plant in Inner Mongolia was conducted to compare product structures and economic benefits under different process conditions. The results show that the proposed model can realize the joint optimization of primary product structure and commercial coal blending, and can provide a quantitative basis for product structure optimization and process selection in coal preparation plants. Full article

The increasing penetration of photovoltaic (PV) systems in distribution networks has introduced new challenges in voltage regulation and energy loss mitigation, particularly under time-varying loading conditions. This paper presents a constrained multi-objective mathematical optimization framework for the optimal allocation and sizing of PV-STATCOM devices in radial distribution systems. The problem is formulated as a nonlinear optimization model that minimizes the daily energy losses over a 24 h operating horizon while satisfying network operational constraints, inverter capacity limits, and renewable penetration restrictions. To efficiently solve the resulting non-convex optimization problem, a metaheuristic algorithm based on the weighted mean of vectors (WMV) is employed. The WMV method integrates wavelet-based weighting mechanisms, mean-driven update rules, vector combination strategies, and a local refinement operator to balance global exploration and local exploitation within the feasible search domain. Constraint violations are handled through a penalty-based mathematical transformation of the objective function. The proposed framework is validated on the IEEE 33-bus and IEEE 69-bus distribution systems under realistic daily load variations. The numerical results demonstrate significant reductions in daily energy losses compared to differential evolution, particle swarm optimization, artificial rabbits optimization, and golden search optimization algorithms. Furthermore, convergence analysis confirms the robustness and computational efficiency of the WMV approach in solving large-scale constrained power system optimization problems. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods. Full article

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment. Full article

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization is computationally efficient, it often experiences premature convergence in complex search spaces. This paper proposes a novel hybrid algorithm, Mushroom–Weed Hybrid Reproduction Optimization (MWHRO), which integrates the colony-based local search of the Mushroom Reproduction algorithm with the fitness-proportional reproduction and competitive elimination mechanisms of Invasive Weed Optimization. Hybridization enhances population diversity and global exploration while preserving fast convergence. The proposed algorithm is evaluated based on a realistic tourist route optimization problem using real-world data from Zagreb, Croatia, across multiple transportation modes and objective-weight scenarios. Performance is compared against Ant Colony Optimization, Invasive Weed Optimization, Particle Swarm Optimization, and standard Mushroom Reproduction Optimization under equal evaluation budgets. Experimental results demonstrate that the proposed MWHRO algorithm consistently achieves high-quality solutions with significantly lower execution times, particularly in constrained and multimodal scenarios. Statistical analysis confirms the robustness and practical suitability of the proposed approach for real-world route optimization. Full article

Appointment-based lock scheduling can mitigate congestion at inland ship locks, but the inherent uncertainty in vessel arrivals frequently causes severe schedule degradation, disrupting the original lockage plans. To address this challenge, we develop an optimization framework that quantifies arrival-time uncertainty using a deep ensemble to generate generates reliable prediction intervals, and embeds a rescheduling mechanism for missed appointments within a multi-objective model. The model is solved with a hybrid heuristic that combines Differential Evolution, Variable Neighborhood Search, and Non-dominated Sorting Genetic Algorithm II (DE–VNS–NSGA-II). Compared to conventional evolutionary techniques, hybrid Particle Swarm Optimization (PSO) approaches, and recent advanced algorithms (GSAA-RL and ADEA-KC), the proposed algorithm effectively overcomes premature convergence in highly constrained discrete scheduling spaces by leveraging DE for robust global exploration and VNS for deep local refinement. In simulations with 143 vessels, the approach reduced average waiting time by 18.51% (28.63 h to 23.33 h), lowered the schedule adjustment rate by 9.02% (0.331 to 0.301), and decreased lock-utilization loss by 5.06% (0.413 to 0.392) relative to a standard baseline. The results demonstrate more stable schedules and more efficient use of lock capacity under uncertainty, providing a data-driven decision-support tool for lock operators to dynamically mitigate disruptions and reallocate passage quotas at inland navigation hubs. Full article

In this paper, we propose the Manifold Random Drift Particle Swarm Optimization (MRDPSO) algorithm for matrix optimization on smooth manifolds. Conventional swarm intelligence methods generally converge prematurely in constrained domains. To mitigate this issue, we introduce the swarm intelligence methods to the manifold and a Random Drift mechanism that regulates the search process. Using Riemannian geometry, our framework treats constrained problems as unconstrained ones on the manifold, which preserves the intrinsic geometric structure of the data. Particles are initialized on the manifold, while updates are performed in tangent spaces. Since geodesic calculations are computationally expensive, we use an inverse retraction as a faster alternative to standard logarithmic mapping. Numerical experiments on Grassmann, Stiefel, and Oblique manifolds show that MRDPSO achieves higher accuracy and superior convergence stability compared to recent state-of-the-art manifold-adapted heuristics, namely IISSO and MSSO. Full article