Search Results (591)

The rapid development and expansion of the Internet of Things (IoT) ecosystem require managing increasing computational demands with the aid of advanced hierarchical architectures. The integration of a complete four-tier hierarchy—including Mobile, Edge, Fog, and Cloud layers—and the priority requirements are being overlooked in the literature despite the effort of existing studies. The goal of this study is to fill these gaps by proposing a novel, context-aware task-offloading framework designed for multi-dimensional ecosystems involving multi-server and multi-application environments. A targeted biomimetic approach is utilized at the core of this research. The decentralized foraging behavior of biological swarms is translated into a concrete engineering solution. This solution is designed specifically for computational offloading and resource management. To achieve this, a “Learning-guided Meta-heuristic” hybrid model is developed. Within this framework, bio-inspired Ant Colony Optimization (ACO) is directly integrated with an Upper Confidence Bound (UCB)-inspired exploration mechanism. Natural, pheromone-based imitation is solely relied upon by traditional biomimetic algorithms. In contrast, higher-order cognitive learning is fully incorporated by this hybrid synergy. Consequently, underlying system dynamics are adaptively learned. Local minima traps are also successfully avoided. This avoidance is achieved by dynamically selecting the optimal layer for each individual task. Both energy consumption and latency are optimized simultaneously. Meanwhile, strict operational feasibility is ensured through a dynamic penalty-based mechanism. Battery and deadline constraints are explicitly handled by this mechanism. Extensive simulations demonstrate the superiority of the proposed UCB-ACO model over state-of-the-art meta-heuristics, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), ACO, Artificial Bee Colony Optimization (ABO), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the proposed framework outperforms the methods compared by achieving 22.5% lower latency and 23% lower energy consumption. This study effectively maps the current literature and then introduces a pioneering solution for next-generation resource management in distributed computing. Full article

With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case. Full article

The optimal expansion of unbalanced three-phase distribution networks in non-interconnected zones requires the simultaneous resolution of two highly complex planning decisions: the selection of feeder routes and the sizing of conductors. This problem, formulated as a non-convex mixed-integer nonlinear program (MINLP), poses significant computational challenges due to the combinatorial explosion of radial topologies, discrete conductor choices, and the nonlinearity of three-phase power-flow equations. While metaheuristics offer flexible exploration, they lack optimality guarantees and repeatability, whereas exact MINLP solvers provide rigorous solutions but become computationally intractable for systems of realistic size. To overcome these limitations, this paper introduces a novel hybrid exact–metaheuristic framework that synergistically combines the global exploration capabilities of the Equilibrium Optimizer (EO) with the rigorous evaluation power of an exact MINLP model. In this cascade architecture, EO efficiently navigates the discrete space of radial topologies, while the exact MINLP stage, solved using BONMIN with an interior-point branch-and-bound scheme, optimizes conductor selection and evaluates the full annualized cost, rigorously enforcing voltage, ampacity, and physical constraints. The proposed methodology was validated on 10-, 30-, 50-, and 110-node test systems derived from real Colombian non-interconnected zones (Nuquí, Leticia, San Andrés, and a large-scale urban case). Comparative analysis against pure metaheuristics (SSA, GWO, VSA) and standalone MINLP demonstrates that EO-MINLP consistently yields the lowest total annualized costs, achieving savings of up to 0.42%, 0.71%, and 1.36% over the best pure metaheuristic for the 10-, 30-, and 50-node systems, respectively. Crucially, the hybrid strategy dramatically enhances scalability, reducing the standalone MINLP computational time by 15.79%, 78.68%, and 88.95% for these cases, while preserving solution quality and improving repeatability (standard deviation reduced from over 1.2% to as low as 0.11%). For the challenging 110-node system, where the standalone MINLP proved computationally infeasible, the proposed method successfully delivered a feasible, high-quality solution with a standard deviation of just 0.43%, confirming its practical applicability to large-scale planning. These results demonstrate that the EO-MINLP framework provides a robust, scalable, and economically superior tool for the cost-effective design of unbalanced distribution networks, effectively bridging the gap between the flexibility of stochastic search and the rigor of mathematical programming. Full article

Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency. 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

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields. Full article

The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as premature convergence, inadequate local refinement, and diminished performance in high-dimensional multimodal environments. To overcome these issues, this study presents HALA, a new hybrid dual-subpopulation optimizer that effectively integrates an enhanced ALA with the SHADE algorithm. HALA employs two interacting subpopulations: one leverages an improved ALA with hybrid t-distribution and Levy flight perturbations to promote persistent long-range exploration and diversity preservation; the other applies SHADE’s success-history adaptation and external archive for accurate local exploitation. Periodic bidirectional elite migration facilitates knowledge transfer between the subpopulations, reducing early stagnation in the enhanced ALA and strengthening SHADE’s global search capability. HALA is thoroughly benchmarked against 17 advanced metaheuristics, including ALA, LSHADE, LSHADE-SPACMA, AOOA, BAEO, BPBO, CCO, CEO, CQALA, DFL, DMOA, DHOA, FGO, KLA, PGA, SO, and SOO, using the IEEE CEC2017 suite in 10, 30, 50, and 100 dimensions and the IEEE CEC2022 suite in 10 dimensions. Comprehensive analyses involving qualitative visualization, convergence curves, boxplots, and statistical tests indicate that HALA achieves competitive or superior solution quality, comparable or faster convergence, and robust stability on a substantial proportion of the test instances. In particular, HALA obtains the most favorable Friedman average ranking values among the compared algorithms, which are 2.55, 2.38, 2.34, and 2.55 for the 10-, 30-, 50-, and 100-dimensional CEC2017 functions, respectively, and 2.58 for the 12 10-dimensional CEC2022 functions. Moreover, HALA is successfully applied to five well-known constrained engineering design problems—pressure vessel, rolling element bearing, tension/compression spring, cantilever beam, and gear train—where it reliably achieves optimal or near-optimal results that match or surpass the compared methods. These findings underscore HALA’s competitive strength and broad potential for practical engineering optimization. Full article

This paper proposes an optimal reconfiguration methodology for electrical distribution systems aimed at improving operational efficiency and service quality. Traditionally, distribution network reconfiguration has focused on minimizing technical losses and improving the voltage profile; however, these approaches do not explicitly incorporate reliability criteria or geospatial aspects associated with the actual operation of distribution networks. The proposed methodology minimizes active power losses while incorporating reliability constraints through the calculation of Energy Not Supplied and a relative georeferenced spatial-operational indicator for the selected switching devices. The approach is based on a topological analysis combined with the Manta Ray Foraging Optimization metaheuristic algorithm, while the electrical evaluation is performed using the fast-decoupled power flow method with the FDXB formulation. The weighted scalar objective function considers active power losses, Energy Not Supplied associated with N–1 contingencies, and the relative georeferenced spatial-operational indicator associated with the selected switching devices. The voltage profile is subsequently evaluated as a technical performance indicator to verify the operational quality of the obtained configurations. The methodology is validated using test systems, achieving loss reductions, improvements in the voltage profile, and a decrease in Energy Not Supplied, thereby demonstrating configurations with improved electrical and reliability performance that are more representative of practical distribution network operation. Full article

The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics. Full article

Hot Rolling Production Scheduling (HRPS) is a crucial combinatorial optimization problem characterized by severe conflicts between rigid physical rolling rules and strict order due dates. While real-time scheduling is essential for dynamic manufacturing, traditional meta-heuristics suffer from severe computational time bottlenecks. Conversely, standard end-to-end Deep Reinforcement Learning (DRL) models offer rapid inference but typically struggle with spatio-temporal feature entanglement, training instability under extreme penalty landscapes, and poor zero-shot scale generalization. To bridge these gaps, this paper proposes a novel framework named Dual-Stream Group-Optimize Policy Optimization with Multiple Optima (DSGO-POMO). The framework introduces three core innovations: (1) a Dual-Stream intervention network that explicitly decouples and synergistically fuses physical attributes with temporal slacks; (2) a Group Relative Policy Optimization (GRPO) training mechanism to stabilize policy updates; and (3) an Entropy-Aware and Dual-Annealed Differential Active Search (EA-DAS) strategy to seamlessly adapt pre-trained weights to out-of-distribution scales. Extensive computational experiments validate the superiority of the proposed framework. On medium-scale instances ($n=50$), the basic DSGO-POMO slashes inference time to merely 0.09 min compared to the approximately 46 min required by classical heuristics while simultaneously achieving lower total costs. Furthermore, during zero-shot extrapolation on ultra-large instances ($n=200$), the framework coupled with EA-DAS outperforms the best heuristic baseline by 14.7% in solution quality and completes the search in under 2 min. These numerical breakthroughs confirm that DSGO-POMO provides a blazing-fast, highly robust, and industrially viable rescheduling paradigm. Full article

The Traveling Salesman Problem (TSP) is a fundamental NP-hard combinatorial optimization problem with broad applications in logistics, scheduling, and satellite mission planning. While Ant Colony Optimization (ACO) offers distributed search and positive feedback, conventional variants suffer from premature convergence and quadratic construction costs that limit scalability. We propose the Globally Adaptive Ant Colony System (GACS), which integrates three synergistic mechanisms: (1) K-nearest neighbor candidate-list pruning that reduces per-step construction complexity from $O\left(n\right)$ to $O\left(K\right)$; (2) a globally adaptive pheromone weighting scheme that dynamically calibrates reinforcement intensity as the search matures; and (3) an adaptive stagnation recovery mechanism that applies pheromone smoothing to escape local optima. Numerical experiments demonstrate that GACS consistently outperforms four traditional ACO baselines under an equivalent time budget. On a large benchmark set from TSPLIB, GACS achieves highly competitive results against various state-of-the-art metaheuristics, with non-parametric statistical tests confirming its significant superiority in both solution quality and convergence rank. Ablation and sensitivity analyses verify that all three mechanisms are individually indispensable and that the framework is robust to parameter perturbation. Specifically, the evaporation rate and stagnation threshold are identified as the most critical parameters affecting performance, while the smoothing and adaptive range parameters exhibit low sensitivity. These results establish GACS as a lightweight, scalable, and adaptable framework for the TSP. Full article

This study investigates the optimal placement of a surge tank to mitigate pressure fluctuations induced by water hammer in a complex, real-world water distribution network (WDN). A transient-flow numerical model was developed using the Method of Characteristics (MOC) integrated with surge tank theory, applied to a simplified and skeletonized representation of the target network. To determine the most effective installation site, Particle Swarm Optimization (PSO) was employed across 32 candidate nodes. Transient events were simulated through valve closure and reopening operations at the terminal nodes of the network. The results indicate that Node 41 is the optimal location for minimizing head fluctuations. Specifically, the maximum head fluctuation was reduced from 64.32 m in the unprotected system to 51.79 m with the surge tank at Node 41, representing a 19.48% improvement in hydraulic stability. These findings emphasize the critical role of strategic surge tank positioning and provide a robust technical framework for the design and operation of surge protection systems in complex WDNs. Full article

The rapid growth of populations and industrial activities has intensified the need to optimize resource management and reduce environmental impacts. A promising pathway toward sustainable development is the gradual replacement of fossil fuel vehicles with electric vehicles (EVs). However, managing EV operations, particularly regarding depot siting and vehicle routing, is a complex challenge that requires balancing economic, environmental, and social objectives. This research proposes a model for designing an intelligent and sustainable transportation system for waste collection using EV fleets. The model simultaneously determines optimal depot locations from a set of candidates and identifies efficient vehicle routes. Its dual objectives are to minimize total costs, including depot set-up, operation, and travel costs, and to minimize maximum travel time, ensuring equitable workload distribution among drivers. Beyond reducing costs and emissions, the model incorporates social equity considerations in balancing driver travel times. EV limitations, such as restricted range, are explicitly addressed. To solve small-scale instances, the $ϵ$-constraint method was applied, while medium- and large-scale instances were tackled with two multi-objective metaheuristics: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The results demonstrate the model’s sensitivity to system parameters such as vehicle capacity and demand rates. Statistical comparative analysis revealed that both algorithms successfully optimized the primary objective functions without significant differences. However, they exhibited distinct performance metric strengths; NSGA-II demonstrated statistically significant advantages in computational efficiency, solution quantity, and uniform distribution, while MOPSO excelled in convergence quality and closeness to the true Pareto front. Furthermore, the practical applicability of the proposed model is validated through a real-world case study of a municipal solid waste management network in Southern Tehran. This research contributes a comprehensive framework for optimizing EV-based waste collection systems, offering a meaningful step toward sustainable and intelligent urban transportation. The findings provide a theoretical framework and strategic insights for transportation managers and policymakers seeking effective strategies for environmentally responsible and socially equitable waste collection. Full article

Due to the rising share of renewable energy sources within distribution systems, there is a need for planning methods that can accommodate wind uncertainty. This paper introduces a holistic bi-level optimization approach for the optimal planning of wind-based distributed generation (WBDG) and battery energy storage system (BESS). At the higher level, the optimal location and size of WBDG and BESS are selected based on minimizing power loss, improving voltage stability, and minimizing the cost of electricity production. Meanwhile, at the lower level, the BESS charging–discharging operations and power transmission between different wind situations are scheduled. Wind uncertainty is considered in the model by applying the Weibull probability density function in combination with the Two-Point Estimation Method (2m-PEM). The resulting complicated bi-level optimization issue is addressed by creating a new Improved Cheetah Optimizer (ICO) that incorporates four enhancements to the Cheetah Optimizer (CO) to improve its explorative and exploitative capabilities. Simulations conducted on the IEEE 33-bus system show the superiority of the proposed method compared to other methods. The ICO outperforms particle swarm optimization (PSO), genetic algorithm (GA), and the basic CO by achieving up to 69.07% daily energy savings, raising the lowest bus voltage from 0.9440 per unit to 0.9610 per unit, and providing an expected operating cost of $5449.92. Full article

This paper studies large-scale last-mile delivery using a heterogeneous fleet of trucks, onboard drones in a hybrid truck–drone mode, and independent drones. Orders are first screened by a feasibility check; feasible orders are then assigned to one of the three modes by a delivery mode selection policy and routed using mode-specific planning algorithms. The delivery mode selection policy is trained with Proximal Policy Optimization (PPO), warm-started by behaviour cloning from heuristic decisions. For route planning, we use a five-step procedure for the hybrid mode and simple depot round trips for independent drones. Experiments on Solomon VRPTW benchmarks and extended instances (100/200/400 customers; R/C/RC distributions) show lower total cost than representative heuristic baselines and metaheuristics, with practical runtime. Sensitivity analysis over fleet sizes further indicates competitive performance across a range of truck and drone configurations, especially for medium and large fleets. Full article