Search Results (1,012)

Algorithmic team formation is a well-studied problem in workforce analytics. Most prior research has focused on team-oriented outcomes, such as cost and performance, while individual-oriented outcomes have been limited to operational constraints. We formally define and solve a new variant, the growth-aware team formation problem (GATFP), which forms teams to maximize individual skill growth. Our formulation lets organizations define their own notion of growth and their own policy for allocating growth opportunities, and integrates standard team formation constraints such as covering a portfolio of projects, capping projects per individual, and meeting each project’s skill, level, and coverage requirements. We prove the problem NP-hard and develop a family of metaheuristics: randomized and greedy construction, a ruin-and-recreate large neighborhood search (RR), and a hybrid evolutionary algorithm (HE). Where the objective is linear, a mathematical program provides an exact baseline. We evaluate them under an equal runtime budget on a synthetic benchmark and a real-world-derived dataset. Both metaheuristics surpass the construction baselines and are closely matched, and which one leads depends on the growth model: RR is attractive for its solution quality and implementation simplicity, whereas HE is the stronger choice when an individual’s growth depends on their team’s composition. Full article

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10⁻⁴ Hz, and the lowest error indices (ISE = 3.073 × 10⁻⁶, ITSE = 0.697 × 10⁻⁶, and ITAE = 3.40 × 10⁻⁴). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs. Full article

Wireless Sensor Network (WSN) underpins numerous applications, including environmental surveillance, industrial control, and smart cities, where reliable connectivity is vital for continuous sensing and data transmission. On the other hand, a small number of failed or compromised Critical Nodes (CNs) can badly fragment the network topology, interfere with communication, and impair performance. Consequently, devising effective solutions to the CN Detection Problem (CNDP) while taking connectivity, energy, and reliability into account remains a serious research endeavor. To tackle this challenge, this work proposes a Genetic Algorithm-assisted Damped Yo-Yo Grey Wolf Optimization framework with a Revitalized Boltzmann Distribution connectivity model (GA-DY-RBD-GWO). The CNDP is formulated as a node-elimination optimization problem that identifies the top-(k) CNs, where each search agent represents a candidate subset of k nodes. To realistically characterize network connectivity, a Revitalized Boltzmann Distribution (RBD)-based pairwise connectivity model is developed by jointly considering hop distance, residual path energy, and distance-based link reliability. Based on the resulting connectivity matrix, Total Pairwise Connectivity (TPC) is computed, and node criticality is quantified by the reduction in TPC after removing a candidate node set. To effectively explore the combinatorial search space, the Grey Wolf Optimization is augmented with a damped Yo-Yo control mechanism that adaptively balances exploration and exploitation during the optimization process. Furthermore, Genetic Algorithm-inspired crossover and mutation operators improve population diversity and avoid premature convergence, while elitist retention keeps the best-so-far candidate solution. By integrating realistic RBD-based connectivity modeling with an adaptive hybrid metaheuristic, GA-DY-RBD-GWO accurately identifies CNs whose deletion induces maximal TPC degradation. Extensive experiments under diverse network topologies, deployment scenarios, and spatial distributions demonstrate that the GA-DY-RBD-GWO exhibits superior performance over representative baselines, revealing that it is an efficient topology-aware solution for CNDP to improve the reliability of WSNs. Full article

This paper proposes a Quantum-Machine-Learning-enhanced Double Deep Q-Network (QML-DDQN) for the supervisory control of battery–supercapacitor hybrid energy storage systems in islanded microgrids. This method combines a variational quantum circuit as a nonlinear state encoder with a DDQN decision layer for safe discrete dispatch. Three representative islanded cases, Island 1, Island 2, and Island 3, were used to evaluate the robustness under different scales, renewable profiles, and reliability requirements. Compared with deterministic optimization, predictive control, metaheuristics, and classical reinforcement-learning baselines, the proposed controller delivers the best overall trade-off among operating cost, renewable utilization, diesel reduction, and loss-of-power-supply risk. On the three-case averages, QML-DDQN reduces daily cost and LPSP by 0.99% and 4.04% relative to DDQN, by 2.91% and 7.32% relative to DQN, and by 9.09% and 16.63% relative to MILP; it also lowers curtailment and diesel share by up to 13.02% and 9.09%, respectively, across the same benchmark sets. The largest gains appear under volatility-dominated and stress-scenario conditions, where the quantum encoder strengthens the state representation, and the DDQN backbone mitigates value overestimation. These results highlight the practical advantages of the QML-DDQN as a resilient and high-value supervisory strategy for islanded hybrid energy storage operations. Full article

Unmanned Aerial Vehicles (UAVs) are used in numerous practical applications in industry, science, and ecology; however, the large-scale use of UAVs is hampered by the limited onboard energy capacity. Increasing the energy efficiency of UAVs has thus become one of the most important tasks in UAV research. This review examines approaches to energy optimization of UAVs using algorithms and artificial intelligence (AI). It evaluates and compares algorithms and methods used to optimize energy efficiency of trajectory planning, adaptive speed control, battery management in mission planning, and navigation that accounts for environmental characteristics. It differs from those focusing on hardware solutions by highlighting optimization problems where energy usage is considered the key target for optimization and not a limiting constraint. The analyzed methods have been categorized into five groups, including classical optimization, metaheuristics, machine learning (ML), reinforcement learning (RL), and hybrid approaches. Key approaches such as RL, Model Predictive Control, evolutionary algorithms, and data-driven energy modeling have been outlined and compared with regard to energy-model accuracy, type of validation, scalability, and deployment readiness. Additionally, it emphasizes practical aspects such as the accuracy of energy modeling, real-time capabilities, scalability to multiple UAVs, and robustness to environmental uncertainty. Finally, this review provides directions for future research that will help develop sustainable, intelligent, and energy-efficient UAVs. Full article

Population-based bio-inspired metaheuristics are the dominant tools for tuning hybrid decomposition–deep-learning forecasters, yet their relative behavior on a common problem is rarely assessed with a leakage-free, physically meaningful protocol. We benchmark eight metaheuristics on the joint nine-dimensional hyperparameter optimization of a discrete-wavelet-transform bidirectional-LSTM (DWT–BiLSTM) architecture for short-term wind speed forecasting, using 409,152 hourly observations from eight meteorological stations. The set comprises six nature-inspired methods (Artificial Bee Colony, ABC; genetic algorithm, GA; Particle Swarm Optimization, PSO; Grey Wolf Optimizer, GWO; Hippopotamus Optimization, HO; and the Raindrop Optimizer) together with two recent metaphor-free or social variants (the Farthest-better Nearest-worse Optimizer, FNO; and the Tuckman Optimization Algorithm, TOA). A multi-stage protocol covers 30 independent runs per algorithm, a joint-versus-sequential comparison, a genuine rolling-origin out-of-sample evaluation, and component ablation. Friedman testing reveals significant differences (χ² = 49.76; p < 10⁻⁸), with the Grey Wolf Optimizer attaining the best mean rank (2.27) and Pareto-dominant run-time; ablation shows the DWT front-end is essential (Cohen’s d = 13.09) and bidirectionality negligible at the one-hour horizon (p = 0.674). Critically, evaluating forecasts in reconstructed physical units reveals that the per-component advantage does not persist: at the one-hour horizon the reconstructed forecast does not exceed a naive persistence baseline (skill ≈ −0.5 in m/s versus +0.44 in normalized component space), a discrepancy independent of decomposition leakage that we report transparently. This work thus contributes a rigorous, leakage-controlled bio-inspired benchmark and a cautionary evaluation methodology. Full article

Designing time-optimal trajectories for solar sail spacecraft is highly difficult due to the strong nonlinearity of the solar radiation pressure model, the attitude–orbit coupling, and the prevalence of local minima, which often lead to the failure of gradient-based solvers lacking adequate initial guesses. To address this issue, the present study proposes a two-stage hybrid optimization framework: a coarse-search stage uses B-spline-parameterized metaheuristics to identify a dynamically feasible trajectory, which then serves as a physics-informed warm start for direct-collocation-based gradient refinement, strictly satisfying the full nonlinear dynamics and terminal constraints. The methodology is validated through three increasingly difficult space missions: a rendezvous between Earth and Mars (Case A), a near-Earth asteroid rendezvous considering non-ideal optical reflection (Case B), and a Solar Polar Orbiter mission necessitating an 82.75° inclination adjustment with a thermal safety constraint (Case C). Statistical evaluations reveal that no single metaheuristic dominates universally; each algorithm’s suitability is contingent on the problem’s constraint structure. The hybrid framework further shows a consistent advantage over stand-alone direct collocation: by redirecting the gradient solver toward favorable convergence basins, it locates transfer solutions that remain inaccessible from a cold start. Full article

A hybrid algorithm combining two metaheuristics is proposed to solve the flow-shop scheduling problem, aiming to minimise the makespan (Cmax). This approach accounts for random machine failures and limited buffer capacity between machines. Since flow-shop scheduling problems are NP-hard, the metaheuristics could be used to solve them effectively. Researchers proved that the hybridisation of metaheuristics would improve the solution quality. Therefore, this study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm. The initial solutions are generated using a dynamic generation technique that relies on a set of constructive heuristics. The optimal solutions from the PIOA serve as input for the ABC algorithm. Various local search and variable neighbourhood search methods are also included to enhance solution quality. Extensive computational experiments, which focus on industrial scheduling scenarios and benchmark problem instances, are conducted to test the performance of the hybrid algorithm. Statistical analysis shows that the proposed algorithm outperforms other algorithms found in the existing literature. Full article

This paper aims to present efficient alternative mixed-integer non-linear programming models and a customized hybrid metaheuristic, the Genetic-Based Algorithm (GBA), for a resource-constrained project-scheduling problem with a flexible network structure (RCPSP-FNS). We also consider the cost–time trade-off in the problem with a flexible network by using activity-duration compression. We present three approaches to solve the problem, including a mixed-integer non-linear program (MINLP) using binary variables representing activity completion times (MINLP1), an alternative mixed-integer non-linear program using integer variables representing activity-completion times (MINLP2) that has not presented before in RCPSP-FNS modeling, and the GBA. A total of 35 different problems are solved to examine the computational efficiency of the solution approaches. The MINLP1 and MINLP2 models are both solved by the GEKKO solver. The results indicate that solving the MINLP2 model can reach the optimal objective value obtained by solving the MINLP1 model in significantly less time. In addition, the proposed genetic-based algorithm can solve some large problems in a more efficient way in comparison to solving MINLP1 by using GEKKO. However, solving the MINLP2 model using GEKKO is the most efficient solution approach in comparison to both MINLP1 and the proposed genetic-based algorithm. MINLP2 can be solved to proven optimality (in much less time) for problems in which the MINLP1 model can, at most, reach near-optimal solutions. Full article

The spatial distribution of occupants shapes energy use and work-related performance. Algorithmic seating optimization can shorten communication distances, reduce electricity for lighting, and increase daily light exposure, whereas prior studies often targeted a single objective. Given the nondeterministic polynomial-time (NP)-hardness of key subproblems and the complexity of multi-objective search, this study evaluates heuristic and metaheuristic methods driven by sensor data from an open-plan office. Evolutionary, sampling-based, and model-based approaches are compared in terms of Pareto dominance, solution diversity, stability, convergence, and computation time. Results show that multi-criteria optimization with real-world data yields clear differences in performance profiles and search space exploration. Non-dominated Sorting Genetic Algorithm III (NSGA-III) contributes the highest share of Pareto solutions, while Hybrid Metaheuristics (HMH) achieves the largest target space coverage (hypervolume). Markov Chain Monte Carlo (MCMC) and Pareto Simulated Annealing (PSA) deliver particularly stable gains in light dose, while Bayesian optimization contributes no Pareto solutions in the present setting. Certain user pairings and spatial allocation patterns remain consistent across strategies, indicating persistent structural properties of the search space. Fast methods such as Deep Optimization (DO), Multi-Objective Pareto Simulated Annealing (PSA-Multi), Mulit-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), and MCMC are efficient, whereas NSGA-III offers the highest solution quality at greater computational cost. These findings advance understanding and support algorithm selection and space analysis for similar combinatorial allocation problems. Full article

Background: Reducing production time while making efficient use of resources is a key challenge in modern manufacturing, particularly in environments with high variability in specific components for the automotive industry. Methods: This paper presents a hybrid approach to production scheduling that combines linear programming (LP) principles with heuristic decision-making. A structured literature review is conducted to compare exact methods, heuristics, and metaheuristics in terms of their applicability and limitations. Based on this analysis, a hybrid scheduling method is proposed, where LP defines the objective function and constraints, while heuristic rules enable efficient assignment of operations to workstations under capacity limitations. The approach is validated through a case study involving over 900 product variants in an automotive part production system characterized by interchangeable workstations. The proposed heuristic algorithm was tested in terms of real company daily scheduling performance and compared with former scheduling performance. Results: The results show that the proposed approach achieves better solution quality with significantly lower computational effort, while also improving time utilization and production efficiency. Conclusions: The hybrid LP-heuristic approach provides a computationally efficient and practical tool for real-time production scheduling in high-variability manufacturing environments, effectively balancing solution quality and sub-minute execution speed under strict capacity constraints. Full article

Optimal sizing and design of renewable microgrids and hybrid renewable energy systems require balancing renewable resource variability, demand uncertainty, storage operation, reliability, and techno-economic constraints. Artificial intelligence and metaheuristic optimization strategies have been increasingly used to address these challenges; however, the evidence remains methodologically heterogeneous. This scoping review maps the literature on artificial intelligence, learning-based, metaheuristic, heuristic, and optimization-based strategies for renewable microgrid sizing and design. The review followed PRISMA-ScR guidelines. Searches were conducted in Scopus and the Web of Science Core Collection for research articles published between 2009 and March 2026. A total of 69 studies were included. Metaheuristics dominated the field, appearing in 63 studies, with particle swarm optimization and genetic algorithm-based strategies as the most frequent methodological families. Artificial intelligence and learning-based strategies were mainly used to support forecasting, surrogate modeling, uncertainty handling, and energy management. The most recurrent configurations involved photovoltaic, wind, and battery storage systems, often with diesel backup in stand-alone or off-grid contexts. The literature is strongly oriented toward metaheuristic sizing of PV–wind–battery microgrids, with emerging integration of AI-assisted prediction and decision-support strategies. Future studies should address reproducibility, uncertainty modeling, real-world validation, degradation assessment, explainability, and scalability. Full article

Construction schedule compression requires balancing project-duration reduction against direct, indirect, and risk-related cost increases under uncertain activity performance. Many construction time–cost trade-off models still rely on deterministic durations or predefined execution modes, which limits their ability to represent practical activity-level crashing decisions under uncertainty. This study develops a stochastic risk-aware time–cost optimization framework for construction scheduling using bounded integer crash-day decision variables. Activity durations are represented using triangular distributions based on optimistic, most-likely, and pessimistic estimates, while Monte Carlo simulation is used to propagate uncertainty through the precedence network. Expected and Conditional Value-at-Risk-oriented indicators are integrated into risk-adjusted duration and cost measures, which are then combined through a nonlinear normalized objective function. A Hybrid Genetic Algorithm–Gray Wolf Optimizer is implemented to solve the resulting discrete stochastic optimization problem and is benchmarked against seven metaheuristic algorithms under identical evaluation conditions. The framework is demonstrated using a 30-activity construction project reconstructed from Microsoft Project data. The proposed Hybrid GA–GWO reduced the deterministic project duration from 895 to 699 working days and achieved the best descriptive objective performance across 30 independent runs. However, after Bonferroni correction, its differences from the Genetic Algorithm and MPGWO-DLL were not statistically significant, indicating that these methods remain competitive alternatives. Additional Monte Carlo convergence, tornado sensitivity, correlated-duration sensitivity, and computational-time analyses were added to evaluate the stability, parameter dependence, and practical applicability of the framework. The findings show that the proposed framework can support risk-aware construction schedule-crashing decisions by identifying activity-level acceleration plans while explicitly accounting for downside schedule and cost risk. Broader validation in larger and more diverse real-world projects remains necessary. Full article

Nonlinear systems of equations arise across engineering, physics, and biological modeling; however, classical Newton-type methods may fail when the initial approximation lies outside the convergence region of the NJN local solver. This work proposes a two-stage hybrid framework that couples Particle Swarm Optimization (PSO) for global exploration with the fifth-order Newton–Jarratt (NJN) iterative method for local refinement. The fifth-order convergence of the NJN phase, established through a complete Fréchet-derivative Taylor expansion with explicitly computed error constants, guarantees rapid local convergence once PSO delivers a sufficiently close starting point. The framework is validated on four test problems with increasing numbers of dimensions ($n=2,5,20,40$): a two-dimensional benchmark algebraic system, a five-dimensional metabolic network model for ethanol production in Saccharomyces cerevisiae, and two large-scale Hammerstein integral equation systems. Over 30 independent runs per method and under the tested conditions, PSO-NJN achieves 100% convergence with mean final residuals of order ${10}^{-14}$–${10}^{-16}$, while pure PSO fails completely on the high-dimensional Hammerstein cases ($n=20,40$) and achieves only 10% success on the metabolic model. These results confirm that combining global metaheuristic search with high-order local refinement yields a robust, scalable solver for complex biological and engineering nonlinear systems, though performance on problems with dense high-dimensional Jacobians may require further adaptation. Full article

Trajectory planning, which determines a route from a starting position to a target position within a given airspace, is critical to unmanned aerial vehicle (UAV) mission execution. Many existing meta-heuristic approaches to three-dimensional (3D) trajectory planning aggregate competing requirements into a weighted cost and may suffer from limited adaptability when the environment changes. This paper formulates 3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and proposes a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA). The proposed method organizes objective-oriented swarms within a cooperative search framework and facilitates information exchange through archive sharing, thereby coordinating the search process among different objectives. The memory-time and diverse behavior strategies adjust search behaviors and solution perturbation to balance convergence and diversity. A hybrid change response strategy combines historical information reuse with diversity restoration after dynamic changes. Comparative experiments on dynamic benchmark problems and UAV trajectory planning scenarios demonstrate competitive convergence and adaptation performance, together with a favorable trade-off between solution quality and computational cost. Incremental ablation and parameter-sensitivity analyses further indicate the cumulative benefit of the integrated design and the stable performance of the selected parameter configuration across the tested settings. Full article