Search Results (200)

To enhance renewable energy accommodation and operational flexibility under high renewable energy penetration, this study proposes a multi-objective optimal capacity configuration method for an electricity–heat–hydrogen integrated energy system incorporating a reversible solid oxide cell (RSOC). First, considering the bidirectional electricity–hydrogen conversion capability and waste heat recovery of the RSOC, an electricity–heat–hydrogen multi-energy complementary system is constructed, and efficiency correction models are established for key energy conversion devices to characterize their part-load characteristics. Second, representative source–load scenarios are generated using Latin hypercube sampling and K-means clustering, and a multi-objective optimal capacity configuration model is formulated to minimize the annualized total cost and the wind and photovoltaic power curtailment rate. Finally, given the limitations of the non-dominated sorting genetic algorithm II (NSGA-II) in complex capacity configuration problems, such as premature convergence to local optima and insufficient population diversity, an adaptive crossover and mutation mechanism, a local search strategy, and a dynamic selection mechanism based on comprehensive crowding distance are introduced to improve its optimization performance. A balanced configuration scheme is then selected based on the knee point of the Pareto front obtained by the algorithm. Case-study results show that the Pareto solution set obtained by the improved NSGA-II (INSGA-II) has better overall quality than those obtained by NSGA-II and multi-objective particle swarm optimization (MOPSO). The resulting balanced configuration scheme has an annualized total cost of CNY 422.9 million and a wind and photovoltaic curtailment rate of 2.797%. The proposed method effectively coordinates system economic performance and renewable energy accommodation, enhances the coordinated utilization of electricity, heat, and hydrogen energy flows, and provides a reference for capacity planning of integrated energy systems under high renewable energy penetration. Full article

Airport surface scheduling at multi-runway airports is a complex system engineering task that balances efficiency, safety, and sustainability, making it a key research focus in the field of air traffic management. This study proposes a cosine curve-based pushback rate control strategy and a collaborative pushback and taxiing decision-making method for departure aircraft pushback and taxiing. Additionally, the Markov decision process under dynamic pushback control at dual-runway airports is analyzed. An adaptive departure operation optimization model is established. This model considers path conflicts, fuel consumption, taxiway queuing, and runway occupancy during an aircraft departure process, enhancing the operational efficiency in the temporal and spatial dimensions. In the aircraft departure process, a genetic simulated annealing algorithm with nested Markov state transitions is proposed as the optimization algorithm, utilizing a Q-learning algorithm to adaptively adjust crossover and mutation parameters. The effectiveness of the proposed model and algorithm is validated through simulation experiments at Beijing Capital International Airport. Results indicate that this approach can significantly reduce the estimated taxiing fuel-related operating cost by 17.53% in the simulated case, shorten average taxiway waiting time by 56.24%, and decrease average taxi completion time by 20.81%, thereby improving overall airport operational efficiency. Full article

Fatigue crack propagation prediction plays a critical role in structural health monitoring and remaining useful life (RUL) assessment of engineering structures. However, conventional particle filter (PF) algorithms may suffer from particle impoverishment and insufficient particle diversity, which can adversely affect prediction accuracy and stability. To address these limitations, a hybrid genetic algorithm–particle filter (GA-PF) is developed for fatigue crack propagation prediction by incorporating genetic operations, including selection, crossover, and mutation, into the PF framework to optimize particle distribution and enhance global search capability. The proposed method is evaluated using fatigue crack growth experimental data, and its performance is compared with that of the conventional PF algorithm. The results show that the GA-PF method achieves improved prediction performance for fatigue crack propagation and remaining useful life estimation. At 255,000 cycles, the GA-PF algorithm predicted a median RUL of 22,500 cycles, with a relative RUL error of 9.04%, whereas the conventional PF algorithm resulted in a relative RUL error of 45.41%. These results indicate the potential benefit of introducing genetic optimization into the particle filter framework for fatigue crack propagation prediction. The findings further suggest that the hybrid GA-PF method can improve predictive performance compared with conventional PF on the tested dataset. Full article

Background: Research on family support has primarily focused on emotional aspects, paying less attention to instrumental support and its concurrent association with food-related well-being. Aim: To explore direct and indirect actor and partner effects among parents’ instrumental family support, diet quality, and satisfaction with food-related life (SWFoL) in dual-earner families with adolescents. Methods: Online surveys were conducted with 860 dual-earner parent dyads and one adolescent child (mean age 12.7 ± 1.8 years; 49.8% female) in Chile. Family members completed the Adapted Healthy Eating Index and the SWFoL Scale; parents also answered an Instrumental Support subscale. Analyses were conducted using a mediation Actor–Partner Interdependence Model via structural equation modeling. Results: Mothers’ and fathers’ instrumental support was positively associated with their own SWFoL, both directly and indirectly via their diet quality. Parents’ support was also positively related to adolescents’ SWFoL directly and indirectly via adolescents’ diet quality. Symmetrical crossover effects between instrumental support and SWFoL emerged for both parents. Mothers’ support was additionally associated with fathers’ SWFoL indirectly through fathers’ diet quality. Conclusions: Instrumental support operates as a vital systemic resource within dual-earner households. By enhancing diet quality, tangible support protects family food-related well-being and embeds adolescents’ nutritional health within the parental resource ecosystem. Full article

The efficiency of ammunition support operations in the aircraft carrier intermediate ordnance staging deck is critical to sortie generation rates in naval aviation, yet joint scheduling and personnel allocation in this multistage, resource-constrained environment remains a challenging bi-objective optimization problem. This study develops a framework integrating an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with a marginal-benefit-based iterative feedback mechanism. The intermediate ordnance staging deck support process is decomposed into individual ammunition processing stations and formulated as a processflow model incorporating operation sequencing and personnel specialization constraints. A constraint decision model then dynamically reconciles the minimization of total makespan and personnel workload equilibrium through iterative marginal-benefit comparison across support teams. The NSGA-II is enhanced with an adaptive crossover-mutation mechanism and an improved elitism preservation strategy to strengthen global search capability. Validation on a typical carrier intermediate ordnance staging deck scenario demonstrates that the improved NSGA-II outperforms the conventional NSGA-II in convergence speed and Pareto front quality. Under the optimized configuration, the total makespan remains 3600 s with a workload balance metric of 1075 even as ammunition quantity doubles from two to four units. The proposed framework offers practical decision support for carrier ammunition operations and extends to other resource-constrained multi-objective scheduling domains. Full article

To address the multi-objective conflicts among cost, time, and carbon emissions in Guangxi cross-border container multimodal transport—under the influence of factors such as tariffs, fluctuating customs clearance efficiency, and carbon emission policies—this paper develops a multi-objective optimization model that integrates mixed time windows and scenario analysis. The model incorporates multiple elements, including transportation cost, transshipment cost, tariff cost, time value of cargo, carbon tax cost, in-transit and customs clearance time, and transshipment-related carbon emissions, making it more aligned with real-world cross-border operational scenarios. To effectively solve this complex model, an improved NSGA-II algorithm (I-NSGA2) is designed, which introduces an adaptive crossover and mutation operator along with an elite retention strategy to enhance convergence speed and solution diversity, while embedding a scenario parameter response mechanism to accommodate dynamic fluctuations in key parameters. Subsequently, an evaluation framework is constructed using the entropy weight–TOPSIS method to select recommended routes with favorable cost–time–carbon trade-offs from the Pareto frontier. A case study based on the Nanning–Kuala Lumpur route is conducted for validation. Experimental results demonstrate that the I-NSGA2 algorithm significantly outperforms MOPSO, MOEAD, and the standard NSGA-II in terms of IGD and HV metrics; time-sensitive cargo tends to favor rail-dominated routes, while low-cost cargo prefers combined road–water–rail routes. This study effectively addresses the route selection problem for Guangxi cross-border container multimodal transport under varying key parameters, and also provides a research foundation for optimizing cross-border multimodal transport route selection in other regions. Full article

Heavy crude oils exhibit complex rheological behavior governed by the interplay between temperature, pressure, and colloidal microstructure. In this work, the coupled influence of pressure and temperature on the viscoelastic response of five Mexican heavy crude oils was experimentally investigated under reservoir-relevant conditions. Steady and oscillatory shear measurements were performed at temperatures between 10 and 30 °C and at pressures ranging from atmospheric to 10.034 MPa. The results revealed pronounced shear-thinning behavior for all samples, with viscosity increasing systematically with pressure and decreasing strongly with temperature. Pressure effects were particularly significant at low temperatures, where enhanced elastic behavior and longer characteristic relaxation times were observed. Oscillatory measurements showed that pressurization shifted the viscoelastic crossover toward lower frequencies, indicating pressure-induced reinforcement of internal structures and reduced molecular mobility. The viscoelastic response was interpreted using a pressure-modified Arrhenius model, which successfully described the dependence of characteristic relaxation time on temperature and pressure. The fitted activation energies and activation volumes revealed systematic differences associated with crude oil composition and colloidal stability. Samples with lower resin-to-asphaltene ratios exhibited stronger pressure sensitivity and higher structural rigidity. The results demonstrate that pressure and temperature jointly control the relaxation dynamics and structural organization of heavy crude oils. These findings provide useful insights into flow assurance, transport, and production operations involving heavy crude systems under thermobaric conditions. Full article

To address the potential oscillation instability issues of grid-forming (GFM) inverter systems integrated into grids with reactive power compensation devices, an impedance-based model of the grid-connected system is established. The impedance analysis reveals that the compensation capacitors alter the grid impedance characteristics, leading to impedance crossover points with insufficient phase margin in the mid-to-high frequency range, thereby inducing oscillations. To address this, a negative capacitive and virtual resistive loop-based composite control strategy is proposed. The grid-side capacitive effects can be neutralized through the virtual negative capacitance, and the system damping is enhanced by a virtual resistive loop to maintain stable operation under varying short-circuit ratios. Hardware-in-the-loop experiments validate that the proposed scheme maintains stable operation under various capacitance switching and grid strengths, thereby enhancing the robustness of the GFM inverter in complex distribution network environments. Full article

The present article discusses the application of genetic algorithms (GA) for solving multi-criteria optimization (MCO) problems in underground mining. It has been demonstrated that GAs are highly effective in identifying Pareto-optimal solutions in scenarios involving multiple conflicting criteria, specifically the simultaneous minimization of equipment failure rate, energy consumption, and repair costs. The article presents the main approaches to solving MCO problems, a brief overview of the most popular algorithms, such as NSGA-II and SPEA2, and their improved versions. The proposed algorithm, implemented in Python 3.11 using the DEAP library, incorporates adaptive crossover, enhanced diversity preservation, and problem-specific initialization. Quantitative analysis shows that the proposed algorithm achieves a Hypervolume Indicator of 0.796, representing a 7.2% improvement over standard SPEA2, with an 18.3% reduction in Inverted Generational Distance (IGD), indicating superior convergence to the true Pareto front. The algorithm identifies optimal trade-offs between conflicting objectives—for example, a 15% reduction in energy consumption correlates with a 10% increase in failure rate—providing decision-makers with quantified insights for operational planning. The novel idea is the use of an adaptive crossover strategy, a composite diversity maintenance technique, and application-specific initialization—all of which have not been used before for optimizing underground mining machinery. A visual analysis of the results, employing a graphical representation of the Pareto front, confirmed that the proposed approach enables experts to make informed decisions based on production priorities. Full article

To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and collective information interaction behaviors of P. prominens (jumping spiders), this study proposes a novel bio-inspired meta-heuristic optimization algorithm, termed the Experience Exchange Strategy-Enhanced Philoponella Prominens Optimization (EESPPO). The proposed EESPPO integrates an Experience Exchange Strategy framework to reshape the search dynamics of the population through three progressive evolutionary stages: (1) In the Experience Scarcity (ESC) stage, the algorithm focuses on the construction and dynamic maintenance of an experience library to ensure the effective preservation of high-quality historical information; (2) In the Experience Crossover (ECR) stage, a random guidance vector generation mechanism is introduced to significantly enhance population behavioral diversity and the capability to escape local optima; (3) In the Experience Sharing (ESH) stage, an adaptive fusion update strategy is employed to achieve efficient information interaction and co-evolution among individuals. These three stages operate synergistically within the optimization cycle to establish a dynamic balance between global exploration and local exploitation, effectively overcoming the inherent defects of premature convergence in traditional meta-heuristics. Extensive empirical analysis based on the CEC2017 benchmark functions confirms that EESPPO comprehensively outperforms 12 existing advanced algorithms (including PPO, HSO, SGA, PSO, FLO, DE, HO, WOA, KEO, GWO, FDB-AGSK, and IVYPSO) in terms of convergence accuracy and robustness. Furthermore, the application of EESPPO to four challenging engineering design problems confirms its superiority. The experimental results validate the high precision and feasibility of EESPPO in solving complex constrained engineering problems. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model that minimizes both makespan and total energy consumption. An Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is proposed to solve this model. In the population initialization phase, chaotic mapping is integrated with multiple heuristic rules to generate a high-quality and uniformly distributed initial population. Furthermore, an enhanced elite selection mechanism is employed to effectively prevent premature convergence. Subsequently, adaptive crossover and mutation operators are designed to enable differentiated evolution across sub-populations, effectively coordinating global exploration and local exploitation. Finally, experimental results on the Brandimarte and Hurink benchmark datasets demonstrate the superiority of the proposed algorithm in terms of convergence and diversity, providing a robust solution for optimizing green industrial production scheduling. Full article

The rapid digitalization of district heating systems (DHSs) has driven the large-scale deployment of thermal Internet of Things (TIoT) sensors, which generate massive real-time operational data. Traditional centralized computing architectures struggle to process massive concurrent data. Furthermore, they fail to balance the stringent low-latency demands of real-time control tasks with the low-energy constraints of battery-powered terminal devices. To solve the complex problem of minimizing the weighted sum of system latency and energy consumption, we propose an Improved Mayfly Algorithm (IMA). The algorithm integrates five targeted structural enhancements: random position update masking, differential evolution (DE)-based crossover, targeted subset mutation with boundary scaling, adaptive population reset mechanism, and simulated annealing (SA)-driven local search, to efficiently navigate the high-dimensional rugged decision space and mitigate premature convergence. Extensive simulation results show that the proposed collaborative architecture achieves the lowest total system cost compared with traditional isolated computing paradigms (local-only, edge-only, and cloud-only). Notably, the proposed IMA reduces the total baseline weighted cost by 17.2% compared with the standard MA. Furthermore, under maximum practical industrial workloads (750 concurrent tasks, representing a highly complex 2250-dimensional MINLP space), the IMA maintains strong scalability and dominance, outperforming the second-best algorithm (BWO) by 15.8%. This research provides a low-latency, energy-efficient scheduling solution for TIoT-enabled DHS, and offers technical support for the intelligent and low-carbon transformation of urban energy infrastructure. Full article

Rural logistics faces persistent challenges such as high distribution costs, dispersed demand, and limited transport infrastructure, which hinder efficient last-mile delivery. To address these issues, this study proposes a bus–heterogeneous drone collaborative delivery system that integrates the fixed-route coverage of rural buses with the flexibility of multiple types of drones. The proposed system enables synchronized operations between buses and drones, where buses serve as mobile depots for drone launching and recovery along predefined routes. A mixed-integer programming (MIP) model is developed to jointly optimize bus schedules and drone routing under spatiotemporal synchronization constraints, considering drone endurance, payload capacity, energy consumption, and bus departure times. Due to the NP-hard nature of the problem, an Improved Genetic Algorithm (IGA) is designed, incorporating a three-layer encoding scheme, adaptive crossover and mutation operators, and a local search repair mechanism to enhance convergence and solution feasibility. A real-world case study from Baihe County, Shaanxi Province, China, is conducted to evaluate the performance of the proposed model and algorithm. Comparative experiments under the reported case-study setting show that the proposed bus–heterogeneous drone system achieves notable cost reduction and improved overall delivery performance. Sensitivity analyses further confirm the robustness of the model with respect to drone endurance, drone payload capacity, and bus stop quantity. This research contributes to the literature by bridging the methodological gap between truck–drone coordination and bus-based collaborative delivery, offering an innovative framework for sustainable rural logistics and multi-modal last-mile optimization. Full article

This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible solution space and often lead to multiple local optima. To enhance the performance of the original pied kingfisher optimizer (PKO), three strategies are incorporated: (i) a reverse differential crossover mechanism to improve global exploration and maintain population diversity; (ii) an enhanced diving-fishing operator to strengthen local exploitation; and (iii) an improved commensalism phase to enrich search directions and increase robustness. The performance of MSIPKO is evaluated on 12 benchmark functions from the IEEE Congress on Evolutionary Computation 2006 (CEC 2006) test suite and six classical engineering optimization problems. Experimental results demonstrate that MSIPKO outperforms several state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and stability, particularly for high-dimensional, nonlinear, and multi-constrained problems. Moreover, MSIPKO achieves superior or comparable solutions with fewer function evaluations, indicating its high efficiency and adaptability. These results confirm that MSIPKO is a promising tool for solving complex real-world constrained optimization problems. Future work will focus on extending the proposed algorithm to multi-objective and large-scale optimization scenarios. Full article