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Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

Conventional methods for manufacturing rotor blades, such as composite construction and die casting, are hindered by high costs due to expensive molds, while 3D printing often results in poor quality or high production costs with unfavorable cost‐per‐part scaling. Moreover, conventional airfoil designs perform poorly at Reynolds numbers below 100,000, necessitating larger rotors. This becomes especially problematic in wind tunnel studies, where multiple rotors must fit within a single wind tunnel for wake or multirotor research, significantly increasing both building costs and wind tunnel requirements. To address these challenges, this study develops high‐performance rotor blades for micro wind turbines that are aerodynamically efficient under low Reynolds number conditions and easy to manufacture. Using cambered plate airfoils, the optimization process employed a class shape transformation and seventh‐degree Bernstein polynomials. Aerodynamic performance was analyzed using XFOIL, with evaluations conducted at Reynolds numbers of 30,000, 40,000, and 50,000 to ensure robust performance across realistic operating scenarios. The iterative optimization employed both single‐objective and (genetic) multi‐objective algorithms, targeting both aerodynamic efficiency and manufacturability. The blade tested with the optimized MB‐LR2‐7.5 airfoil exhibited good performance in wind tunnel tests, closely matching Blade Element Momentum (BEM) simulations. This research highlights the potential of cambered plate airfoils to improve micro wind turbine performance while maintaining ease of manufacturing, with potential applications in unmanned aerial vehicles (UAVs), drone propellers, and ventilation systems. The findings advance the understanding of aerodynamic optimization in low Reynolds number environments, paving the way for more efficient and cost‐effective rotor designs.

To improve the efficiency and safety of intelligent construction scheduling, this work explores an optimization method for construction schedules based on multi-objective optimization (MOO) algorithms. This work focuses on the generation and optimization processes of scheduling plans and conducts safety assessments and resource efficiency analyses of the generated plans. The proposed optimized model is compared with classical MOO algorithms. These algorithms include Multi-Objective Evolutionary Algorithm based on Decomposition with Differential Evolution (MOEA/D-DE), Strength Pareto Evolutionary Algorithm 2 with Shift-based Density Estimation (SPEA2+SDE), and Non-dominated Sorting Genetic Algorithm III (NSGA-III). Based on the experimental results, the proposed optimized model outperforms three classic MOO algorithms across multiple key performance indicators. In terms of Hypervolume, the value achieved by the proposed model is 0.722, indicating that its solution set covers the objective space more effectively, demonstrating stronger diversity and global search capability. Furthermore, on the indicators of Generative Distance and Inverse Generative Distance, the proposed model attains lower values of 0.008 and 0.061, suggesting that the solution set is closer to the optimal front, with higher precision. In addition, the Spacing Metric value of 0.011 further shows that the solution set generated by the proposed model is more evenly distributed in the objective space. It avoids excessive clustering and enhances the uniformity and adaptability of the solutions. This uniformity is critical in practical construction scheduling optimization. This is because, under multiple conflicting objectives, a well-distributed solution set provides decision-makers with more options, enabling a better balance between safety and resource efficiency. Regarding safety assessment, Plan C has a high score of 4.63, indicating that under the optimization of the proposed model, the construction plan can achieve excellent performance in resource utilization and provide better safety guarantees. Similarly, Plan D, which demonstrates the highest resource efficiency, receives an overall score of 4.72, showcasing its outstanding advantages in resource usage and scheduling efficiency. These results validate the proposed model's applicability and flexibility under different constraints and objective functions.

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.

Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.

Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.

Background Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. Results The results showed that SVR (R 2  > 0.92) had better performance accuracy than MLP (R 2  > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum’s somatic embryogenesis accurately. Conclusions SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

In recent years, the use of Building Information Modeling (BIM) with Building Energy Modeling (BEM) has become the primary research focus for reducing the energy consumption of buildings in the planning and operational phases. The combination of BIM and BEM offers advantages for the various phases of a construction project. However, there are currently very few studies that can integrate multi-objective optimization algorithms into the BIM-BEM process to achieve automatic optimization and effectively manage many aspects of building development. In this study, an EnergyPlus integrated multi-objective jellyfish search (EP-MOJSO) system was developed, utilizing an optimization algorithm to find the best thermal insulation layers for an Aluminum composite material (ACM) wall. The goal is to reduce the energy consumption and total cost in a BIM-BEM environment. In the case study, the authors successfully applied the system to a real building, resulting in a 10.7% reduction in total cost and a 65 kWh/m 2 /year reduction in EUI. It is expected that the results of the study will open up new ways of using algorithms for multi-criteria optimization in BIM models to optimize various project factors such as energy and total cost and thus make an important contribution to sustainable building design.

The combined method for interval forecasting (CMIF) is proposed for improved real-time prediction of wind speed uncertainty to facilitate wind turbine operation and power grid dispatching. Time-varying filtering for empirical mode decomposition and phase space reconstruction are used to decompose and reconstruct the original wind speed sequence to solve chaotic phenomena and eliminate noise. Statistical and machine learning models are considered as candidates, and models with excellent performances are selected. Finally, the selected models are combined by a multi-objective optimizer to obtain the final prediction. Experiments were performed using data from the Gansu wind tower, and the results showed that CMIF improved the accuracy of the predicted wind speed interval by 1.07–55.37% compared with single models. The prediction interval had a narrow width while maintaining a high coverage rate, which facilitated accurate quantification of the wind speed uncertainty.

The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions' space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.