The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by the foraging behavior of brown bears. The proposed Multiobjective Brown Bear Optimization (MOBBO) algorithm is applied to five structural optimization problems, including 10-bar, 25-bar, 60-bar, 72-bar, and 942-bar trusses, aiming to minimize both mass and maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO's superior convergence, solution diversity, and effectiveness in addressing highly constrained problems. The hypervolume (HV) and inverted generational distance (IGD) metrics place MOBBO in first rank according to the Friedman test, with an average standard deviation of 0.0002. Moreover, the spacing-to-extent (STE) and generational distance (GD) metrics rank MOBBO second. The final Friedman rank test highlights MOBBO's overall dominance, achieving a first rank. Best Pareto plots, diversity graphs, and box plot analyses further suggest MOBBO's superior performance and convergence compared to existing algorithms. Therefore, the MOBBO algorithm can be effectively applied to various MO optimization tasks in industry, offering refined global optimization solutions and contributing valuable insights to the field of MO algorithms.
Semantic interoperability (SI) is defined as the capability of interpreting the nature of the information exchanged inside cloud computing (CC). For SI, ontology is selected as a solution. A hierarchical structure is offered by an ontology that comprises semantic relations between the application and the cloud server (CS). Even though different methods are introduced, ontology centered on semantic relation between the application and CS is not yet attained for achieving accurate interoperability. An ontology‐centered SI in CC utilizing a modified Shell Game optimization‐centered recurrent neural network (MSGO‐RNN) is presented in this work. Here, accessing the data from the data warehouse is initiated that experiences different standardization functions for improving the data’s quality, namely, data extraction, data cleaning, data transformation, data loading, and refreshing. Then, the data are passed via the semantic layer (SL), which offers a constant way of interpreting data. Then, for enhancing the accuracy rate of interoperability along with offering a low computational time, the relevant feature is chosen utilizing genetic crossover mutation‐centered improved fertile field (GCM‐IFF) optimization. Ontology is created utilizing the Protégé tool centered on the data, and respective manipulation is executed utilizing HermiT ontology web language (OWL) reasoner. Lastly, to attain cloud interoperability, the features are trained and tested by employing the MSGO‐RNN. The experimental outcome exhibits that when compared with the top‐notch methods, an accuracy of 96.32% is acquired by the framework for choosing the CS, and less time of 22567 ms is acquired by the framework for training.
The dynamic autonomous mobile ad hoc networks (MANETs) allow nodes to move everywhere quickly and communicate without the support of a network infrastructure. The movement of mobile nodes in MANETs leads to several issues, such as changeable topology, energy limitations, path conservation, and bandwidth ineffectiveness. This would negatively impact the network’s QoS. The primary aim of the present study is to select the optimal path in MANETs using the clustering technique for variable nodes (i.e., 100–1000). The network performance characteristic and energy efficiency of the present ORSMAN protocol are simulated in the ns3 simulator. The research results demonstrate that compared to the current OPSTSA, EAAORS, EERS, and OEERS techniques, the suggested ORSMAN has a higher packet delivery ratio, the lowest latency, the lowest jitters, and superior throughput. The maximum improvement in throughput is 29.81%, and in energy efficiency, it is 9.67% compared to current techniques. Additionally, a trade‐off study between energy efficiency and QoS reveals that the performance characteristics enhance QoS while reducing energy efficiency. This reduction may be attributed to variations in throughput, jitter, and latency. The total QoS performance was improved by 22.16% for the present study compared to the current techniques.
The study shows that refrigerants R1234ze (E) and R1234ze (Z), known for their low global warming potential, are likely to become primary choices in heat pump air conditioning for residential and commercial use. The research evaluates the thermodynamic properties of R1234ze (Z) using experimental, thermodynamic, and numerical analyses. It addresses F-gas regulation requirements and aligns with the Paris Agreement goals by exploring various refrigerants, including R227ea, R114, R236fa, R134a, R1234ze (Z), and R245fa, as potential candidates for evolving industry needs. The analysis indicates that R1234ze (Z) outperforms other working fluids in heat pump applications, with an optimized theoretical coefficient of performance (COP) at a condensation temperature 22 K lower than critical temperatures. However, actual COP deviates due to a significant pressure drop, especially with inadequate volumetric capacity. A key finding is that a substantial portion of the pressure reduction is attributed to mitigating irreversible losses, estimated at a condensation temperature of 70–75°C, emphasizing the complex relationship between pressure and performance. The study suggests that R1234ze (Z) is more suitable for high-temperature applications than traditional air conditioning systems. In a parallel assessment, the initial calculation of the coefficient of performance for R245fa evaluates the reliability of a new refrigeration industry arrangement. Test results for heat pump technology reveal that R1234ze (Z) achieves an impressive COP of up to 3.60 at a buildup temperature of 90°C with a temperature differential of 45 K. This positions R1234ze (Z) as a suitable choice for heat pump applications prioritizing simplicity in system design.
We consider the second-order cone function (SOCF) f:Rn⟶R defined by fx=cTx+d−Ax+b, with parameters c∈Rn, d∈R, A∈Rm×n, and b∈Rm. Every SOCF is concave. We give necessary and sufficient conditions for strict concavity of f. The parameters A and b are not uniquely determined. We show that every SOCF can be written in the form fx=cTx+d−δ2+x−x∗TMx−x∗. We give necessary and sufficient conditions for the parameters c, d, δ, M=ATA, and x∗ to be uniquely determined. We also give necessary and sufficient conditions for f to be bounded above.
