General Layout Planning Model of Landscape Ceramic Sculpture Based on NSGA - Ⅱ Algorithm

Abstract

The current overall layout planning model matrix of landscape ceramic sculpture is generally unidirectional, and the planning efficiency is low, resulting in a decline in the layout optimization ratio of the model. Therefore, the design and verification analysis of landscape ceramic sculpture’s overall layout planning model based on the Nondominated Sorting Genetic Algorithm (NSGA - II) algorithm is proposed. According to the actual planning needs and standards, first set the basic layout points, establish a cross-planning matrix in a multi-level manner, and improve the efficiency of the overall layout planning of the sculpture. The NSGA - II calculation landscape ceramic sculpture layout planning structure is constructed on this basis, and the model design is realized by level conversion. This novel NSGA-II with level conversion performs better layout planning when compared with other conventional models. The final test results show that through three stages of layout optimization processing, compared with the initial planning layout, the optimal layout optimization ratio for the setting of the plaza sculpture can reach more than 60%, indicating that with the help of this method, the layout planning of sculpture has been further improved, the space has been expanded, and has practical application value.

Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

“…However, obtaining parameters for each cycle can be challenging in practice because battery charge and discharge patterns are often irregular. To address this, the rain-fow counting method is utilized to compute battery loops and their associated parameters [23]. Figure 6 illustrates the process of calculating ES battery capacity degradation using this method.…”

“…Tis is because literature [18] does not account for energy action losses. In comparison, literature [24] uses data with an additional 17% normal distribution bias, leading to a reduction of 389 days in the lifespan of the ESS when compared to literature [23]. In contrast to literature [23], which optimizes the entire cycle of data, the algorithm presented in this paper utilizes data from the current time cycle, resulting in the longest lifespan for the system.…”

“…In contrast to literature [23], which optimizes the entire cycle of data, the algorithm presented in this paper utilizes data from the current time cycle, resulting in the longest lifespan for the system. Tis surpasses the performance of literature [23] by 850 days. Tus, it can be inferred that energy capacity losses, which lead to frequent charging and discharging of the ESS, accelerate the decay of its lifespan and ultimately reduce its long-term benefts.…”

“…Tis shows that the improved algorithm in this paper can learn the scheduling strategy that makes the return higher. Compared to literature [18], literatures [23,24] reduce the number of charging and discharging loops, resulting in lower power for these operations. In comparison to literatures [18,23], our algorithm achieves the least number of charging and discharging loops for the ESS.…”

“…Compared to literature [18], literatures [23,24] reduce the number of charging and discharging loops, resulting in lower power for these operations. In comparison to literatures [18,23], our algorithm achieves the least number of charging and discharging loops for the ESS. Tis is because in our algorithm, we assign a higher weight coefcient to the cost of ES, which extends the lifespan of the system.…”