Employing Data-Driven NOA-LSSVM Algorithm for Indoor Spatial Environment Design

This study aims to enhance the precision and efficiency of indoor spatial design for college physical bookstores in the context of the new media environment. To achieve this, a novel intelligent analysis model was developed by integrating the Navigator Optimization Algorithm (NOA) with the Least Squ...

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Vydáno v:International journal of advanced computer science & applications Ročník 16; číslo 1
Hlavní autoři: Wang, Di, Ma, Hui, Lv, Tingting
Médium: Journal Article
Jazyk:angličtina
Vydáno: West Yorkshire Science and Information (SAI) Organization Limited 2025
Témata:
Accuracy
Algorithms
Bookstores
Case studies
College campuses
College students
Computer science
Convergence
Design analysis
Design improvements
Design optimization
Design parameters
Divergence
Efficiency
Indoor environments
Noise control
Optimization algorithms
Parameter identification
Performance evaluation
Performance measurement
Principles
Quiet areas
Support vector machines
User experience
ISSN:2158-107X, 2156-5570
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Abstract This study aims to enhance the precision and efficiency of indoor spatial design for college physical bookstores in the context of the new media environment. To achieve this, a novel intelligent analysis model was developed by integrating the Navigator Optimization Algorithm (NOA) with the Least Squares Support Vector Machine (LSSVM). The research analyzes the relationship between the new media environment and bookstore design, identifies key design principles, and establishes performance metrics. The proposed NOA-LSSVM model optimizes design parameters by utilizing a hybrid convergence-divergence search mechanism, achieving improved accuracy and computational efficiency. A case study of Jilin Jianzhu University's bookstore was conducted to evaluate the model's performance. The NOA-LSSVM model was compared with three other optimization algorithms: the Flower Pollination Algorithm (FPA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). Results showed that the NOA-LSSVM model achieved superior accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.9, significantly lower than FPA (4.6), WOA (3.8), and SCA (4.2). Additionally, the model exhibited faster convergence and enhanced design efficiency, optimizing the bookstore's functional zones and spatial layout to balance dynamic and quiet areas effectively. In conclusion, the NOA-LSSVM model demonstrates a robust capability to optimize indoor spatial design in the new media environment, outperforming traditional methods in accuracy and practicality. This study provides valuable insights for integrating intelligent algorithms into spatial design processes, with the potential for broader applications in other commercial or educational spaces. Future research should focus on extending the model's generalizability and incorporating advanced media technologies for enhanced user experiences.
AbstractList This study aims to enhance the precision and efficiency of indoor spatial design for college physical bookstores in the context of the new media environment. To achieve this, a novel intelligent analysis model was developed by integrating the Navigator Optimization Algorithm (NOA) with the Least Squares Support Vector Machine (LSSVM). The research analyzes the relationship between the new media environment and bookstore design, identifies key design principles, and establishes performance metrics. The proposed NOA-LSSVM model optimizes design parameters by utilizing a hybrid convergence-divergence search mechanism, achieving improved accuracy and computational efficiency. A case study of Jilin Jianzhu University's bookstore was conducted to evaluate the model's performance. The NOA-LSSVM model was compared with three other optimization algorithms: the Flower Pollination Algorithm (FPA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). Results showed that the NOA-LSSVM model achieved superior accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.9, significantly lower than FPA (4.6), WOA (3.8), and SCA (4.2). Additionally, the model exhibited faster convergence and enhanced design efficiency, optimizing the bookstore's functional zones and spatial layout to balance dynamic and quiet areas effectively. In conclusion, the NOA-LSSVM model demonstrates a robust capability to optimize indoor spatial design in the new media environment, outperforming traditional methods in accuracy and practicality. This study provides valuable insights for integrating intelligent algorithms into spatial design processes, with the potential for broader applications in other commercial or educational spaces. Future research should focus on extending the model's generalizability and incorporating advanced media technologies for enhanced user experiences.
Author Ma, Hui
Lv, Tingting
Wang, Di
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SubjectTerms Accuracy
Algorithms
Bookstores
Case studies
College campuses
College students
Computer science
Convergence
Design analysis
Design improvements
Design optimization
Design parameters
Divergence
Efficiency
Indoor environments
Noise control
Optimization algorithms
Parameter identification
Performance evaluation
Performance measurement
Principles
Quiet areas
Support vector machines
User experience
Title Employing Data-Driven NOA-LSSVM Algorithm for Indoor Spatial Environment Design
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