Muhammad Naim, Nordin and Mohd Herwan, Sulaiman and Nor Farizan, Zakaria and Zuriani, Mustaffa (2025) Enhanced multi-objective evolutionary mating algorithm with improved crowding distance and levy flight for optimizing comfort index and energy consumption in smart buildings. Franklin Open, 11 (100286). pp. 1-17. ISSN 2773-1871. (Published)
|
Pdf
1-s2.0-S2773186325000763-main.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
Abstract
This paper introduces a novel Multi-Objective Evolutionary Mating Algorithm (MOEMA) designed to address the inherent challenges of optimizing comfort index and energy consumption in smart building systems. While current Evolutionary Mating Algorithms (EMA) primarily focus on single-objective optimization and rely on weighted functions for handling multiple objectives, such approaches prove impractical for the complex trade-offs between comfort index and energy efficiency. The proposed MOEMA enhances the original EMA framework through two key innovations: an improved crowding distance function inspired by the Non-dominated Sorting Genetic Algorithm (NSGA) to enhance solution diversity and selection pressure, and the integration of Levy flight mechanics to improve exploration efficiency by balancing local and global searches. These enhancements enable MOEMA to effectively navigate complex multi-objective landscapes, leading to more diverse and well-converged Pareto-optimal solutions. The algorithm's performance is thoroughly assessed using the chosen benchmark functions and validated through practical applications in smart building environments. It simultaneously optimizes various comfort parameters, including temperature, illuminance, and air quality, while minimizing energy consumption and maximizing the comfort index. Comparative analysis against established algorithms, like NSGA-II demonstrates MOEMA's effectiveness in achieving superior solution diversity and convergence characteristics. The results indicate that MOEMA offers a robust framework for handling the complex balance between the smart building's comfort index and energy usage where it achieves 0.03 better at comfort index and with 10.65 lower energy consumption than NSGA-II. It contributing to the broader fields of building automation and sustainable development while aligning with Industry 4.0 initiatives.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Comfort index optimization, Crowding distance, Energy efficiency, Evolutionary Mating Algorithm, Levy flight, Multi-objective optimization, Pareto optimization, Smart buildings |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical and Electronic Engineering Technology Institute of Postgraduate Studies Faculty of Computing Centre for Research in Advanced Fluid & Processes (Fluid Centre) |
| Depositing User: | Dr. Mohd Herwan Sulaiman |
| Date Deposited: | 09 Jul 2025 01:04 |
| Last Modified: | 09 Jul 2025 01:04 |
| URI: | http://umpir.ump.edu.my/id/eprint/44973 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |