Jearsiripongkul Thira, Karbasforoushha Mohammad Ali, Khajehzadeh Mohammad, Keawsawasvong Suraparb, Thongchom Chanachai
Research Unit in Advanced Mechanics of Solids and Vibration, Department of Mechanical Engineering, Thammasat School of Engineering, Faculty of Engineering, Thammasat University, Pathumthani, 12121, Thailand.
Department of Architecture, Islamic Azad University, Tehran-West Branch, Tehran, Iran.
Sci Rep. 2024 Jul 31;14(1):17644. doi: 10.1038/s41598-024-68239-4.
In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.
本文采用一种名为改进瞬态搜索优化算法（ITSOA）的新算法来求解经典测试函数、优化建筑能耗以及优化混合能源系统发电量。传统的瞬态搜索优化算法（TSOA）的灵感来源于带有储能元件的电路的瞬态行为。采用罗森布罗克直接旋转技术来改善传统TSOA在探索与利用不平衡方面的性能。首先，研究了ITSOA在求解23个经典基准函数时的性能，结果表明，与传统TSOA、差分迁移算法（DMO）、正弦余弦算法（SHO）、遗传算法（GA）、蛾火优化算法（MRFO）和粒子群优化算法（PSO）相比，所提算法具有更优的性能。此外，还验证了ITSOA在解决建筑能耗优化（BEO）问题方面的能力，即最小化两座简单和详细建筑的能源使用。优化结果表明，与传统TSOA、DMO、SHO、GA、MRFO和PSO相比，ITSOA在单目标和多目标优化中的最优解获得了更低的成本函数值。此外，与先前方法相比，ITSOA在解决BEO问题上的优越性也得到了证实。此外，多目标优化结果表明，ITSOA能够基于模糊决策方法和建筑能源利用决策在帕累托前沿集中确定最终解。
