On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, ten meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. Besides, an integrative ranking of the models is achieved using average ranking algorithm.

Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

Antlion optimization algorithm has good search and development capabilities, but the influence weight of elite ant lions is reduced in the later stage of optimization, which leads to slower algorithm convergence and easy to fall into local optimization. For this purpose, an antlion optimization algorithm based on immune cloning was proposed. In the early stage, the reverse learning strategy was used to initialize the ant population. The Cauchy mutation operator was added to the elite antlion update to improve the later development ability of the algorithm; finally, the antlion was cloned and mutated with the immune clone selection algorithm to change the position and fitness value of the antlion, and further improve the algorithm's global optimization ability and convergence accuracy. 10 test functions and a 0~1 backpack were used to evaluate the optimization ability of the algorithm and applied to the size and layout optimization problems of the truss structure. The optimization effect was found to be good through the force effect diagram. It is verified that ICALO is applied to combinatorial optimization problems with faster convergence speed and higher accuracy. It provides a new method for structural optimization.This article is submitted as original content. The authors declare that they have no competing interests.

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

Fault diagnosis of gearboxes based on vibration signal processing is challenging, as vibration signals collected by acceleration sensors are typically a nonlinear mixture of unknown signals. Furthermore, the number of source signals is usually larger than that of sensors because of the practical limitation on sensor positions. Hence, the fault characterization is actually a nonlinear underdetermined blind source separation (NUBSS) problem. In this paper, a novel NUBSS algorithm based on kernel independent component analysis (KICA) and antlion optimization (ALO) is proposed to address the technical challenge. The mathematical model demonstrates the nonlinear mixing of source signals in the underdetermined cases. Ensemble empirical mode decomposition is used as a preprocessing tool to decompose the observed signals into a set of intrinsic mode functions that suffers from the problem of redundant components. The correlation coefficient is utilized to eliminate the redundant components. An adaptive threshold singular value decomposition method is proposed to estimate the number of source signals. Then a whitening process is carried out to transform the overdetermined blind source separation (BSS) into determined BSS, which can be solved by the KICA method. However, the reasonable selection of parameters in KICA limits its application to some extent. Therefore, ALO and Fisher’s linear discriminant analysis are adopted to further enhance the accuracy of the KICA method. The separation performance of the proposed method is assessed through simulation. The numerical results show that the proposed method can accurately estimate the number of source signals and attains a higher separation quality in tackling nonlinear mixed signals when compared with the existing methods. Finally, the inner ring fault experiment is conducted to preliminarily validate the practicability of the proposed method in bearing fault diagnosis.

PEMODELAN NOWCASTING TINGKAT PENGANGGURAN TERBUKA MENGGUNAKAN DATA GOOGLE TRENDS DENGAN METODE ANTLION OPTIMIZATION-SUPPORT VECTOR REGRESSION

Tingkat Pengangguran Terbuka adalah salah satu indikator strategis nasional yang didapatkan oleh BPS melalui Survei Angkatan Kerja Nasional (SAKERNAS) yang dilaksanakan dua kali dalam satu tahun, yaitu pada Februari dan Agustus. Terdapat lag waktu tiga bulan sejak data dikumpulkan hingga data dipublikasikan yang dapat diatasi menggunakan nowcasting dengan memanfaatkan data Google Trends. Data indeks pencarian dari kueri yang berkaitan dengan proses pencarian kerja kemudian diseleksi menggunakan koefisien korelasi Pearson dan LASSO hingga diperoleh kueri pencarian yang relevan dengan nilai TPT. Pemodelan nowcasting dilakukan menggunakan Support Vector Regression dengan parameter yang dioptimisasi menggunakan Ant-Lion Optimization. Hasil dari penelitian ini, diperoleh tiga kueri pencarian yang relevan dan dapat digunakan untuk nowcasting TPT Jawa Barat yaitu “lowongan kerja”, “lowongan pekerjaan”, dan “job”. Hasil dari pemodelan juga menunjukkan MAE, MAPE, dan RMSE yang baik.