{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:10:29Z","timestamp":1760710229651,"version":"build-2065373602"},"reference-count":91,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The sine cosine algorithm (SCA) is a population-based stochastic optimization method that updates the position of each search agent using the oscillating properties of the sine and cosine functions to balance exploration and exploitation. While flexible and widely applied, the SCA often suffers from premature convergence and getting trapped in local optima due to weak exploration\u2013exploitation balance. To overcome these issues, this study proposes a multi-faceted SCA (MFSCA) incorporating several improvements. The initial population is generated using dynamic opposition (DO) to increase diversity and global search capability. Chaotic logistic maps generate random coefficients to enhance exploration, while an elite-learning strategy allows agents to learn from multiple top-performing solutions. Adaptive parameters, including inertia weight, jumping rate, and local search strength, are applied to guide the search more effectively. In addition, L\u00e9vy flights and adaptive Gaussian local search with elitist selection strengthen exploration and exploitation, while reinitialization of stagnating agents maintains diversity. The developed MFSCA was tested against 23 benchmark optimization functions and assessed using the Wilcoxon rank-sum and Friedman rank tests. Results showed that MFSCA outperformed the original SCA and other variants. To further validate its applicability, this study developed a fuzzy c-means MFSCA-based adaptive neuro-fuzzy inference system to forecast energy consumption in student residences, using student apartments at a university in South Africa as a case study. The MFSCA-ANFIS achieved superior performance with respect to RMSE (1.9374), MAD (1.5483), MAE (1.5457), CVRMSE (42.8463), and SD (1.9373). These results highlight MFSCA\u2019s effectiveness as a robust optimizer for both general optimization tasks and energy management applications.<\/jats:p>","DOI":"10.3390\/computers14100444","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:07:22Z","timestamp":1760706442000},"page":"444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved Multi-Faceted Sine Cosine Algorithm for Optimization and Electricity Load Forecasting"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0444-7929","authenticated-orcid":false,"given":"Stephen O.","family":"Oladipo","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5770-3669","authenticated-orcid":false,"given":"Udochukwu B.","family":"Akuru","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0275-8670","authenticated-orcid":false,"given":"Abraham O.","family":"Amole","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, and Telecommunication Engineering, Bells University of Technology, Ota 112104, Nigeria"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103671","DOI":"10.1016\/j.advengsoft.2024.103671","article-title":"Enhanced Artificial Hummingbird Algorithm for Global Optimization and Engineering Design Problems","volume":"194","year":"2024","journal-title":"Adv. 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