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Lai V, Huang YF, Koo CH, Ahmed AN, Sherif M, El-Shafie A. Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique. Sci Rep 2023; 13:6966. [PMID: 37117263 PMCID: PMC10147929 DOI: 10.1038/s41598-023-33801-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023] Open
Abstract
To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.
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Affiliation(s)
- V Lai
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia
| | - Y F Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia.
| | - C H Koo
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Mohsen Sherif
- Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
- National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
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Abdel-Basset M, El-Shahat D, Jameel M, Abouhawwash M. Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10403-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5974842. [PMID: 36072718 PMCID: PMC9444365 DOI: 10.1155/2022/5974842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/29/2022] [Accepted: 07/10/2022] [Indexed: 11/18/2022]
Abstract
The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels—Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.
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Guo D, Guo Y, Xing Y. Data on the Impact of Epidemic on Nursing Staff's Mental Health in the Context of Wireless Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3413815. [PMID: 35432842 PMCID: PMC9010164 DOI: 10.1155/2022/3413815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 01/29/2022] [Accepted: 02/05/2022] [Indexed: 11/18/2022]
Abstract
The research was aimed to analyze the impact of epidemic pneumonia on nursing personnel's mental health under wireless network background and to improve the selection of random forest classification (RFC) algorithm parameters by the whale optimization algorithm (WOA). Besides, a total of 148 in-service nursing personnel were selected as the research objects, and 148 questionnaires were recycled effectively. The collected data were analyzed by the improved RFC algorithm. In addition, the research investigated the impacts of demographic factors on nursing personnel's mental health by the one-way variance method. The results demonstrated that the accuracy of the improved algorithm in training samples and test samples reached 83.3% and 81.6%, respectively, both of which were obviously higher than those of support vector machine (SVM) (80.1% and 79.3%, respectively) and back-propagation neural network (BPNN) (78.23% and 77.9%, respectively), and the differences showed statistical meanings (P < 0.05). The Patient Health Questionnaire-9 (PHQ-9) showed that the depression levels of 9.46% of the included personnel were above moderate. The Generalized Anxiety Disorder (GAD-7) demonstrated that the anxiety levels of 3.38% of the included personnel were above moderate. The insomnia severity index (ISI) indicated that the insomnia levels of 3.38% of the included personnel were above moderate. The average score of male personnel (3.65) was obviously lower than that of female personnel (3.71). Besides, the average scale score of married personnel (3.78) was significantly higher than that of unmarried personnel (3.65). The average scale scores of personnel with bachelor's (3.66) and master's degrees (3.62) were obviously lower than those of personnel with junior college (3.77) and technical secondary school (3.75) diplomas. The average scale score of personnel with over 5-year work experience (3.68) was significantly lower than that of personnel working for less than five years (3.72). The average scale score of personnel with experience in responding to public emergencies (3.65) was obviously lower than that of personnel without related experience (3.74). The differences all showed statistical meaning (P < 0.05). The results of this research revealed that the accuracy of the improved RFC algorithm was remarkably higher than that of the SVM and BPNN algorithms. Furthermore, many nursing personnel suffered from mental diseases at different levels with the impact of the epidemic. Gender, marital status, education level, and experience in responding to public emergencies were the main factors affecting nursing personnel's mental health.
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Affiliation(s)
- Dan Guo
- Department of Operating Room, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China
| | - Yi Guo
- Department of Haikou Administrative Center Outpatient, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China
| | - YanJi Xing
- Department of Health Medicine, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China
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