Yazar "Bacanin, Nebojsa" seçeneğine göre listele

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    An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms
    (SpringerLink, 2024) Seyyedabbasi, Amir; Tareq Tareq, Wadhah Zeyad; Bacanin, Nebojsa
    Recently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms.
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    Blood supply chain network design with lateral freight: A robust possibilistic optimization model
    (Pergamon-Elsevier Science Ltd, 2024) Ala, Ali; Simic, Vladimir; Bacanin, Nebojsa; Tirkolaee, Erfan Babaee
    The blood supply chain stands out as a crucial component within a healthcare system, which can significantly improve efficiency and save the health system's costs. This paper presents a multi-objective blood supply chain network design problem that aims to reduce the cost of establishing fixed and temporary facilities, transferring blood products, and the amount of shortage. In order to address the shortfall and boost adaptability, lateral freight across hospitals is suggested due to the uncertainty in supply and demand. A novel robust possibilistic mixed-integer linear programming method is proposed in this work in order to deal with distribution and locational decisions. Two well-known solution approaches of lexicographic and Torabi-Hassini methods are then utilized to treat the multi-objectiveness of the robust possibilistic optimization model. Lateral freight between various blood supply chain demands significantly affects load balancing, declining both delivery time and costs. According to the obtained outcomes, the overall delivery time and total cost decrease by 10% and 15%, respectively. Moreover, it is revealed that the lexicographic approach outperforms the Torabi-Hassini method in this research.
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    Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Jovanovic, Luka; Zivkovic, Miodrag; Bacanin, Nebojsa; Dobrojevic, Milos; Simic, Vladimir; Sadasivuni, Kishor Kumar; Tirkolaee, Erfan Babaee
    This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.
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    Forecasting bitcoin: decomposition aided long short-term memory based time series and its with values
    (Elsevier, 2024) Mizdrakovic, Vule; Kljajic, Maja; Zivkovic, Miodrag; Bacanin, Nebojsa; Jovanovic, Luka
    Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two -layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cuttingedge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.
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    Improving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysis
    (Elsevier, 2023) Todorovic, Mihailo; Stanisic, Nemanja; Zivkovic, Miodrag; Bacanin, Nebojsa; Simic, Vladimir; Tirkolaee, Erfan Babaee
    This study aims to create a machine learning model that can predict opinions in external audits and surpass the benchmark set in a prior study from the literature. This tool could reduce audit risk, which is a crucial task in external audits. Previous studies have shown that it is possible to create models that can predict the audit opinion a company will receive. In these studies, authors used statistics and machine learning models, and both non-financial (e.g. audit lag) and financial data (e.g. financial ratios, or absolute value items available from financial statements) to make predictions. In this study, the performance of the XGBoost model optimized by metaheuristics algorithms is examined and evaluated. This study compares the performance of six different metaheuristic algorithms used to tune the XGBoost model in two separate scenarios. The first scenario represents a realistic client portfolio, where a majority of the clients are known, while the second scenario simulates a new clients-only portfolio, a more difficult scenario where prior information such as audit lag is not available. The study uses a dataset of 12,690 observations of Serbian companies and their audit opinions from 2016 to 2019. The findings indicate an improvement over the benchmark due to a more optimized hyperparameter tuning process and the use of the iterative sine-cosine algorithm for the XGBoost model.
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    Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting
    (Springer, 2024) Pavlov-Kagadejev, Marijana; Jovanovic, Luka; Bacanin, Nebojsa; Deveci, Muhammet; Zivkovic, Miodrag; Tuba, Milan; Strumberger, Ivana
    Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions.
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    Uncovering pathways towards sustainable transportation: investigating factors influencing societal acceptance of end-of-life vehicle management in Indonesia
    (SAGE publications ltd, 2024) Sitinjak, Charli; Simic, Vladimir; Babaee Tirkolaee, Erfan; Bacanin, Nebojsa; Simanullang, Wiyanti Fransisca; Fajar, Rizqon
    Addressing the critical environmental challenge of end-of-life vehicle (ELV) management in Indonesia's transportation industry, this study investigates the complex interplay between societal factors and technical adoption. We use a comprehensive survey and path analysis to investigate the relationships between demographic characteristics (gender, age, income and education) and ELV acceptance, revealing complex preferences and concerns across several population groups. Comparative analyses with previous research reveal gender-specific inequities and age-related problems, emphasizing the importance of customized measures. Our findings indicate that environmental concerns exhibit a significant positive relationship with community acceptance (path coefficient = 0.426, p < 0.001). Moreover, technological familiarity (path coefficient = 0.352, p < 0.001) and infrastructure availability (path coefficient = 0.518, p < 0.001) demonstrate noteworthy positive associations, emphasizing the role of knowledge and accessible infrastructure in promoting acceptance. Conversely, the cost of adoption exhibits a negative relationship with societal acceptance (path coefficient = -0.269, p < 0.001), suggesting potential challenges that must be addressed. Mediation analysis uncovers the mediating roles of information exposure, perceived safety, as well as convenience and accessibility. Total effects analysis validates the collective influence of crucial factors while acknowledging the potential hindrance posed by the cost of adoption. Our findings contribute to inclusive policies and initiatives for sustainable ELV practices, offering insights to address a critical environmental issue in Indonesia. Although acknowledging limitations in scope and methodology, our research advances the discourse on sustainable transportation transitions and guides strategies to promote responsible ELV management in pursuing a greener and more socially equitable future.