Meta-Heuristic Algorithms

The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to determine the winning algorithm, the performance indexes, Set coverage and the Mean Ideal Distance were used. Finally, the active shares of 50 Tehran Stock Exchange com-panies were analysed (2007-2016). The results indicate that the SPEA-II algo-rithm can perform optimization and achieve a better performance than the NSGA-II. This algorithm could achieve better outcomes than the winning strategy during the selection period based on the risk-taking strategies in different months

The purpose of this article is to present the evolutionary model of loan sales using collective intelligence and meta-heuristic algorithms (bird flight algorithm). In terms of method, this research is in the category of quantitative research, and in terms of purpose, it is included in the category of applied research. The statistical population includes all active companies admitted to the Tehran Stock Exchange. This research has been investigated between 2011 and 2019 for active companies admitted to the Tehran Stock Exchange. The method of data collection is through library study and financial data of companies admitted to the stock exchange by referring to the financial statements and explanatory notes with the financial statements, and it has also been compiled using the Rahavard Novin software. Also, with the help of EViews 9 and MATLAB software, he presented a borrowing sales model, and in the next step, with the help of MATLAB software and the flight of bird's algorithm, he presented an evolutionary model of borrowing sales, in the end, by comparing the step-by-step regression model and the borrowing sales model. The findings showed that the borrowing sales model with the help of the bird flight algorithm has a higher efficiency.

Supply chain network design entails strategic decisions that critically shape the operational framework of a supply chain. The purpose of this study is to develop and optimize a green dental tourism supply chain network using metaheuristic algorithms. This applied research combines field surveys and library-based methods for data collection. Empirical data were obtained from dental clinics and three- and four-star hotels in Qom Province, forming the basis for model implementation. The proposed network integrates environmental considerations and seeks to minimize total supply chain cost. A multi-objective optimization model is formulated and solved using two algorithms: NRGA (Non‑dominated Ranked Genetic Algorithm) and NSGA‑II (Non‑dominated Sorting Genetic Algorithm II). The comparative analysis demonstrates that NSGA‑II achieves more efficient solutions and superior ranking of Pareto‑optimal fronts across the model’s multiple objectives. This study advances the design and optimization of sustainable dental tourism supply chains, providing practical insights for policymakers and industry stakeholders aiming to promote environmentally responsible and economically viable tourism services.

Background: Network security and penetration pose a significant challenge in the extensive IoT research of recent years. System security and user privacy demand security solutions that are carefully planned and diligently maintained. Aims: This paper introduces a novel three-stage hybrid IDS, IoT-APOSCA, leveraging machine learning and meta-heuristics for attack detection; stages include pre-processing, feature selection, and attack detection. The pre-processing steps are: cleaning, visualization, feature engineering, and vectorization. Methodology: Networks use Intrusion Detection Systems (IDSs) to monitor and detect malicious activities as a key security feature. The Arctic Puffin Optimization (APO) and Sine-Cosine Algorithm (SCA) are used in the feature selection stage, while a changed Random Neural Network (RNN) is employed in the attack detection stage. Results: The proposed technique is assessed using the DS2OS dataset, and the outcomes show that the approach, integrating multiple learning models, led to an accuracy enhancement to 99.66%. Also, the values Recall and False Alarm Rate obtained are equal to 0.9926 and 0.003, respectively. Conclusion: Intrusion detection system efficacy is directly tied to the quality of its classification method. Enhanced neural network performance is achievable through adjustments to parameters, such as network weights.