metaheuristic algorithm
در نشریات گروه ریاضی-
This research will identify the best financial ratios and the best method for forecasting the probability of fraud in the financial statements of approved companies, taking into account the financial significance of decision-making as well as the rise in fraud statistics and its detrimental effects. The statistical sample consisted of 180 companies listed on the stock exchange in Tehran from 2014 to 2021 (532 years of companies -years suspected of fraud and 908 years of non-fraudulent companies). First, by looking at the theoretical underpinnings, 96 financial ratios were extracted, k-nearest neighbor and the Bayesian network, support vector machine, and combined method (bagging) were used to predict fraud in financial statements. The findings reveal that, in general, the methods don't meet the evaluation standards. The gray wolf optimization algorithm, which has an accuracy of 70.60% and a proportionality function value of 0.2940, was thus used to reduce the ratios in order to improve performance. After 31 iterations, 9 appropriate financial ratios were obtained. The effectiveness of the proposed fraud prevention strategies was then assessed again using the extracted financial ratios. The results show that after lowering the financial ratios, all of the proposed methods perform better. The accuracy and efficiency of the proposed methods are respectively 79.25% and 81.70% in the combined method (begging), support vector machine 75.83% and 80.30%, Bayesian network 72.01% and 74.60%, and k- nearest neighbor 74.55%. % and 75.60%, which shows the higher accuracy and efficiency of the combined method (begging) compared to other methods.
Keywords: Metaheuristic Algorithm, Data Mining, Financial Ratios, Classification Algorithms, Fraud Risk Detection -
International Journal Of Nonlinear Analysis And Applications, Volume:16 Issue: 2, Feb 2025, PP 209 -217
Many researchers proved that hybrid models have better results in comparison with independent models. A combination of different methods could enhance the accuracy of time series prediction. Hence, this research used the hybrid of three methods of chaos theory, multi-layer perceptron and metaheuristic algorithm to increase the power of the model forecasting. Artificial neural networks have properly considered complex nonlinear relations and are good comprehensive approximators. Multi-objective evolutionary algorithms such as multi-objective particle swarm optimization are good at solving multi-objective optimization issues. This algorithm organized the combination of parent and children populations by elitist strategy, decreased the messy comparing factors to improve the solution variety and avoided to use of niche factors. Chaos theory controls the complexities of stochastic systems. So, this research offers Tehran Stock Exchange Index (TSEI) prediction by a hybrid model of chaos theory, multi-layer perceptron and metaheuristic algorithm. The results show that in perceptron-based mode, RMSE measures are gradually increased in all intervals. The continuous decrease of RMSE shows that the perceptron-based model could show consistency with the whole data flow. This matter could offer a better learning and consistency process by perceptron-based models to predict stock prices, as this type of learning could apply more experiences for forecasting future behaviour in order to change the system content.
Keywords: Financial Timeseries, Chaos Theory, Multi-Layer Perceptron, Metaheuristic Algorithm -
International Journal Of Nonlinear Analysis And Applications, Volume:16 Issue: 2, Feb 2025, PP 325 -342The convergence issues and getting trapped in local optimal points are two of the major concerns in the field of optimization. For this purpose, improving the standard algorithms to reach better performance in facing complex optimization problems is considered as one of the main challenges in the field of optimization. In this paper, the performance improvement of metaheuristic algorithms is considered while the applicability of the improved and standard algorithms is evaluated through the weight optimization problem of truss structures. For this purpose, the recently proposed Coot optimization algorithm is utilized as the main algorithm which is inspired by different movement types of Coot birds in the water in order to reach food supplies. Regarding the fact that the standard Coot algorithm utilizes random movement in the main search loop, a new improving methodology is utilized in this paper by replacing these random movements with Levy flight as a stochastic procedure with step length defined by levy distribution. The performance of the standard and improved Coot optimization algorithms is investigated in dealing with the problem of optimizing the shape and size of truss structures. Based on the best and statistical results, it is concluded that the improved Coot algorithm is capable of providing better results that the standard Coot algorithm while the capability of the improving methods in increasing the overall performance of the standard algorithm is demonstrated.Keywords: Coot Optimization Algorithm, Truss Structure, Metaheuristic Algorithm, Levy Flight, Optimization
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International Journal Of Nonlinear Analysis And Applications, Volume:14 Issue: 9, Sep 2023, PP 251 -262The main purpose of this study is to predict saffron’s binding efficiency using the meta-heuristic algorithm. This collection of information is a documentary research library and the result is quantitative research. The time period from 2018 to 2021 was 5 years and the frequency of daily frequencies of the Ministry of Agricultural Jihad and Customs of Iran were collected from the Iran Mercantile Exchange (JPI). The meta-heuristic algorithm consisting of a combination of birds, bats, and cuckoos was designed. The proposed methods were modelled by coding in a MATLAB environment using normal data. The results of the computational analysis show that all models were approved; And the artificial neural network shows that price fluctuations, cash price, the volume of transactions and liquidity are of the most importance, respectively, on the yield of saffron contracts.Keywords: Saffron Binding Efficiencies, metaheuristic algorithm, Bird Algorithm, BAT Algorithm, Cuckoo algorithm
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Iranian Journal of Numerical Analysis and Optimization, Volume:12 Issue: 1, Winter and Spring 2022, PP 173 -186
As known, outliers and multicollinearity in the data set are among the important diffculties in regression models, which badly affect the leastsquares estimators. Under multicollinearity and outliers’ existence in the data set, the prediction performance of the least-squares regression method is decreased dramatically. Here, proposing an approximation for the condition number, we suggest a nonlinear mixed-integer programming model to simultaneously control inappropriate effects of the mentioned problems. The model can be effectively solved by popular metaheuristic algorithms. To shed light on importance of our optimization approach, we make some numerical experiments on a classic real data set as well as a simulated data set.
Keywords: Condition number, linear regression, Penalty method, Metaheuristic algorithm, Nonlinear mixed-integer programming -
International Journal Of Nonlinear Analysis And Applications, Volume:12 Issue: 1, Winter-Spring 2021, PP 1397 -1410
Metaheuristic algorithms are effective ways to solve optimization problems and use existing phenomena in nature to solve these problems. Due to the independence of metaheuristic algorithms from the gradient information, the objective function can be used to solve large-scale problems by optimization solutions. The organisms’ behavior in nature in their interaction with each other is one of the optimization methods that are modeled as swarm-based algorithms. Swarm-based algorithms are a set of metaheuristic algorithms which are modeled based on group behavior of their organisms and social interactions. The behavior of wildebeests in nature is considered as a swarm-based algorithm for survival because it can be seen that these organisms migrate in groups and try to survive for themselves and their own herd. In this paper, a new metaheuristic algorithm (WOA) based on migratory and displacement behavior of wildebeests is presented of solving optimization problems. In this algorithm, problem solutions are defined as wildebeest herds that search the problem space for appropriate habitat. The results of the implementation of a set of benchmark functions for solving optimization problems such as the Wildebeest Optimization Algorithm, Whale Optimization Algorithm, BAT, Firefly and Particle Swarm Optimization (PSO) algorithms show that the proposed algorithm is less error rate to find global optimum and also caught up rate in the local optimum is less than the methods.
Keywords: Wildebeest optimization algorithm, Swarm-Based algorithms, Optimization problems, Metaheuristic algorithm -
International Journal Of Nonlinear Analysis And Applications, Volume:11 Issue: 1, Winter-Spring 2020, PP 301 -319Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled environments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable feature extraction procedure. In the second step, different metaheuristic methods are merged with CNN so that its best structure is found automatically. Finally, in the last step the decision is made by employing proper multi-class support vector machine (SVM). In this fashion a fully automated system is developed that is self-tuned and do not need manual adjustments. Simulation results demonstrate efficacy of the proposed method.Keywords: Face recognition, Convolutional Neural Network, Support Vector Machine, Multi-Class Classification, metaheuristic algorithm
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International Journal Of Nonlinear Analysis And Applications, Volume:9 Issue: 2, Summer-Autumn 2018, PP 231 -239Optimization of the product portfolio has been recognized as a critical problem in industry, management, economy and so on. It aims at the selection of an optimal mix of the products to offer in the target market. As a probability function, reliability is an essential objective of the problem which linear models often fail to evaluate it. Here, we develop a multiobjective integer nonlinear constraint model for the problem. Our model provides opportunities to consider the knowledge transferring cost and the environmental effects, as nowadays important concerns of the world, in addition to the classical factors operational cost and reliability. Also, the model is designed in a way to simultaneously optimize the input materials and the products. Although being to some extent complicated, the model can be efficiently solved by the metaheuristic algorithms. Finally, we make some numerical experiments on a simulated test problem.Keywords: Product portfolio optimization, nonlinear programming, multiobjective optimization, reliability, metaheuristic algorithm
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