جستجوی مقالات مرتبط با کلیدواژه « Meta-heuristic algorithm » در نشریات گروه « ریاضی »

Analyzing and examining the price trend of an asset is a fundamental step in managing investment risk on that asset. Therefore, in markets, predicting the price trend of an asset is of special interest to traders and even plays a crucial role in a country's monetary policies. Based on this, in this paper, we will try to use the concept of fractal interpolation to predict the price trend of gold, given its price fluctuations and greater importance compared to other metals in markets. By analyzing the gold’s price trend using time series data with a fractal structure, we aim to determine the pattern of price trend to predict the price trend of gold ounces. Such an approach can provide the necessary tool to help investment decision-making in different time periods (short-term, medium-term, and possibly long-term). To achieve this, we first identify the presence of long-term memory in gold's price trend using the Hurst exponent. After confirming stability, we generate fractal data by calling the fractal interpolation algorithm and then predict the behavior of the corresponding time series data using a neural network algorithm based on fractal data. Finally, we compare the results obtained from calling the algorithms present in the literature on gold data.

 In order to solve the problem of assigning teachers to schools, in the form of a mathematical model using an innovative approach based on Constructive Genetic Algorithm (CGA), we seek to increase teachers' satisfaction.

 In this paper, using the Constructive Genetic Algorithm (CGA ), in a reasonable time, in addition to the appropriate allocation of teachers to schools, also increases their satisfaction. This type of algorithm includes several new features such as population consisting of schema, recombination between schema, dynamic population size, mutation in chromosomes, and possibility of using schema discoveries or chromosome representation. Schemas, unlike chromosomes, do not contain all the information about the problem data. By combining schemas, new schemas or chromosomes are created. New schemas are evaluated based on a predetermined assessment test, and if they are valid, they have the conditions to survive and are added to the population. Unlike other evolutionary algorithms in which the evaluation of individuals is defined on the basis of proportionality function, in CGA, this process involves the use of two functions, f and g.

 The proposed algorithm is superior to the traditional method in terms of time, execution time and efficiency as well as flexibility of the problem. This algorithm selects the solution with the highest number of attributes as the final solution among several optimal solutions. One of the advantages of the proposed algorithm is its generalizability and flexibility, which can easily produce suitable various solutions by changing the existing constraints on the problem.

Originality/Value: 

The proposed algorithm for assigning teachers to schools for one academic year was implemented for the first time on the case study of boys' high schools in Torbat-e-Jam city.

This research proposes a two-phase algorithm whose main idea is based on meta heuristic algorithms, Big Bang and Black Hole. In the first phase of this algorithm, the artificial ants scan the reticulated rectangular region in parallel directions. The best points in the ant’s navigations are used as starting points for the second stage of this algorithm. Big Bang and Black Hole algorithms, as an exploitation phase, try to investigate more accurate answers in the neighborhood of the starting points by reducing the neighborhood radius. Numerical examples confirm that this algorithm is capable to achieve an optimal solution with the desired accuracy and low computational costs.