firefly algorithm

By anticipating financial turmoil, it is possible to take the necessary precautions before financial distress occurs by managers and investors. This study compares two algorithms for predicting bankruptcy using an Artificial Neural Network (ANN) and Neural network optimized metaheuristic Firefly Algorithm (FA). To run the test, initial values are first set for the network weights and biases. Then, during optimization, the FA algorithm generates a population of different weights and biases. The conversion function used in the output layer is linear, and a non-linear sigmoid function is selected for the middle layer. To conduct this research, the data of 79 companies listed on TSE from 2012 to 2015 were collected and analyzed statistically by backpropagation neural network and FA algorithms. The results show that FA, compared to ANN predicted the companies’ bankruptcy much better. Also, the FA Algorithm maintains a good correlation between bankrupt and non-bankrupt companies, just like real data.

The current research is based on the explanation of the optimal model for predicting the performance of companies using data mining techniques. The method of this research is of the applied type, in terms of the way of doing the work, it is of the descriptive-causal research type, and in terms of the time dimension, it is of the post-event research type. In the first step, by referring to databases such as theses, articles and similar researches, the required literature was collected in order to write the theoretical foundations and background of the research. In the following, the information of the investigated companies selected as a statistical sample, whose information is available in the form of data banks on CDs and is under the supervision and review of the responsible institutions, was audited by referring to the financial statements and New implementation software was compiled. The mentioned information included the financial data of the companies admitted to the Tehran Stock Exchange for a period of 10 years from the beginning of 2011 to the end of 2014. Finally, the findings showed that the firefly algorithm, genetic algorithm and evolutionary algorithm were effective in predicting the ratio of QTobins, return on equity and return on equity, and the firefly algorithm had a higher power to predict the ratio of QTobins, return on equity and return. has shares

In this study, a method for predicting engine torque and emissions considering fuel consumption and engine speed parameters is presented. An adaptive neuro-fuzzy inference system (ANFIS) optimized with the Firefly algorithm is used. This strategy uses the global optimization capabilities of the Firefly algorithm, an algorithm inspired by biological phenomena, in combination with the ability of ANFIS to describe complicated non-linear relationships between inputs and outputs. The ANFIS system was trained on a dataset containing various engine operating conditions, with the Firefly algorithm fine-tuning the model parameters to ensure optimal effectiveness. The input parameters of the model consisted of fuel quantity and engine speed, while engine torque and nitrogen oxide emissions formed the output parameters. The results obtained showed high accuracy in predicting engine torque and emissions, confirming the effectiveness of the Firefly-optimized ANFIS model. This model makes an important contribution to engine performance monitoring and emissions management. It provides a powerful tool for real-time regulation and has the potential to improve fuel efficiency while reducing environmental impact. Future research efforts should extend the applicability of this model to a wider range of engine shapes and operating conditions.

In this paper we consider 1//∑nj=1(Ej+Tj+Cj+Uj+Vj) problem, the discussed problem is called a Multi objectives Function (MOF) problem, As objective is to find a sequence that minimizes the multiple objective functions, the sum earliness, the tardiness, the completion time, the number of late jobs and the late work. The NP-hard nature of the problem, hence the existence of a polynomial time method for finding an optimal solution is unlikely. This complexity result leads us to use an enumeration solution approach. In this paper we propose a branch and bound method to solve this problem. Also, we use fast local search methods yielding near optimal solution. We report on computation experience; the performances of exact and local search methods are tested on large class of test problems.

In this paper, we have investigated a new spectral Quasi-Newton (QN) algorithm. New search directions of the proposed algorithm increase its stability and increase the arrival to the optimum solution with a lowest cost value and our numerical applications on the standard Firefly Algorithm (FA)and the new proposed algorithm are powerful as in meta-heuristic field. Our new proposed algorithm has quite common uses in several sciences and engineering problems. Finally, our numerical results show that the proposed technique is the best and its accuracy higher than the accuracy of the standard FA. These numerical results are compared using statistical analysis to evaluate the efficiency and the robustness of new proposed algorithm.