Firefly algorithm

Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Algorithms, has led to a lot of research in the field of finance and accounting. Accordingly, the purpose of this research is to model the Stock price crash risk of listed companies in Tehran Stock Exchange using firefly algorithm and compare the results with multivariate regression as a traditional method. Of the companies listed on the stock exchange, 101 companies have been selected as samples. Initially, 19 independent variables were introduced into the model as input property of the particle accumulation algorithm, which was considered as a feature selection method. Finally, in each of the different criteria for calculating the risk Stock price crash risk, some optimal variables were selected, then using firefly algorithm and multivariate regression, the stock price crash risk was  predicted  and results were compared. To quantify the Stock price crash risk, three criteria for negative skewness, high fluctuations and maximum sigma have been used. Two methods of MSE and MAE have been used to compare the methods. The results show that the ability of meta-meta-heuristic methods to predict the risk Stock price crash risk is not  generally higher than the traditional method of multivariate regression, And the research hypothesis was not approved.

In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.

Software testing is an expensive and time-consuming process. These costs can be significantly reduced using automated methods. Recently, many researchers have focused on automating this process using search algorithms. Many different methods have been proposed, all of which using a means of heuristic or meta-heuristic search algorithms. The main problem with these methods is that they are usually stuck in local optima. In this paper, to overcome such a problem, we have combined the firefly algorithm (FA) and asexual reproduction optimization algorithm (ARO). FA is a bio-inspired algorithm that is very efficient at exploitation and local searches; however, it suffers from poor exploration and is prone to local optima problem. On the other hand, ARO can be used for escaping from local optima. For this combination, we have inserted ARO into the steps of FA for increasing the population diversity. We have utilized this combination for automatic test case generation with the aim of covering all finite paths of the control flow graph. To evaluate the performance of the proposed method, we have utilized it for generating test cases for a number of programs. Results have indicated that, while giving similar results in terms of the test coverage, the proposed method is significantly better than the existing state of the art algorithms in terms of the number of fitness evaluations. Compared algorithms are FA, ARO, traditional genetic algorithm (TGA), adaptive genetic algorithm (AGA), adaptive particle swarm optimization (APSO), hybrid genetic tabu search algorithm (HGATS), random search (RS), differential evolution (DE), and hybrid cuckoo search and genetic algorithm (CSGA).