Maintenance scheduling of power generators is of particular importance for power grids from both economic and reliability aspects. In this paper, the discrete Firefly Algorithm is implemented for maintenance scheduling of generators. This project utilizes the combination of the discrete Firefly Algorithm, as the main tool, and heuristic methods, as the secondary tool, for constructing initial solutions and searching the solution space. The paper investigates a case study consisting of a 32-generator problem formulated as a mixed-integer problem. The performance of the proposed method is compared with those in the literature and its strengths and weaknesses are discussed. The obtained results showed that performance of the proposed Algorithm is not highly affected by its parameters’ values and is capable of providing multiple efficient maintenance schedules in a desirable time.

Evolutionary Algorithms are among the most powerful Algorithms for optimization, Firefly Algorithm (FA) is one of them that inspired by nature. It is an easily implementable, robust, simple and flexible technique. On the other hand, Integration of this Algorithm with other Algorithms, can be improved the performance of FA. Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are suitable and effective for integration with FA. Some method and operation in GSA and PSO can help to FA for fast and smart searching. In one version of the Gravitational Search Algorithm (GSA), selecting the K-best particles with bigger mass, and examining its effect on other masses has a great help for achieving the faster and more accurate in optimal answer. As well as, in Particle Swarm Optimization (PSO), the candidate answers for solving optimization problem, are guided by local best position and global best position to achieving optimal answer. These operators and their combination with the Firefly Algorithm (FA) can improve the performance of the search Algorithm. This paper intends to provide models for improvement Firefly Algorithm using GSA and PSO operation. For this purpose, 5 scenarios are defined and then, their models are simulated using MATLAB software. Finally, by reviewing the results, It is shown that the performance of introduced models are better than the standard Firefly Algorithm.

As a crucial issue in aqua sciences, optimizing dam reservoirs exploitation has been studied with a variety of optimization techniques. One of these methods is using Meta-Heuristic Algorithms such as Firefly and Ants Algorithms. Using the Firefly Algorithm, this study studies the exploitation optimization of Doroudzan reservoir in a 99-month period. The most sensitive parameter in sensitivity analysis of Firefly Algorithm, α, is known as the mutation rate. Selecting its appropriate value by Firefly worms leads to an appropriate solution and increases the efficiency of the Firefly Algorithm dramatically. To determine the efficiency of this Algorithm in optimizing the utilization of the dam reservoir, the obtained results were compared with the results of Continuous Ant System and Ranking Ant System. The findings indicated that FACC Algorithm with objective function rate of 4.196 had a satisfactory performance. ACOrCC and ACOrankCC Algorithms with the values of 17.004 and 26.156 followed it respectively. In addition, FACC Algorithm with a value of 0.959 had the highest reliability coefficient. The results indicated that regarding Chain constraints, all program performances led to feasible solutions; however, ignoring chain constraints, the Continuous Ant System Algorithm was unable to find a feasible solution. Hence, applying these constraints in the main structure of this Algorithm would enhance its efficiency significantly.

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.

In data grid, using reservation is accepted to provide scheduling and service quality.Users need to have an access to the stored data in geographical environment, which can be solved by using replication, and an action taken to reach certainty. As a result, users are directed toward the nearest version to access information. The most important point is to know in which sites and distributed system the produced versions are located. By selecting a suitable place for versions, the versions having performance, efficiency and lower access time are used. In this study, an efficient method is presented to select the best place for those versions created in data grid by using the users’ Firefly Algorithm which is compared with two Algorithms. Results show that Firefly Algorithm has better performance than others.

Introduction: Liver disease is one of the most common and dangerous diseases the early detection of which can be very effective in preventing complications as well as controlling and treating the disease. The purpose of this study was to improve Adaboost Algorithm using Firefly Algorithm for diagnosing liver disease. Method: This is a descriptive-analytic study. The dataset consists of 583 independent records including 10 features of machine learning dataset in the University of California, Irvine. In this study, Adaboost and Firefly Algorithm were combined to increase the effectiveness of liver disease diagnosis. 80% of the data were used for training and 20% for testing. Results: The results highlighted the superiority of the hybrid model of feature selection over the models without feature selection. Of course, the selection of important features affect the performance of the model. The accuracy of the hybrid model considering 5 and all features was 98. 61% and 94. 15%, respectively. Overall, the hybrid model proved more accurate compared with most of the other data mining models. Conclusion: Hybrid model can be used to help physicians identify and classify healthy and unhealthy individuals; it can also be used in medical centers to enhance accuracy and speed, and reduce costs. It cannot be claimed that the hybrid model is the best model; however, it proved more accurarate.

In the last decade, online shopping has played a vital role in the customers' approach to purchase different products, providing convenience to the shops and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. Thus, the recommendation systems are of great importance, especially in the today's hectic world, which searches for the content that would be interesting to an individual. This research proposes a new two-step recommender system based on the demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on the demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into the local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to a much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naï, ve Bayesian Classification. The results obtained are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results obtained show the effectiveness of the K-means/FA/KNN compared with the other extant models.

In this paper, a new and an effective combination of two metaheuristic Algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly Algorithm (FA). Firefly Algorithm is the nature-inspired Algorithm which has its roots in the light intensity attraction process of Firefly in the nature. Differential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful Algorithms but FA Algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each Algorithm deficiencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and findings showed that HFADE is a more preferable and effective method in solving the high-dimensional functions.