firefly algorithm

Guyed masts are slender, tall structures widely used to support various types of antennas in the field of telecommunications. These structures consist of a tall vertical mast laterally supported at several levels along its height by sets of inclined pre-tensioned cables. It is well known that natural frequencies are fundamental parameters that affect the dynamic behavior of structures. Optimizing natural frequencies of Guyed masts is an important issue in structural engineering. The main objective of this study presented in this paper is to obtain a design that will maximize the frequency of the Guyed masts. Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, three population-based meta-heuristic algorithms are employed for frequency optimization of Guyed masts. These algorithms consist of the Genetic Algorithm (GA), Firefly Algorithm (FA) and Particle Swarm Optimization Algorithm (PSO) aims to maximize the natural frequencies of Guyed masts while satisfying their constraints. The study on optimizing the position of the cable anchorages along the pole of a guyed tower using algorithms demonstrates that the optimal values for these angles are approximately 35 degrees without pretension forces in the guys. Additionally, the optimum cable angle is 75 degrees when subjected to prestressed guy cables.

The goal of any structural design process is to produce a safe design that meets all the design codes requirements, while trying to minimize the cost of the design. In Civil Engineering Structures, majorly cost is the objective of optimization of the structure. In most cases, the material cost is included in objective function. In some cases, construction cost is also included in objective function. The objective of the optimization is to minimize the cost of the Structures while satisfying strength and serviceability constraints. In order to investigate the efficiency and accuracy of the modern optimization methods in comparison with classical methods, an elevated INTZE tank is primarily designed based on the recommendations of Indian code and then optimized using Genetic Algorithm (GA), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO). objective function is the material cost of the tank which is the function of the design variables. The total material cost of the tank can be expressed as the sum of the cost of concrete, reinforcement and cost of formwork. Design constraints of the problem also include the stresses of the structure members and the limitations of the volume and geometric dimensions of the water tank. All constraints were satisfied and the stresses and stability constraints were in acceptable ranges.

Roadheader machine is one of those machines that have high drilling capability in rocks with low to medium strength. Hence they are widely used in underground excavations. Estimating the performance of roadheader machine is one of the main and important issues in estimating the approximate project completion time as well as project costs. Therefore, the purpose of this paper is to propose intelligent forecasting models for estimating the performance of roadheader machine by two intelligent methods (the firefly algorithm (FA) and the Teaching-learning based optimization algorithm (TLBO)) and using a database (a case study). Is. In these models, the Schmidt hammer rebound values and the rock quality degree (RQD) are used as input parameters and the cutting rate of the roadheader is used as the output parameter. Finally, to evaluate the accuracy of the models and modeling, the indices of square correlation coefficient (R2), variance account for (VAF), root mean square error (RMSE) and mean square error (MSE) have been used. The results indicated that the two models have strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

In structural engineering, it is vital to design structures by providing pre-defined performance objectives economically. Reliability-based design optimization (RBDO) is looking for the best balance between safety and cost, by considering uncertainties in structural parameters and loading conditions. In recent decades, several methods have been used to solve RBDO problem. However, most of these methods suffer from high computational cost of solving the problem as a big challenge.In this paper, weighted simulation method, one of the newest and most effective methods for simulation-based reliability assessment methods, has been used for reliability analysis and evaluation of probabilistic constraints. The advantages of this method are a significant reduction in the computational cost and the proper accuracy of the final results.For minimizing the objective function, which is the weight of structure in this paper, Firefly algorithm (FA) is utilized. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in way that it is associated with the objective function to be minimized. The numerical example results indicate that using the proposed method leads to the optimum design solutions better than other methods in the literature without violating reliability constraints.

Leakage in water distribution system is a crucial issue because of problems such as water loss, water pollution, and land subsidence. Current leakage detection methods are usually costly and time-consuming, therefore new methods have been developed based on water networks simulation. In this paper, a new method for identifying the location and amount of leakage in water distribution system based on a two-step Algorithm is introduced. The first (Stepped Algorithm) and second (Firefly Algorithm) steps for determination of leak location and amount of leakage are respectively used which is applicable for even two simultaneous leaks. The proposed method is based on the comparison of the network hydraulic simulation results and some field network data (pressure or flow or their combination). The obtained results for six examples in a looped-water network are presented. The results show that the proposed algorithm is able to locate either one leak or two simultaneous leaks and their leakage values with less than 8% error. The proposed method developed in this paper can be utilized by operators of water supply networks for identifying the position and amount of leaks.

Construction cost reduction for design engineers has always been important. However, reducing the cost of building should not reduce the quality of the structures. In other words, by providing an optimized design, the maximum capacity of the materials is used. In this study, to achieve these objectives, meta-heuristic algorithms perform the optimization. To optimize RC shear walls, meta-heuristic hybrid algorithm called firefly and particle swarm optimization (FA-PSO) is used. The results of this study confirm the efficiency and accuracy of the algorithm to achieve the global optimum. In this study, the optimal design of reinforced concrete shear walls is done by boundary element and seismic conditions. Cost of reinforced concrete shear wall includes the cost of materials (steel and concrete) and cost formwork, introduced as a goal function. Shear wall design criteria and constraints according to ACI Regulations are written. So the optimal point at the lowest cost is achieved when all constraints are met. In addition, the objective function is written in a manner that requires no database optimization done continuously. This collection is presented in the form of a program. This program is a graphical interface, allowing the user to enter information related to the shear walls and outputs designed to easily observe. Two methods are provided to optimize the shear walls in this research. The first method is continuous optimization, and the second method is the continuous optimization while taking into account the dimensions of discrete variables to include construction conditions. One of the main advantages of this method compared to discrete is that there is no need to create the database (Section database). This causes the application to review all design modes. The results show that plan is the most economical as possible due to the continuous optimization, as opposed to discrete method, which depends on a variety of databases and problem types.

In road design, pavement has detailed design and its cost is inevitable. But, in the infrastructure, the costs can be considerably reduced by optimizing the position of vertical alignment. One of the most expensive parts of the infrastructure is earthwork costs, which is highly dependent on the project vertical alignment. Therefore, the aim of this study was to evaluate various meta-heuristic methods for proposing a method for vertical-alignment optimization to have the lowest cost of earthwork operations. In this study, first, the objective function and constraints of the problem were formulated and then the distance, height, and length of the vertical curve in every point of vertical intersection (PVI) were considered as decision variables. Initial vertical alignment and position of the PVIs were defined according to the guidelines and demands. The objective function was defined as the sum of absolute difference of the vertical alignment height and ground surface. Meanwhile, the constraints were defined as minimum and maximum longitudinal slope, minimum height of the bridges, avoiding interference of the curves, and minimum length of the vertical curve. For optimization of the problem, Genetic Algorithm (GA), Accelerated Particle Swarm Algorithm (APSA) and Firefly Algorithm (FA) were used in the MATLAB software. For better evaluation of the developed method, three different topographies (plain, rolling, and mountainous) were designed and the original vertical alignment was optimized by each algorithm. In addition to the difference of vertical alignment and ground surface, comparison of the ratio of volume of fill to volume of cut, and earthwork costs, was carried out. Results showed that the highest optimiztion percentage for the three topographies belonged to FA, APSA and GA, respectively. Therefore, application of FA highly reduced the costs of road infrastructure. In addition, the time and replications needed to solve the problem show that the necessary time for optimization and obtaining the optimum solution is ignorable in comparison to the achieved cost saving.

This study considers a scheduling problem of combined arrival-departure aircrafts over multiple runways with a number of a parking spaces constraint that minimizes the total weighted tardiness. This phenomenon is modelled as a parallel machine scheduling problem with unequal ready time, target time and dead line. Additionally, sequence-dependent separation times on each runway are considered to prevent the dangers associated with wake vortex effects. Considering occasional occurrence of such restrictions at airports as well as planning a better deal with such issues, a mixed-integer programming (MIP) model is presented and solved by Lingo software to show its validity. A meta-heuristic algorithm, namely firefly algorithm (FA), is proposed to achieve acceptable results in a reasonable time.The performance of the proposed FA is evaluated through a comprehensive benchmark of instances. Furthermore, the computational results show that the performance of the proposed FA is satisfactory in terms of computational time compared with Lingo. Finally, the conclusion is provided.

Support Vector Machine (SVM) has emerged in recent years as a popular approach for classification of remote sensing data. SVMs don’t require huge training samples and have little possibility of over fitting however; the accuracy of SVM mainly depends on the parameters selection of it. So, one of the significant research problems in SVM is the selection of optimal parameters that can increase the accuracy of this classifier. Regularization constant C and kernel function parameters exert a considerable influence on the accuracy of SVM. In recent years, the development of parameter optimization for SVM is supported by evolutionary algorithms and bio-inspired metaheuristic algorithms such as swarm-based methods. This paper evaluates the potential of one of the swarm-based bio-inspired optimization methods called Firefly Algorithm (FA) for SVM optimization. FA is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Firefly algorithm is