firefly algorithm

As energy demand surges due to technological advancements and population growth, optimizing energy supply networks becomes critical. This study presents a novel approach to intelligent energy management in microgrids that incorporates renewable resources and electric vehicle (EV) charging stations. The primary innovation lies in the simultaneous application of the Firefly algorithm and Monte Carlo method to enhance optimization speed and reduce operational costs, a strategy not previously explored in the literature. Despite existing research on microgrid management, significant gaps remain, particularly regarding the integration of EV charging infrastructure without active vehicle participation and the use of fuel cells as energy storage solutions. This paper addresses these gaps by proposing a framework that allows for future consumer integration while minimizing risks associated with operational uncertainties. Key findings indicate that utilizing the Firefly algorithm significantly outperforms traditional Particle Swarm Optimization (PSO) methods in identifying optimal solutions for energy management. The results demonstrate a marked reduction in operational costs over a 24-hour period while ensuring reliability in energy supply. Furthermore, the study establishes a robust foundation for transforming passive distribution systems into active ones, aligning with smart grid concepts.

In this research study, an attempt is made to present a new optimization scheme by combination of the firefly algorithm and artificial bee colony (FA-ABC) to solve mathematical test functions and real-world problems as best as possible. In this regard, the main operators of the two meta-heuristic algorithms are employed and combined to utilize both advantages. The results are compared with those of five prominent well-known approaches on sixteen benchmark functions. Moreover, thermodynamic, economic and environmental modeling of a thermal power plant known as the CGAM problem is represented. The proposed FA-ABC algorithm is used to reduce the total cost and increase the efficiency of the system as shown in the Pareto front diagrams.

Power transformers play an important role in the transmission and distribution of electrical energy. Power transformers increase or decrease the voltage level without changing the power and frequency of alternating current (AC) electricity. Power transformers are divided into oil type and dry type transformers. Both two types have disadvantages of high cost and isolation problems etc. These problems are reduced by the optimization of transformer design parameters. In this study, the most optimal design dimension is determined by using the firefly algorithm, which is a new heuristic approach in calculating the volume of oil type power transformers at least time. At the same time, the performance comparison is made with the most preferred genetic algorithm, which is one of the other intuitive methods, and the advantages of the firefly algorithm are revealed. This work will provide both cost and time benefits for transformer manufacturers.

In this research study, energy, exergy and economic analyses is performed for a combined cycle power plant (CCPP) with a supplementary firing system. The purpose of this analyses is to evaluate the economic feasibility of a CCPP by applying an optimization techniques based on Evolutionary algorithms. Actually, the evolutionary algorithms of Firefly, PSO and NSGA-II are applied to minimize the cost function and to optimally adjust the operating design variables of a CCPP. The input parameters are measured in real case study (i.e., Yazd city, Iran) and they are used to model and optimize the system performance. The cost objective function is formed from several parts: Operating cost, capital cost and exergy destruction cost. In following of optimization procedure, a thermo-economic method is employed to compare the impact of operating parameters from an economic standpoint by COMFAR III (Computer Model for Feasibility Analysis and Reporting) software. The economic analysis consists of determination of NPV, sensitivity analysis and calculation of break-even point. The results showed that the optimization results are economically more feasible than the base case. In addition, among different optimization techniques, Firefly algorithm improves the economic justification of CCPP. At the end, the results of sensitivity analysis show that by decreasing the operation costs, fixed assets and sales revenue by 40%, the IRR increases by 6.7%, 42.8% and decreases by 41.4%, respectively. Furthermore, the lowest sensitivity of IRR is related to operation cost, while the highest sensitivity of IRR is corresponding to variations of fixed assets.

The paper deals with swirling flow (SF) phenomenon which occurs in fluid flow after local obstacle. The phenomenon of SF has been considered in many studies, but here is some different approach to this problem. Based on the theorem of conservation and transfer of energy, a mathematical model of SF was developed in the form of differential equation which defines the velocity profile. During research, special accent was put on the following parameters: swirling parameter, swirling intensity, swirling flux and swirling coefficient. Firefly Algorithm (FA) optimization model, in conjunction with Simulink, was used to get velocity profile vs. time dependence and to verify the developed model. The diagrams of velocity profile were obtained for variation of the initial boundary values of given parameters and conclusions were derived upon mathematical model and simulation of the process.

In the present study, some modifications on the firefly algorithm are presented to improve its performance. The firefly algorithm is a recently developed robust metaheuristic optimization technique which mimics the social behavior of fireflies based on their flashing characteristics. To improve its performance three basic modifications are proposed in the present work. These modifications consist of adding memory, adding newborn fireflies and proposing a new updating formula. To evaluate the applicability of the proposed method, three classical engineering design optimization problems and three sizing optimization of truss structures are solved and results are compared with those available in the literature. It is observed that the proposed method can effectively be used in solution of engineering design optimization problems.