Calculation of optimal induction heater capacitance based on the smart bacterial foraging algorithm

In designing a parallel resonant induction heating system, selecting a suitable capacitor for its parallel circuit is very important. To properly select this capacitor, several solutions have been proposed such as using Lagrange method, standard bacterial foraging and genetic algorithms. Although some of these methods have been performed well, they have not considered either some restrictions or a multi-objective function in their design. To obtain an optimal value for the capacitor, this paper utilizes a bio-inspired optimization method called smart bacterial foraging algorithm (SBFA). SBFA is modeled based upon the foraging behavior of bacteria including the individual and social intelligences. SBFA uses a multi-objective optimization function including efficiency, power output and difference between power loss and output by taking the voltage and resonant frequency limitations into consideration. In order to demonstrate the efficiency and performance of the proposed approach, this paper simulates it using MATLAB. Then, the simulation results are compared with two existing algorithms: standard bacterial foraging and genetic algorithms. Based upon the results, SBFA is outperformed in comparison with the aforementioned algorithms in selecting the optimum capacitance. Results point to the fact that there can be different resonant frequencies with respect to the different capacitances of the heater as well as that for one of these frequencies the objective function has a minimum value. In addition, findings show a Pareto-Efficient situation for efficiency and power output value in an induction heating system.