Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC)
In order to optimize the proton exchange membrane fuel cell (PEMFC) model parameters, a novel approach based on seeker optimization algorithm (SOA) is proposed. The SOA is based on the concept of simulating human searching behaviors, where the choice of search direction is based on the empirical gradient by evaluating the response to the position changes and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, after evaluated on benchmark function optimization, the SOA is applied to optimal modelling of the PEMFC by using a fuel cell test system in Fuel Cell Application Centre (FAC) at the Temasek Polytechnic, and compared with several state-of-the-art versions of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The simulation results show that the proposed approach is superior to other compared algorithms, and the PEMFC model with optimized parameters by SOA fitted experimental data well. Hence, SOA is an effective and reliable technique for optimizing the parameters of PEMFC model, and can be helpful for system analysis, optimization design and real-time control of the PEMFCs.
seeker optimization algorithm global optimization optimal modelling proton exchange membrane fuel cell human searching behaviors