Comparison of Flower Pollination Algorithm and Particle Swarm Optimization for Structural Weight Minimization of RC Beams with Carbon Fiber Reinforced Polymer (CFRP)

In order to generate the best namely most appropriate design, different methodologies can be preferred. In nowadays, especially, metaheuristic algorithms, which have been proposed by inspiring various metaphors such as genetic, abilities of animals or natural behaviors of plants etc., can be useful, suitable and effective to provide the target results for any optimization problem. For this reason, in the current study, to design the most appropriate structural model intended for reinforced concrete (RC) beam structures supported with carbon fiber reinforced polymer (CFRP), two different population-based metaheuristic methodologies as flower pollination algorithm (FPA), and a classical one as particle swarm optimization (PSO) were operated. By this respect, several independent optimization cases were applied to compare the success and efficiency of algorithms in terms of minimization of total weight besides optimization of design parameters as number and width of CFRP plates. According to results, it can be said that FPA is the most powerful algorithm for determining the best parameters of structural models with the aim of providing minimum weight by considering the error metrics of objective function, and the lowness of weight values.