A new biologically inspired optimization algorithm

This paper proposes a new biologically inspired algorithm for optimization. The algorithm, called the Paddy Field Algorithm (PFA) operates by initially scattering seeds at random in the parameter space. The number of seeds of each plant depend on the function value such that a plant closer to the optimum solution produces the most seeds. Out of these, depending on the number of neighbors of the plant, only a percentage will become viable due to pollination. In order to prevent getting stuck in local minima, the seeds of each plant are dispersed. This algorithm is tested on four sample functions along side other conventional algorithms. The effect of various parameters on the performance of the algorithm is also investigated. Its performance is also tested with a hybrid algorithm. The results show that the algorithm performs well.