Particle Swarm Optimization and its applications in agricultural research

Abstract

Particle Swarm Optimization (PSO) is a non-derivative, nature inspired evolutionary optimization algorithm to solve the complex real time problems. It is a robust stochastic optimization technique based on the movement and intelligence of swarms. As like Genetic Algorithm (GA), the PSO also have fitness function. The PSO has advantage of both local and global optima over only local optimization in GA. PSO can be employed to many areas of agriculture namely precision farming, Irrigation scheduling, machinery power optimization, Fertilizer application optimization, Active Ingredient optimization in chemical treatment of plants, parameter optimization of numerical crop simulation models, stock market price determination, cost optimization, optimal control of plant growth etc. As a contextual investigation monthly maximum temperature (oC) of nine districts North Karnataka has been considered to evaluate PSO in optimizing the parameters of Space Time Autoregressive Moving Average (STARMA) model. The proposed STARMA-PSO model outperformed the classical STARMA model in both training and testing data set.