A hybrid search optimization technique based on evolutionary learning in plants

In this article, we have proposed a search optimization algorithm based on the natural intelligence of biological plants, which has been modelled using a three tier architecture comprising Plant Growth Simulation Algorithm (PGSA), Evolutionary Learning and Reinforcement Learning in each tier respectively. The method combines the heuristic based PGSA along with Evolutionary Learning with an underlying Reinforcement Learning technique where natural selection is used as a feedback. This enables us to achieve a highly optimized algorithm for search that simulates the evolutionary techniques in nature. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run times of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.