Efficient Economic Dispatch Using Flower Pollination–Based Optimization

This work presents a bi-inspired optimization method, in particular the Flower Pollination Algorithm (FPA), for handling the Economic Dispatch (ED) problem. The algorithm is motivated by the biological process of pollination, especially biotic pollination, in which insects and birds help pollen move from one bloom to another so that blossoming plants can reproduce and survive best. The suggested method uses models of these natural processes to quickly find the best solutions. We examine the effectiveness of FPA on three conventional test systems with 3, 6, and 20 producing units, taking into consideration transmission losses. In addition, a comparison is made between Grey Wolf Optimization (GWO) and Ant Lion Optimization (ALO) methods. The simulation findings show that FPA works better than GWO and ALO on several evaluation criteria, especially when it comes to lower fuel costs and shorter computation times. This shows that it has a lot of promise as an effective way to solve ED problems.