A Hybridized Flower Pollination Algorithm and Its Application on Microgrid Operations Planning

The meta-heuristic algorithms have been applied to handle various real-world optimization problems because their approach closely resembles natural human thinking and processing relatively quickly. Flowers pollination algorithm (FPA) is one of the advanced algorithms; still, it has suffered from slow convergence insufficient accuracy when dealing with complicated problems. This study suggests hybridizing FPA sine–cosine (call HSFPA) avoid drawbacks for microgrid operations planning global objective function constructed based on power generation distribution system’s relevant economic costs environmental profits. Adapting hop size, diversifying local search, diverging agents as strategies learning SCA are used modify original equation improving HSFPA solutions in terms diversity pollinations, increasing convergence, preventing optimal traps. experimental results compared other literature benchmark test problem evaluate proposed scheme. Compared show that offers outstanding performance competitors testing function. also delivers efficient solving problem.