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

Abstract

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