AL-Rafidain Journal of Computer Sciences and Mathematics

The Flower Pollination Algorithm (FPA) is a well-known method for solving optimization problems using swarm intelligence. But, like other swarm-based algorithms, FPA has some problems, such as being prone to local optima and being slow to converge in high-dimensional global optimization problems. The Modified Flower Pollination Algorithm (MFPA) was made to deal with these problems. MFPA tries to improve exploitation capabilities and accuracy of solutions by using a balanced approach to exploitation and exploration. It uses the spiral renewal mechanism from the Moth Flame Optimization (MFO) algorithm to avoid local optimums. These strategies help FPA work better when they are used together. Benchmark test functions, including those from the CEC 2017 test suite, were tested experimentally. When MFPA\'s performance was compared to that of other swarm intelligence algorithms and more complex methods, the results were very positive and encouraging.