An improved fruit fly optimization algorithm (FOA) is proposed for optimizing continuous function problems and handling joint replenishment problems (JRPs). In the proposed FOA, a level probability policy and a new mutation parameter are developed to balance the population diversity and stability. Twenty-nine complex continuous benchmark functions are used to verify the performance of the FOA with level probability policy (LP–FOA). Numerical results show that the proposed LP–FOA outperforms two state-of-the-art variants of FOA, the differential evolution algorithm and particle swarm optimization algorithm, in terms of the median and standard deviations. The LP–FOA with a new and delicate coding style is also used to handle the classic JRP, which is a complex combinatorial optimization problem. Experimental results reveal that LP–FOA is better than the current best intelligent algorithm, particularly for large-scale JRPs. Thus, the proposed LP–FOA is a potential tool for various complex optimization problems. 
An effective and efficient fruit fly optimization algorithm with level probability policy and its applications