An improved fruit fly optimization algorithm and its application to joint replenishment problems

Abstract

Fruit fly optimization algorithm (FOA) is one of the recent evolutionary computation approaches. This paper presents an effective and improved FOA (IFOA) for optimizing numerical functions and solving joint replenishment problems (JRPs). In the proposed IFOA, a new method of maintaining the population diversity is developed to enhance the exploration ability. Fruit flies with better fitness values use vision to fly toward a new location, and the others fly randomly in initial search space based on swarm collaboration. In addition, a new parameter to avoid the acquisition of local optimal solution is introduced to implement intelligent searching. Random perturbation is added to the updated initial location to jump out of the local optimum. Comparisons are carried out using 18 benchmark functions to verify the performance of the IFOA. Experimental results show that IFOA has better comprehensive performance than the original FOA, differential evolution algorithm, and particle swarm optimization algorithm. The IFOA is also utilized to solve the typical JRPs that have been proven as non-deterministic polynomial hard problems. Comparative examples reveal that the proposed IFOA can find better solutions than the current best algorithm; thus, it is a potential tool for various complex optimization problems.