RWFOA: a random walk-based fruit fly optimization algorithm

As one kind of the classic swarm intelligence (SI) algorithms, fruit fly optimization algorithm has been widely used in many aspects, such as multiple objective optimization and service computing. However, due to the limitation that introduced by the initial population location, its global optimization ability is limited. Therefore, we propose a random walk-based fruit fly optimization algorithm, namely RWFOA, to enhance its global optimization ability. RWFOA employs the random walk mechanism to dynamically adjust the position of the fruit fly population, which can reduce the impact of the initial population location and thus enhance the global optimization ability. We conduct a comprehensive experimental evaluation of RWFOA by comparing it with three representative algorithms, i.e., the OFOA, CFOA and IFFO, over 29 widely used benchmark functions published in CEC 2015. Experimental results demonstrate that RWFOA can find better solutions in most of the selected benchmark functions.