A self-adaptive modified fruit fly optimization algorithm
Yingtong Tan, Mei Zhang, Jinhui Zhu, Haiming Liu · 2017
Fruit fly optimization algorithm (FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm (SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.