A Self-Adaptive Modified Fruit Fly Optimization Algorithm
作者单位：School of Automation Science and Control EngineeringSouth China University of Technology Engineering Research Center for Precision Electronic Manufacturing Equipment of Ministry of EducationSouth China University of Technology School of Software EngineeringSouth China University of Technology
会议名称：《第36届中国控制会议》
会议届次：36
主办单位：Dalian University of Technology;Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
会议日期：2017年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the Fundamental Research Funds for the Central Universities under Grant No.2015zz100 supported by the Science and Technology Planning Project of Guangdong Province under Grant No.2014A010104004 supported by Guangzhou Science and Technology Program(Key Laboratory Project)under Grant No.15180007 supported by Science and Technology Planning Project of Guangzhou under Grant No.201707010437
关 键 词：Swarm intelligence optimization Fruit fly optimization algorithm Numerical optimization
摘 要：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.