A new type bionic global optimization: Construction and application of modified fruit fly optimization algorithm

Abstract

Fruit fly optimization algorithm, which is put forward through research on the act of foraging and observing groups of fruit flies, has some merits such as simplified operation, strong robustness, easy to parallel computing, and fast convergence rate; it could solve the bottlenecks of traditional intelligent optimization algorithms on precocity and low convergence speed effectively. Fruit fly optimization algorithm is applied to almost all the numerical optimization problems and is very useful in engineering applications. When the design variable is negative, traditional fruit fly optimization algorithm is not qualified for the extraordinarily slow convergence rate during the late stage of calculation and easy to be trapped in local optimum. Because of the defects of classical fruit fly optimization algorithm, a new coding method of the process of optimization is improved by this article, so the design variables could be searched toward the direction. In addition, a novel bionic global optimization—fruit fly optimization algorithm of learning—is proposed by introducing the concept of “study.” This article tries to apply fruit fly optimization algorithm of learning to compare calculations; therefore, four classical test functions and two engineering problems are performed. It turned out that not only does fruit fly optimization algorithm of learning inherit the advantages of fruit fly optimization algorithm, but has a strong learning ability. The introduction of “study” ability into fruit fly optimization algorithm notably improves the efficiency and capability of optimization; it has characteristics of fast convergence rate and fast speed of approaching the global optimum solutions. Fruit fly optimization algorithm of learning has the ability to solve practical problems, and its engineering prospect is promising.