Fruit Fly Optimization Algorithm Based on Single-Gene Mutation for High-Dimensional Unconstrained Optimization Problems

Xiao-Dong Guo,Xue-Liang Zhang,Li-Fang Wang,S A Edalatpanah

doi:10.1155/2020/9676279

Abstract

The fruit fly optimization (FFO) algorithm is a new swarm intelligence optimization algorithm. In this study, an adaptive FFO algorithm based on single-gene mutation, named AFFOSM, is designed to aim at inefficiency under all-gene mutation mode when solving the high-dimensional optimization problems. The use of a few adaptive strategies is core to the AFFOSM algorithm, including any given population size, mutation modes chosen by a predefined probability, and variation extents changed with the optimization progress. At first, an offspring individual is reproduced from historical best fruit fly individual, namely, elite reproduction mechanism. And then either uniform mutation or Gauss mutation happens by a predefined probability in a randomly selected gene. Variation extent is dynamically changed with the optimization progress. The simulation results show that AFFOSM algorithm has a better accuracy of convergence and capability of global search than the ESSMER algorithm and several improved versions of the FFO algorithm.

Highlights

In the recent years, swarm intelligence has become a research focus of optimization design field because of its special advantages, such as simple to operate, quick convergence rate, and powerful ability of global search
IFFO algorithm is presented in which a control parameter σ is used to tune self-adaptively the search scope in a random direction of current swarm location, and offspring individuals are generated in the single-gene uniform mutation mode [28]: σ⟵σmax exp􏼠log􏼠σ min 􏼡 σmax
To verify the proposed AFFOSM algorithm, a total of 6 benchmark problems with different characteristics are listed in Table 1 where n denotes the dimensionality of the functions and f(x∗) is the global optimal

Summary

Introduction

Swarm intelligence has become a research focus of optimization design field because of its special advantages, such as simple to operate, quick convergence rate, and powerful ability of global search. Fruit flies acquire chemical information in their environment through smell and taste receptors on the surface of their bodies and regulate behaviors, such as foraging, aggregation, mating, and spawning In these processes, olfactory plays an important role over long distances and shorter ranges. A series of studies show that some unreasonable algorithmic design makes FFO algorithm ill-equipped to jump out of local extremum and to handle complex, high-dimensional, and nonlinear problems With this as the starting point of the paper, a small population, adaptive and improved version of the FFO algorithm, named AFFOSM, is developed based on the single-gene mutation mode, in which the only one gene of an offspring is different from the elite individual.

Related Work

FFO: From All-Gene to Single-Gene Mutation

AFFOSM Algorithm

Test Functions and Results Analysis

Conclusion and Future Work