A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

Abstract

The Fruit Fly Optimization Algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization Algorithm tends to converge prematurely. In response to this situation, this article proposes a new optimized structure—Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm. The new algorithm uses the evolution matrix in QUasi-Affine TRansformation Evolution algorithm to update the position coordinates of particles. This strategy makes the movement of particles more scientific and the search space broader. In order to prove its effectiveness, we compare Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm with five other mature intelligent algorithms, and test them on 22 different types of benchmark functions. In order to observe the multi-faceted performance of Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm more intuitively, we also conduct experiments on algorithm convergence analysis, the Friedman test, the Wilcoxon signed-rank test, and running time comparison. Through the above several comparative experiments, Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm has indeed demonstrated its strong competitiveness. Finally, we apply it to Capacitated Vehicle Routing Problem. Through comparing with the contrast algorithms, it is confirmed that Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm can achieve better vehicle routes planning.

A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network

“…FOA simulates the optimization process of fruit flies according to their foraging behavior and then finds the optimal solution. It is characterized by advantages including understandable logic, no need to adjust data and parameters, and rapid convergence [36][37][38]. When searching in a complex environment, FOA is likely to be trapped in local extremums and leads to a large deviation between the results and reality.…”

Trucks Pooling and Allocation in TSE Concept Using GIS Spatial and Novel FFOA

“…Fruit fly optimization (FFOA), a newly developed algorithm, has recently received much attention, discussion, and popularity because of its fast and clever computations (Karkalos et al, 2019;R. Y. Wang et al, 2022;Xing & Gao, 2014).…”

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

“…But there is no perfect optimization algorithm, and the optimization algorithm should be improved to solve problems better. Therefore, numerous excellent algorithms were proposed, such as work in [60] , introducing quasi-affine transformation evolutionary strategy to prevent premature convergence of FOA (QTFOA) and applying it to optimize the capacitated vehicle routing problem. To prevent FOA falls prematurely, [61] proposed an improved FOA to solve the transmission line tower junction optimization problem.…”