A new improved fruit fly optimization algorithm based on particle swarm optimization algorithm for function optimization problems

Abstract

The Fruit Fly Optimization algorithm is an intelligent optimization algorithm. To improve accuracy, convergence speed, as well as jumping out of local optimum, a modified Fruit Fly Optimization algorithm (MFFOV) is proposed in this paper. The proposed algorithm uses velocity in particle swarm optimization and improves smell based on dimension and random perturbations. As a result of testing ten benchmark functions, the convergence speed and accuracy are clearly improved in Modified Fruit Fly Optimization (MFFOV) compared to algorithms of Fruit Fly Optimization (FFO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based Optimization (TLBO), Genetic Algorithms (GA), Gravitational Search Algorithms (GSA), Differential Evaluations (DEs) and Hunter–Prey Optimizations (HPOs). A performance verification algorithm is also proposed and applied to two engineering problems. Test functions and engineering problems were successfully solved by the proposed algorithm.

Keywords

• fruit fly optimization algorithm
• particle swarm optimization
• random perturbation
• velocity