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

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Etesami Reza; Madadi Mohsen; Keynia Farshid

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Journal Paper

Paper Information

Journal: Journal of Mahani Mathematical Research Year:2024 | Volume:13 | Issue:2 Page(s): 73-91

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Title

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

Author(s)

Etesami Reza | Madadi Mohsen | Keynia Farshid | Issue Writer Certificate

Keywords

Fruit fly optimization algorithm

particle swarm optimization

random perturbation

velocity

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.

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