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LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems

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  • Dan Shan
  • GuoHua Cao
  • HongJiang Dong

Abstract

Recently, a new fruit fly optimization algorithm (FOA) is proposed to solve optimization problems. In this paper, we empirically study the performance of FOA. Six different nonlinear functions are selected as testing functions. The experimental results illustrate that FOA cannot solve complex optimization problems effectively. In order to enhance the performance of FOA, an improved FOA (named LGMS-FOA) is proposed. Simulation results and comparisons of LGMS-FOA with FOA and other metaheuristics show that LGMS-FOA can greatly enhance the searching efficiency and greatly improve the searching quality.

Suggested Citation

  • Dan Shan & GuoHua Cao & HongJiang Dong, 2013. "LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, September.
    • Handle: RePEc:hin:jnlmpe:108768
    DOI: 10.1155/2013/108768
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    Cited by:

    1. Qasim M. Zainel & Saad M. Darwish & Murad B. Khorsheed, 2022. "Employing Quantum Fruit Fly Optimization Algorithm for Solving Three-Dimensional Chaotic Equations," Mathematics, MDPI, vol. 10(21), pages 1-21, November.
    2. Pan, Wenchao & Guo, Zhichen & Zhang, Jiayan Shi Yaxuan & Luo, Lingle, 2024. "Forecasting of coal and electricity prices in China: Evidence from the quantum bee colony-support vector regression neural network," Energy Economics, Elsevier, vol. 134(C).

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