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A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm.

Zhihang Yue1,2, Sen Zhang1,2, Wendong Xiao1,2

  • 1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Sensors (Basel, Switzerland)
|April 16, 2020
PubMed
Summary
This summary is machine-generated.

A new hybrid algorithm, Fireworks Grey Wolf Optimizer (FWGWO), combines Fireworks Algorithm and Grey Wolf Optimizer. It enhances global search capability and convergence speed, overcoming limitations of individual algorithms.

Keywords:
Fireworks AlgorithmGrey Wolf Optimizerexploitation and explorationhybrid algorithm

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Area of Science:

  • Computational Intelligence
  • Meta-heuristic Optimization Algorithms
  • Nature-Inspired Computing

Background:

  • Grey Wolf Optimizer (GWO) and Fireworks Algorithm (FWA) are effective meta-heuristic algorithms.
  • GWO can converge to local optima, and FWA exhibits slow convergence.
  • A hybrid approach is needed to leverage the strengths of both GWO and FWA.

Purpose of the Study:

  • To propose a novel hybrid algorithm, Fireworks Grey Wolf Optimizer (FWGWO).
  • To enhance global optimal search capability and convergence speed by fusing GWO and FWA.
  • To address the limitations of individual GWO and FWA algorithms.

Main Methods:

  • Developed the hybrid Fireworks Grey Wolf Optimizer (FWGWO) algorithm.
  • Integrated exploration from FWA and exploitation from GWO using a balance coefficient.
  • Evaluated FWGWO on 16 benchmark functions of varying dimensions and complexities.

Main Results:

  • FWGWO demonstrated improved global optimal search capability compared to individual algorithms.
  • The hybrid algorithm showed enhanced convergence speed.
  • Experimental results validated FWGWO's effectiveness against standard FWA, GWO, EGWO, and AGWO.

Conclusions:

  • The proposed FWGWO algorithm effectively combines the strengths of FWA and GWO.
  • FWGWO offers superior performance in terms of global optimization and convergence speed.
  • This hybrid approach presents a promising solution for complex optimization problems.