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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Related Experiment Video

Updated: Jan 22, 2026

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion.

Çiğdem İnan Acı1, Hakan Gülcan2

  • 1Mersin University, Department of Computer Engineering, Mersin 33343, Turkey.

Computational Intelligence and Neuroscience
|July 9, 2019
PubMed
Summary
This summary is machine-generated.

This study enhances the dragonfly algorithm (DA) by replacing Levy flight with Brownian motion, improving optimization performance. The modified DA achieved up to 90% better results on benchmark problems and reduced welded beam design costs by 20%.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The dragonfly algorithm (DA) is a recent metaheuristic optimization technique inspired by dragonfly swarm behavior.
  • The standard DA utilizes the Levy flight mechanism (LFM) for randomization, which can lead to search space overflow and flight interruptions due to large step sizes.

Purpose of the Study:

  • To improve the randomization process of the dragonfly algorithm.
  • To address the limitations of the Levy flight mechanism in the standard DA.
  • To evaluate the performance of a modified dragonfly algorithm using Brownian motion.

Main Methods:

  • The Levy flight mechanism in the dragonfly algorithm was replaced with Brownian motion.
  • The modified dragonfly algorithm was tested on 15 single-objective and 6 multi-objective benchmark optimization problems.
  • Performance comparison between the original DA and the modified DA was conducted.

Main Results:

  • The modified dragonfly algorithm demonstrated significant improvements, achieving up to 90% enhancement in minimum point access compared to the original DA.
  • Application to the welded beam design problem resulted in a 20% reduction in optimum cost.
  • The modified algorithm showed superior performance in both benchmark testing and a practical engineering design problem.

Conclusions:

  • Replacing Levy flight with Brownian motion effectively enhances the randomization stage of the dragonfly algorithm.
  • The modified dragonfly algorithm offers improved efficiency and effectiveness for solving complex optimization problems.
  • The enhanced DA presents a viable alternative for optimization tasks, including engineering design challenges.