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Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing.

Yong Li1, Jinxing Li1, Yu Sun1

  • 1School of E-Commerce and Logistics, Beijing Technology and Business University, Beijing 100048, China.

Biomimetics (Basel, Switzerland)
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

A new firefly-ant compound optimization (FaCO) algorithm improves load balancing in parallel computing by optimizing graph partitioning. This method enhances efficiency and accuracy over existing swarm intelligence algorithms for complex simulations.

Keywords:
Firefly algorithmbio-inspired designheuristic algorithmhybrid methodsload balancemulti-objective optimisationparallel computing

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Area of Science:

  • Computational Science
  • Computer Science
  • Optimization Algorithms

Background:

  • Large-scale parallel computing is essential for complex computational fluid dynamics simulations.
  • Efficient load balancing and communication overhead are critical in multi-block grid numerical simulations.
  • Multi-level graph partitioning algorithms are key to solving load-balancing challenges.

Purpose of the Study:

  • To propose a novel Firefly-Ant compound optimization (FaCO) algorithm for enhancing load balancing in parallel computing.
  • To address limitations of existing swarm intelligence algorithms, such as local optimization and low accuracy.
  • To optimize the mapping strategy of grid blocks to processors for improved computational efficiency.

Main Methods:

  • Developed a Firefly-Ant compound optimization (FaCO) algorithm, merging firefly and ant colony optimization rules.
  • Transformed multi-level graph partitioning results into a Traveling Salesman Problem (TSP).
  • Applied FaCO to optimize load distribution and refine graph segmentation for parallel computing.

Main Results:

  • The FaCO algorithm demonstrated effectiveness on benchmark datasets (Oliver30, eil51).
  • FaCO significantly outperformed the standard firefly algorithm and other methods in search results and efficiency.
  • The algorithm achieved superior results in optimizing load balancing for parallel computing tasks.

Conclusions:

  • The proposed FaCO algorithm offers a significant improvement for load balancing in parallel computing.
  • FaCO effectively addresses local optimization and accuracy issues in swarm intelligence-based TSP solutions.
  • This approach enhances the efficiency of numerical simulations involving complex grids and large-scale computations.