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Diversity of Protists IV01:27

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Amoebozoa represent a diverse group of terrestrial and aquatic protists that utilize lobe-shaped pseudopodia for locomotion and feeding. This characteristic differentiates them from the Rhizaria, which possess threadlike pseudopodia. The primary classifications within Amoebozoa include gymnamoebas, entamoebas, and the plasmodial and cellular slime molds. Phylogenetic evidence indicates that Amoebozoa diverged from a lineage that ultimately gave rise to fungi and animals.Gymnamoebas and...
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FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm.

Jassim Alfadhli1, Ali Jaragh1, Mohammad Gh Alfailakawi1

  • 1Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait.

Neural Computing & Applications
|March 14, 2022
PubMed
Summary

An adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) reduces runtime by 20-30% compared to the original SMA. This enhanced computational efficiency makes FP-SMA ideal for time-sensitive applications.

Keywords:
Fluctuant population (FP)Metaheuristic algorithm (MA)Population adaptationPopulation diversitySlime mould algorithm (SMA)

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The original Slime Mould Algorithm (SMA) uses a fixed population size, potentially limiting its efficiency.
  • Balancing exploration and exploitation is crucial for effective optimization.

Purpose of the Study:

  • To introduce an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA).
  • To improve the computational efficiency and performance of the SMA.

Main Methods:

  • Developed FP-SMA with an adaptive population size mechanism.
  • Evaluated FP-SMA on 13 standard and 30 IEEE CEC2014 benchmark functions.
  • Compared FP-SMA against the original SMA in terms of runtime and solution quality.

Main Results:

  • FP-SMA demonstrated significant reductions in runtime, averaging 20-30% fewer function evaluations.
  • In some cases, runtime savings reached up to 60%.
  • FP-SMA maintained good solution quality comparable to the original SMA.

Conclusions:

  • FP-SMA offers higher computational efficiency than the original SMA.
  • The adaptive population size effectively balances exploitation and exploration phases.
  • FP-SMA is a more favorable choice for time-constrained optimization problems.