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Multi-swarm UPSO algorithm based on seed strategy for atomic clusters structure optimization.

Xinghua Tang1, Jing Liu1, Jingjing Zhu1

  • 1Information Engineering College, Shanghai Maritime University, Shanghai, China.

Computational Biology and Chemistry
|November 15, 2021
PubMed
Summary
This summary is machine-generated.

The novel Seed Strategy-based Multi-swarm Unified Particle Swarm Optimization (SS-DMS-UPSO) algorithm effectively optimizes atomic cluster structures. This method overcomes local optima issues in Particle Swarm Optimization (PSO), finding stable structures with higher accuracy.

Keywords:
Atomic clustersInformation communication strategyMulti-swarm mechanismParticle swarm optimizationSeed strategyUnified particle swarm optimization

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Optimization Algorithms

Background:

  • Particle Swarm Optimization (PSO) algorithms often suffer from premature convergence to local optima and limited inter-particle communication.
  • Optimizing atomic cluster structures is crucial for understanding material properties and designing new materials.

Purpose of the Study:

  • To introduce a new optimization algorithm, the Seed Strategy-based Multi-swarm Unified Particle Swarm Optimization (SS-DMS-UPSO), designed to address the limitations of traditional PSO.
  • To apply the SS-DMS-UPSO algorithm for finding stable and low-energy structures of atomic clusters.

Main Methods:

  • The SS-DMS-UPSO algorithm divides the particle population into sub-populations that evolve independently using the Unified Particle Swarm Optimization (UPSO) algorithm with varying unification factors.
  • Sub-populations are periodically merged, and the population is re-divided to facilitate information exchange and exploration.
  • The algorithm iteratively refines the population to identify the global best particle, representing the optimal atomic cluster structure.

Main Results:

  • The SS-DMS-UPSO algorithm successfully identified optimal or near-optimal structures for atomic clusters with 2-31 atoms.
  • For atomic clusters with 32-35 atoms, the algorithm found approximate optimal structures.
  • The energy values obtained by SS-DMS-UPSO were significantly closer to ideal energy values compared to other algorithms, indicating more stable structures.

Conclusions:

  • The SS-DMS-UPSO algorithm demonstrates superior performance in optimizing atomic cluster structures, overcoming common PSO limitations.
  • The algorithm's effectiveness in finding stable and low-energy configurations highlights its potential for computational materials science.
  • SS-DMS-UPSO offers a more stable and accurate approach for atomic cluster structure optimization.