Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review

@article{Bonyadi2017ParticleSO,
  title={Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review},
  author={Mohammad Reza Bonyadi and Zbigniew Michalewicz},
  journal={Evolutionary Computation},
  year={2017},
  volume={25},
  pages={1-54}
}
This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study. 
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371 Citations
Highly Influential Citations
19
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61
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371 Citations

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The benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm and it is compared with genetic algorithm (GA) in order to prove capability of PSO.
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This article looks at the feedback control of system which contains randomness from the time delay feedback control perspective, and selected particles are controlled to a stable point by adding small perturbations to the particle position.
Hybrid PSO Algorithm with Iterated Local Search Operator for Equality Constraints Problems
TLDR
A hybrid PSO algorithm with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems and shows improvement in accuracy, reducing the gap for the tested problems.
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This paper presents a computational procedure and shows that this approach leads to reevaluation and extension of previously know stability regions for PSO using a Lyapunov approach under stagnation assumptions.
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References

SHOWING 1-10 OF 183 REFERENCES
Parameter Selection in Particle Swarm Optimization
This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.…
The particle swarm optimization algorithm: convergence analysis and parameter selection
  • I. Trelea
  • Computer Science
    Inf. Process. Lett.
  • 2003
Recent advances in particle swarm
TLDR
This paper reviews the development of the particle swarm optimization method in recent years and modifications to adapt to different and complex environments are reviewed, and real world applications are listed.
A new locally convergent particle swarm optimiser
This paper introduces a new Particle Swarm Optimisation (PSO) algorithm with strong local convergence properties. The new algorithm performs much better with a smaller number of particles, compared…
Particle swarm optimization
TLDR
A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Fuzzy adaptive particle swarm optimization
  • Yuhui Shi, R. Eberhart
  • Computer Science
    Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
  • 2001
TLDR
The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.
Particle swarm optimisation with spatial particle extension
TLDR
This paper introduces spatial extension to particles in the PSO model in order to overcome premature convergence in iterative optimisation and shows that the SEPSO indeed managed to keep diversity in the search space and yielded superior results.
Particle Swarm Optimization Method for Constrained Optimization Problems
TLDR
The performance of the Particle Swarm Optimization method in coping with Constrained Optimization problems is investigated and results are compared with those obtained through other evolutionary algorithms, such as Evolution Strategies and Genetic Algorithms.
Research on particle swarm optimization: a review
  • Meiping Song, Guo-chang Gu
  • Computer Science
    Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
  • 2004
TLDR
The fundamental and standard algorithm is introduced, the work on the algorithm improvement during the past years is surveyed, as well as the applications on the multi-objective optimization, neural networks and electronics, etc.
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
  • P. Angeline
  • Computer Science
    Evolutionary Programming
  • 1998
TLDR
This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization by comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature.
...
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