A review of particle swarm optimization. Part I: background and development

@article{Banks2007ARO,
  title={A review of particle swarm optimization. Part I: background and development},
  author={Alec Banks and Jonathan Vincent and Chukwudi Anyakoha},
  journal={Natural Computing},
  year={2007},
  volume={6},
  pages={467-484}
}
Particle Swarm Optimization (PSO), in its present form, has been in existence for roughly a decade, with formative research in related domains (such as social modelling, computer graphics, simulation and animation of natural swarms or flocks) for some years before that; a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst… 

A Brief Historical Review of Particle Swarm Optimization (PSO)

Mathematics Department, Universidad de Oviedo, 33007 Oviedo, SpainParticle Swarm Optimization is an evolutionary algorithm that has been applied to many differentengineering and technological

Particle Swarm Optimization from Animation to Recent Trends: A Systematic Review

A systematic and chronological effort has been made for literature review from 1983 to 2019 which fits the need of researcher from starting with all the developments like historical development, addition of new parameters, tuning or refinement of parameters and its variants for different optimization problems with constraints, multi-objectives.

Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms

An evolutionary algorithm (EA) is proposed which is algorithmically similar to PSO, and then different EA-specific operators are borrowed to enhance the PSO's performance to establish an equivalence between various genetic/evolutionary and other bio-inspired algorithms.

Particle Swarm Methods

The present work exposes the basic concepts of particle swarm optimization and presents a number of popular variants that opened new research directions by introducing novel ideas in the original model of the algorithm.

Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review

  • Ahmed G. Gad
  • Computer Science
    Archives of Computational Methods in Engineering
  • 2022
This paper analyzes the existing research on methods and applications published between 2017 and 2019 in a technical taxonomy of the picked content, including hybridization, improvement, and variants of PSO, as well as real-world applications of the algorithm categorized into: health-care, environmental, industrial, commercial, smart city, and general aspects applications.

Improving a Particle Swarm Optimization Algorithm Using an Evolutionary Algorithm Framework

It is emphasized here that EA and related optimization researchers should put more efforts in establishing equivalence between different existing optimization algorithms of interests to enhance an algorithm’s performance and also to better understand the scope of operators of different algorithms.
...

References

SHOWING 1-10 OF 77 REFERENCES

A Diversity-Guided Particle Swarm Optimizer - the ARPSO

The attractive and repulsive PSO (ARPSO) is introduced in trying to overcome the problem of premature convergence, an algorithm that alternates between phases of attraction and repulsion and clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization.

Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

A novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations to overcome the difficulties of selecting an appropriate mutation step size for different problems.

Optimization Using Particle Swarms with Near Neighbor Interactions

The resulting algorithm, known as Fitness-Distance-Ratio based PSO (FDR-PSO), is shown to perform significantly better than the original PSO algorithm and several of its variants, on many different benchmark optimization problems.

A modified particle swarm optimizer

  • Y. ShiR. Eberhart
  • Computer Science
    1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360)
  • 1998
A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.

The particle swarm - explosion, stability, and convergence in a multidimensional complex space

This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.

Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed.

Particle swarm optimization: surfing the waves

  • E. ÖzcanC. Mohan
  • Computer Science
    Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
  • 1999
This paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space.

A Parallel Particle Swarm Optimization Algorithm with Communication Strategies

A parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data, which demonstrates the usefulness of the proposed PPSO algorithm.

Particle swarm optimization

A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.

An Empirical Comparison of Particle Swarm and Predator Prey Optimisation

A new algorithm, which the authors call predator prey optimiser, combines the ideas of particle swarm optimisation with a predator prey inspired strategy, which is used to maintain diversity in the swarm and preventing premature convergence to local suboptima.
...