# Defining a Standard for Particle Swarm Optimization

@article{Bratton2007DefiningAS, title={Defining a Standard for Particle Swarm Optimization}, author={Daniel Bratton and James Kennedy}, journal={2007 IEEE Swarm Intelligence Symposium}, year={2007}, pages={120-127} }

Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developmentsā¦Ā

## 1,182 Citations

Particle Swarm Optimization with Flexible Swarm for Unconstrained Optimization

- Computer Science
- 2013

The particle swarm optimization algorithm with flexible swarm (PSO-FS) was evaluated on 14 functions often used to benchmark the performance of optimization algorithms and showed that PSO- FS always performed one of the better results.

New evolution algorithm based on the standard particle swarm optimization

- Computer Science2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)
- 2011

This research proves that the convergence process of PSO has nothing to do with the velocity and the proposed method modified simple PSO (msPSO) can converge.

Particle swarm optimization with adaptive mutation for multimodal optimization

- Computer ScienceAppl. Math. Comput.
- 2013

A Comprehensive Review of Swarm Optimization Algorithms

- Computer SciencePloS one
- 2015

The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

Particle Swarm Optimization with Velocity Adaptation

- Computer Science2009 International Conference on Adaptive and Intelligent Systems
- 2009

The idea is to introduce a velocity adaptation mechanism into PSO algorithms that is similar to step size adaptation used in evolution strategies and it is shown that using velocity adaptation leads to better results for a wide range of benchmark functions.

Simple and Adaptive Particle Swarms

- Computer Science
- 2010

This thesis details and explains the substantial advances made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community.

Design and experimental evaluation of multiple adaptation layers in self-optimizing particle swarm optimization

- Computer ScienceIEEE Congress on Evolutionary Computation
- 2010

A novel self-optimizing particle swarm optimizer with multiple adaptation layers is introduced, and the new idea of using virtual parameter swarms which hold modifiable parameter configurations each is introduced.

A behavioral-based approach to Particle Swarm Optimization

- Computer Science2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)
- 2013

The presented algorithm relies on the idea that the particles exploring the search space can be divided in subgroups, each of which with a peculiar behavior, such as to enlarge the explored area while refining the actual solution.

Limiting the Velocity in the Particle Swarm Optimization Algorithm

- Geology, Computer ScienceComputaciĆ³n y Sistemas
- 2016

This work presents a different method to regulate the velocity by changing the maximum limit of the velocity at each iteration, thus eliminating the use of a factor in the PSO algorithm.

Hybrid Particle Swarm Optimizers with a General Fitness Evaluation Strategy

- Computer Science2009 International Forum on Information Technology and Applications
- 2009

This paper hybridizes GFES with several PSO's variants, and shows that these hybrid PSOs are effective for coping with multimodal problems.

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