# Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture

@article{Mussi2011EvaluationOP, title={Evaluation of parallel particle swarm optimization algorithms within the CUDA{\texttrademark} architecture}, author={Luca Mussi and Fabio Daolio and Stefano Cagnoni}, journal={Inf. Sci.}, year={2011}, volume={181}, pages={4642-4657} }

## 154 Citations

Adaptive Particle Swarm Optimization with Heterogeneous Multicore Parallelism and GPU Acceleration

- Computer ScienceIEEE Transactions on Parallel and Distributed Systems
- 2017

A heterogeneous approach mitigates the time complexity of PSO adaptations, suggesting that other time-intensive stochastic methods can also benefit from the techniques proposed here.

Dynamic particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration

- Computer Science2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)
- 2015

Heterogeneous high-performance computing is proposed as a way to mitigate the time complexity of dynamic PSO adaptions, and results on high-dimensional “moving-peaks” functions show that high speedups can be obtained through making use of different high- performance components on commodity hardware.

Accelerating parallel particle swarm optimization via GPU

- Computer ScienceOptim. Methods Softw.
- 2012

A GPU-accelerated PSO (GPSO) algorithm is proposed by using a thread pool model and implement GPSO on a GPU, a promising method for tackling high-dimensional and difficult optimization problems using a low-cost and many-core GPU system.

A Survey on GPU-Based Implementation of Swarm Intelligence Algorithms

- Computer ScienceIEEE Transactions on Cybernetics
- 2016

This paper presents a comprehensive review of GPU-based parallel SIAs in accordance with a newly proposed taxonomy and novel criteria are proposed to evaluate and compare the parallel implementation and algorithm performance universally.

High-Dimensional Adaptive Particle Swarm Optimization on Heterogeneous Systems

- Computer Science
- 2014

This paper explores the efficacy of employing other types of parallel hardware for PSO and provides another demonstration that "supercomputing on a budget" is possible when subtasks of large problems are run on hardware most suited to these tasks.

Performance Evaluation of Parallel Particle Swarm Optimization for Multicore Environment

- Computer Science2018 International Conference on Advanced Science and Engineering (ICOASE)
- 2018

The proposed algorithm is applied to the standard optimization test set CEC (Congress on Evolutionary Computation) 2014 and gave good results compared to the previous algorithm, and the execution time of Shared-PSO is more efficient than the serial PSO’s.

Scheduling Using Multiple Swarm Particle Optimization with Memetic Features on Graphics Processing Units

- Computer ScienceMassively Parallel Evolutionary Computation on GPGPUs
- 2013

This work investigates the performance of a highly parallel Particle Swarm Optimization (PSO) algorithm implemented on the graphics processing unit (GPU) and shows that the GPU offers a high degree of performance and achieves a maximum of 37 times speedup over a sequential implementation when the problem size in terms of tasks is large and many swarms are used.

Implementation of Parallel Simplified Swarm Optimization in CUDA

- Computer ScienceArXiv
- 2021

The time complexity of Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.

A Survey on Parallel Particle Swarm Optimization Algorithms

- Computer ScienceArabian Journal for Science and Engineering
- 2019

Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

A CUDA Implementation of the Standard Particle Swarm Optimization

- Computer Science2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)
- 2016

This paper presents a good implementation for the Standard Particle Swarm Optimization (SPSO) on a GPU based on the CUDA architecture, which uses coalescing memory access and demonstrates that the GPU algorithm runs about maximum 46 times faster than the corresponding CPU algorithm.

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