Results 241 to 250 of about 73,502 (293)
Some of the next articles are maybe not open access.

Scalability of niche PSO

Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2004
In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are ...
R. Brits   +2 more
openaire   +1 more source

An intelligent PSO watermarking

2010 International Conference on Machine Learning and Cybernetics, 2010
Meerwald et. al. [2] revealed that the method proposed in [1] exists potential insecurity. This paper proposes an intelligent watermarking by invoking particle swarm optimization (PSO) technique in wavelet domain to overcome the revealed insecurity issue, furthermore resolve the conflict between imperceptibility and robustness of watermarking ...
Yuh-Rau Wang   +2 more
openaire   +1 more source

Cellular PSO: A PSO for Dynamic Environments

2009
Many optimization problems in real world are dynamic in the sense that the global optimum value and the shape of fitness function may change with time. The task for the optimization algorithm in these environments is to find global optima quickly after the change in environment is detected. In this paper, we propose a new hybrid model of particle swarm
Ali B. Hashemi, Mohammad Reza Meybodi
openaire   +1 more source

Control strategy PSO

Applied Soft Computing, 2016
An evaluation index called "Control Strategy PSO" is developed.It can be applied to other intelligent algorithms.We present a detailed theoretical and empirical analysis. Many variants of particle swarm optimization (PSO) both enhance the performance of the original method and greatly increase its complexity.
Wei Zhang   +4 more
openaire   +1 more source

On Trajectories of Particles in PSO

2007 IEEE Swarm Intelligence Symposium, 2007
The moving behaviour of the particles in particle swarm optimization (PSO) algorithms is studied in this paper. It is shown that particles in standard PSO have a clear bias in their movement direction that depends on the direction of the coordinate axes.
Stefan Janson, Martin Middendorf
openaire   +1 more source

Random Asynchronous PSO

The 5th International Conference on Automation, Robotics and Applications, 2011
In this work we propose the Random Asynchronous PSO (RAPSO) algorithm, a rather simple but intuitive variant of the Asynchronous PSO (APSO) that introduces a randomized order in which particles share their information. Our algorithm, while conceived as serial in terms of execution, is able to model the behavior of the Parallel Asynchronous PSO (PAPSO ...
Juan Rada-Vilela   +2 more
openaire   +1 more source

NSC-PSO, a Novel PSO Variant without Speeds and Coefficients

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015
The paper is introducing the principles of a new global optimization method, No Speeds and Coefficients Particle Swarm Optimization (NSC-PSO), applied to approaching the Continuous Global Optimization Problem (CGOP). Inspired from existing meta-heuristic optimization methods in the Swarm Intelligence (SI) class, like canonical Particle Swarm ...
George Anescu, Ilie Prisecaru
openaire   +1 more source

An improved rotationally invariant PSO: A modified standard PSO-2011

2016 IEEE Congress on Evolutionary Computation (CEC), 2016
Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is simple and powerful algorithm, and is applied to many real world problems. However, because the bias of the search area exists in the conventional PSO, the search performance is deteriorated in non-separable ...
Yosuke Hariya   +2 more
openaire   +1 more source

Comparative study of random-PSO and Linear-PSO algorithms

2012 International Conference on Computer & Information Science (ICCIS), 2012
This paper presents a Linear-Particle Swarm Optimization (PSO) algorithm for discovering motifs in DNA sequences, and the strengths and weaknesses of using the Linear-PSO for discovering motifs of DNA sequences will be discussed. For the experiment, ten DNA sequences from an online database were used as an input for the algorithms.
Sharifah Lailee Syed Abdullah   +3 more
openaire   +1 more source

Impact of Ant Size on Ant Supervised by PSO, AS-PSO, Performances

2017
AS-PSO, ANT Supervised by PSO is hybrid hierarchical metaheuristic optimization method where PSO optimizes ANT parameters to enhance its performances. In this paper, a focus is made on the impact of the ACO swarm size on AS-PSO performances for the Traveling Salesmen Problem (TSP) where AS-PSO is already known as a relevant solver.
Sonia Kefi, Nizar Rokbani, Adel M. Alimi
openaire   +1 more source

Home - About - Disclaimer - Privacy