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 Programme
Overview
Detailed
Programme
CHAIR: T.B.D.
Time: Tuesday, March
22nd, 14h20-16h00
| SO-1 |
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| Title: |
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Applications of PSO Algorithm and
OIF Elman Neural Network to Assessment and Forecasting for
Atmospheric Quality |
| Author(s): |
L.M. Wang,
X.H. Shi,
G.J. Chen,
H.W. Ge,
H.P. Lee,
Y .C. Liang |
| Abstract: |
The assessment and forecast for atmospheric
quality have become the key problem in the study of the quality
of atmospheric environment. In order to evaluate the grade
of the atmospheric pollution, a model based on the particle
swarm optimization (PSO) algorithm is proposed in this paper.
Experimental results show the advantages of the proposed models,
such as pellucid principle and physical explication, predigested
formula and low computation complexity. In addition, the output-input
feedback Elman (OIF Elman) neural network is also applied
to forecast the atmospheric quality. Simulations show that
the OIF Elman neural network has great potential in the field
of forecasting the atmospheric quality. |
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| SO-2 |
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| Title: |
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A hybrid particle swarm optimization
model for the traveling salesman problem |
| Author(s): |
Thiago Rogalsky Machado,
Heitor Silvério Lopes |
| Abstract: |
This work presents a new hybrid model, based
on Particle Swarm Optimization, Genetic Algorithms and Fast
Local Search, for the blind traveling salesman problem. A
detailed description of the model is provided. The implemented
system was tested with instances from 76 to 2103 cities. For
instances up to 299 cities, results are less than 1% in excess
of the known optima. In the average, for all instances, results
are 3.8317 % in excess. These excellent results encourage
further research and improvement in the hybrid model |
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| SO-3 |
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| Title: |
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Perceptive Particle Swarm Optimisation |
| Author(s): |
Boonserm Kaewkamnerdpong,
Peter J. Bentley |
| Abstract: |
Conventional particle swarm optimisation relies
on exchanging information through social interaction among
individuals. However for real-world problems involving control
of physical agents (i.e., robot control), such detailed social
interaction is not always possible. In this study, we propose
the Perceptive Particle Swarm Optimisation algorithm, in which
both social interaction and environmental interaction are
increased to mimic behaviours of social animals more closely. |
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| SO-4 |
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| Title: |
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Wasp swarm optimization of logistic
systems |
| Author(s): |
Pedro Pinto,
T homas A. Runkler,
J oão M. Sousa |
| Abstract: |
In this paper, we present the optimization of
logistic processes in supply chains using the meta-heuristic
algorithm known as wasp swarm, which draws parallels between
the process to optimize and the way individuals in wasp colonies
interact and allocate tasks to meet the demands of the nest. |
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| SO-5 |
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| Title: |
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A Parallel Vector-Based Particle
Swarm Optimizer |
| Author(s): |
I. L Schoeman,
A. P. Engelbrecht |
| Abstract: |
Several rechniques have been employed to adapt
particle swarm techniques to find multiple optimal solutions
in a problem domain. Niching algorithms have to identify good
candidate solutions among a population of particles in order
to split the space into regions where an optimal solution
may be found. Subsequently the swarm must be optimized so
that particles contained inside the niches will converge on
multiple optimal solutions. This paper presents an improved
vector-based particle swarm optimizer where subswarms contained
in niches are optimized in parallel. |
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