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 Programme
Overview
Detailed
Programme
CHAIR: T.B.D.
Wednesday, March 23rd, 11h00-12h40
| NC-1 |
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| Title: |
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Swarm Intelligence Clustering Algorithm
based on Attractor |
| Author(s): |
Qingyong Li,
Zhiping Shi,
Zhongzhi Shi |
| Abstract: |
Ant colonies behavior and their self-organizing
capabilities have been popularly studied, and various swarm
intelligence models and clustering algorithms also have been
proposed. Unfortunately, the cluster number is often too high
and convergence is also slow. We put forward a novel structure-attractor,
which actively attracts and guides the ant’s behavior,
and implement an efficient strategy to adaptively control
the clustering behavior. Our experiments show that swarm intelligence
clustering algorithm based on attractor (SICBA for short)
greatly improves the convergence speed and clustering quality
compared with LF and also has many notable virtue such as
flexibility, decentralization. |
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| NC-2 |
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| Title: |
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Ant-based distributed optimization
for supply chain management |
| Author(s): |
Carlos. A. Silva,
J. M. Sousa,
T. Runkler,
J.M.G Sá da Costa |
| Abstract: |
Multi-agent systems are the best approach for
an efficient supply chain management. However, the control
of each sub-system in a supply-chain is a complex optimization
problem and therefore the agents have to include powerful
optimization resources along with the communication capacities.
This paper presents a new methodology for supply-chain management,
the distributed optimization based on ant colony optimization,
where the concepts of multi-agent systems and meta-heuristics
are merged. A simulation example, with the logistic and the
distribution sub-systems of a supply-chain, shows how the
distributed optimization outperforms a centralized approach. |
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| NC-3 |
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| Title: |
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Comparison of nature inspired and
deterministic scheduling heuristics considering optimal schedules |
| Author(s): |
Udo Honig,
Wolfram Schiffmann |
| Abstract: |
We report about a performance evaluation of
nature inspired stochastic vs. conventional deterministic
scheduling algorithms. By means of a comprehensive test bench,
that comprises task graphs with diverse properties, we determined
the absolute performance of those algorithms with respect
to the optimal solutions. Surprisingly, the nature inspired
stochastic algorithms outperformed all the investigated deterministic
algorithms. |
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| NC-4 |
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| Title: |
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An External Memory Supported ACO
for the Frequency Assignment Problem |
| Author(s): |
Adnan Acan,
Akin Gunay |
| Abstract: |
Ant colony optimization algorithm is integrated
with an external memory for the purpose of improving its efficiency
for the solution of a well-known hard combinatorial optimization
problem. The external memory keeps variable-size solution
segments extracted from promising solutions of previous iterations.
Each solution segment is associated with its parent’s
fitness value. In the construction of a solution, each ant
retrieves a segment from the memory using tournament selection
and constructs a complete solution by filling the absent components.
The proposed approach is used for the solution of minimum
span frequency assignment problem for which very promising
results are obtained for provably difficult benchmark test
problems that could not be handled by any other ACO-based
approach so far. |
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