Reference: K. Tang, X. Yao, P. N. Suganthan, C. MacNish, Y. P. Chen, C. M. Chen, and Z. Yang, "Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization," Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, http://nical.ustc.edu.cn/cec08ss.php, November 2007.
Performance
Comparisons and analysis in pdf: https://github.com/P-N-Suganthan
Accepted Papers are listed below (Codes Available for DMS-PSO)
(PDFs are available: https://github.com/P-N-Suganthan )
1.
Janez
Brest, Aleˇs Zamuda, Borko Boˇskovi´c,
Mirjam Sepesy Mauˇcec, and Viljem
ˇZumer, "High-Dimensional
Real-Parameter Optimization using Self-Adaptive Differential Evolution
Algorithm with Population Size Reduction"
2.
Sheng-Ta
Hsieh, Tsung-Ying Sun, Chan-Cheng Liu and Shang-Jeng Tsai, "Solving Large Scale Global Optimization Using
Improved Particle Swarm Optimizer"
3.
Cara
MacNish, Xin Yao, "Direction
Matters in High-Dimensional Optimisation"
4.
Lin-Yu
Tseng and Chun Chen, "Multiple Trajectory Search for Large Scale Global
Optimization"
5.
Yu
Wang, Student Member, IEEE, Bin Li, Member, IEEE, "A Restart Univariate
Estimation of Distribution Algorithm: Sampling under Mixed Gaussian and L´evy probability Distribution"
6.
Zhenyu
Yang, Ke Tang and Xin Yao, "Multilevel Cooperative Coevolution for Large
Scale Optimization"
7.
Aleˇs Zamuda, Janez
Brest, Borko Boˇskovi´c,
Viljem ˇ Zumer, "Large Scale Global Optimization Using Differential
Evolution With Self-adaptation and Cooperative Co-evolution"
8.
S.
Z. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren, "Dynamic Multi-Swarm
Particle Swarm Optimizer with Local Search for Large Scale Global
Optimization"
Update Notice:
In the past two decades, different kinds of nature-inspired optimization algorithms have been designed and applied to solve optimization problems, e.g., simulated annealing (SA), evolutionary algorithms (EAs), differential evolution (DE), particle swarm optimization (PSO), Ant Colony Optimisation (ACO), Estimation of Distribution Algorithms (EDA), etc. Although these approaches have shown excellent search abilities when applying to some 30-100 dimensional problems, many of them suffer from the "curse of dimensionality", which implies that their performance deteriorates quickly as the dimensionality of search space increases. The reasons appear to be two-fold. First, complexity of the problem usually increases with the size of problem, and a previously successful search strategy may no longer be capable of finding the optimal solution. Second, the solution space of the problem increases exponentially with the problem size, and a more efficient search strategy is required to explore all the promising regions in a given time budget.
Historically, scaling EAs to large size problems have attracted much interest, including both theoretical and practical studies. The earliest practical approach might be the parallelism of an existing EA. Later, cooperative coevolution appears to be another promising method. However, existing work on this topic are often limited to the test problems used in individual studies, and a systematic evaluation platform is not available in the literature for comparing the scalability of different EAs.
This special session is devoted to the novel approaches,
algorithms and techniques for tackling large scale
global optimization problems, involving single objective or multiple
objectives, binary or discrete or real or mixed variables. Papers on novel test
suites that help us in understanding problem characteristics are also welcome.
We encourage all authors to submit their test functions, algorithms and results
to the Birmingham Benchmark site -- Evolutionary Computation Benchmark
Repository:
EvoCoBR: http://www.cs.bham.ac.uk/research/projects/ecb/
Further, a set of scalable function optimization problems are available at: LSGO.CEC08.Benchmark.zip. Researchers who make use of this test suite may also participate in our companion competition too.
A competition on high-dimensional function optimization will also be organized in company with our special session. In the competition, a set of scalable function optimization problems are provided. The details of the set of scalable functions and requirements on the simulation procedure are available here. Researchers are welcome to apply any kind of computational intelligence approaches (e.g. EAs, Neural Nets, fuzzy-based methods) to the test suite. The results of this competition will be archived on our web pages as done for the CEC 2005, 2006, 2007.
The WCCI2008 committee will kindly provide one free registration for the winner of the competition.
|
Paper
Submission: |
Dec
1, 2007 |
|
Acceptance
Notification: |
Feb
1, 2008 |
|
Final
Manuscript Due: |
Mar
1, 2008 |
Ke Tang
Nature Inspired Computation and Applications Laboratory (NICAL)
Department of Computer Science and Technology
University of Science and Technology of China, Hefei, Anhui, China
Xin Yao
Nature Inspired Computation and Applications Laboratory (NICAL)
Department of Computer Science and Technology
University of Science and Technology of China
The Centre of Excellence for Research in Computational Intelligence and
Applications (CERCIA)
School of Computer Science
University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.
P. N. Suganthan
School of Electrical and Electronic Engineering
Nanyang Technological University, Singapore
http://www.ntu.edu.sg/home/epnsugan
Cara MacNish
School of Computer Science & Software Engineering
The University of Western Australia
M002, 35 Stirling Highway, Crawley, Western Australia, 6009