Test suite for our companion competition is
available
here
Some indicative results are given
here!
[There is a Special Issue in
"Soft Computing" Journal on Large Scale Optimization
Guest Edited by
Manuel Lozano &
Francisco Herrera. . For details, please follow
this link. ]
Reference: K. Tang, Xiaodong Li, P. N. Suganthan, Z. Yang and
T. Weise, "Benchmark Functions for the CEC'2010 Special Session and
Competition on Large Scale Global Optimization," Technical Report,
Nature Inspired Computation and Applications Laboratory, USTC, China,
http://nical.ustc.edu.cn/cec10ss.php, & Nanyang Technological
University, 2009.
Papers Submitted to CEC2010 (The last 2 were not
published by the CEC 2010)

D. Molina, M. Lozano, and F. Herrera, "MASWChains:
Memetic Algorithm Based on Local Search Chains for Large Scale
Continuous Global Optimization", pp.31533160.
(Winner of this
competition)

J. Brest, A. Zamuda, I. Fister, M. S. Maucec, "Large
Scale Global Optimization using Selfadaptive Differential Evolution
Algorithm", pp.30973104.

H.
Wang, Z. Wu, S. Rahnamayan and D. Jiang, "Sequential DE Enhanced by
Neighborhood Search for Large Scale Global Optimization",
pp.40564062.

S.Z Zhao,
P. N. Suganthan, S. Das, "Dynamic
MultiSwarm Particle Swarm Optimizer with Subregional Harmony
Search", pp.19831990.

P.
Korosec, K. Tashkova, J. Silc, "The
Differential AntStigmergy Algorithm for LargeScale Global
Optimization", pp.42884295.

M. N. Omidvar, X. Li, X. Yao, "Cooperative
Coevolution with Delta Grouping for Large Scale Nonseparable
Function Optimization", pp.17621769.

Y.
Wang,
B. Li, "Twostage based
Ensemble Optimization for LargeScale Global Optimization,"
pp.44884495.

S. Kukkonen, "Benchmarking the Classic
Differential Evolution Algorithm on LargeScale Global
Optimization".

S. Chen, "Locust Swarms for Large
Scale Global Optimization of Nonseparable Problems".
Call for papers
In the past two decades, different kinds of natureinspired optimization
algorithms have been developed and applied to solve optimization
problems, including Simulated Annealing (SA), Evolutionary Algorithms
(EAs), Differential Evolution (DE), Particle Swarm Optimization (PSO),
Ant Colony Optimization (ACO), Estimation of Distribution Algorithms
(EDA), etc. Although these approaches have shown excellent search
abilities when applying to some small or medium size problems, many of
them will encounter severe difficulties when applying to large scale
problems, e.g., problems with up to 1000 variables. The reasons appear
to be twofold. First, the complexity of a problem usually increases
with the number of decision variables, number of constraints, or even
number of objectives (for multiobjective optimization). This emergent
complexity might prevent a previously successful search strategy from
finding the optimal solution. Second, the solution space of the problem
increases exponentially with the number of decision variables, and a
more efficient search strategy is required to explore all the promising
regions with limited computational resources.
Historically, scaling up EAs to large scale problems has attracted
much interest, including both theoretical and practical studies.
However, existing work in the areas of EAs are still limited given the
significance of the scalability issue. Due to this fact, this special
session is devoted to highlight the recent advances in EAs for large
scale optimization problems, involving single objective or multiple
objectives, unconstrained or constrained problems, binary or discrete or
real or mixed decision variables. Specifically, we encourage interested
researchers to submit their latest work on:
 Both theoretical and experimental analysis of the scalability of
EAs.
 Novel approaches and algorithms for scaling up EAs to large
scale optimization problems.
 Applications of EAs to realworld large scale optimization
problems.
 Papers on novel test suites that help us in understanding
problem characteristics are also welcome.
The Companion Competition
A competition on Highdimensional Numerical Optimization will
also be organized in company with our special session. The competition
allows participants to run their own algorithms on 20 benchmark
functions, each of which is of 1000 dimensions. The purpose of this
competition is to compare different algorithm on the exactly same
platform. The experiments will take about 205 hours with the Matlab
version on a PC with 2.40GHz CPU, and 104 hours with the Java version on
a PC with 2.2GHz CPU. Each participant (or research group) is invited to
submit a paper to the special session to present their algorithm as well
as the results obtained. Details of the set of scalable functions and
requirements on the simulation procedure are available at
http://nical.ustc.edu.cn/wcci2010/lsgo_benchmark.zip. Researchers
are welcome to apply any kind of computational intelligence approaches
(e.g. EAs, Neural Nets, fuzzybased methods) to the test suite. The
results of this competition will be archived on our web pages as done
for
the CEC 2008
competition on Highdimensional Function Optimization.
Important Dates
Paper Submission: 
January 31, 2010 
Acceptance Notification: 
March 15, 2010 
Final Manuscript Due: 
May 2, 2010 
Paper Submission
Manuscripts should be prepared according to the standard format and page
limit specified in CEC 2010. For more submission instructions, please
see the WCCI submission page at:
http://www.wcci2010.org/submission.
All special session papers will be treated in the same way as regular
papers. All papers accepted by the special session will be included in
the CEC 2010 conference proceedings and selected authors will be invited
to present their results during WCCI 2010.
Notice:
When submitting, please make sure you have chosen "S01: Large
Scale Global Optimization" as the "Main Research Topic".
Special Session Organizers
Ke Tang
Nature Inspired Computation and Applications Laboratory (NICAL)
School of Computer Science and Technology
University of Science and Technology of China, Hefei, Anhui, China
Email:
ketang@ustc.edu.cn,
Website:
http://staff.ustc.edu.cn/~ketang
Xiaodong Li
School of Computer Science and Information Technology
RMIT University, Australia
Email:
, Website:
http://goanna.cs.rmit.edu.au/~xiaodong
P. N. Suganthan
School of Electrical and Electronic Engineering
Nanyang Technological University, Singapore
Email: epnsugan@ntu.edu.sg,
Website:
http://www.ntu.edu.sg/home/epnsugan