Competition on

Large Scale Global Optimization

2012 IEEE World Congress on Computational Intelligence (CEC@WCCI-2012)

June 10-15, 2012, Brisbane, Australia

Introduction
The competition is organized in company with the Special Session on Evolutionary Computation for Large Scale Global Optimization. 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://staff.ustc.edu.cn/~ketang/cec2012/lib/lsgo_benchmark.zip. Researchers are welcome to apply any kind of approach to the test suite. Interested participants are welcome to report their approaches and results in a paper and submit it to the above mentioned special session via the online submission system of WCCI-2012. Alternatively, the results can also be submitted in the form of a brief technical report, which should be sent to Ke Tang directly. Submissions in both forms will be considered together for the final evaluation of the competition.


Test suite for our companion competition is available here. We provide three versions (matlab, Java and C++) of the test suites (Thanks Mr. Wenxiang Chen for providing the C++ version).

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, 2009.

To those who may participate in the competition: Please could you inform Ke Tang about your participation, so that we can update you about any correction of bugs or extension of the deadline.


Important Dates

For participants who plan to submit a paper to the Special Session on Evolutionary Computation for Large Scale Global Optimization:
Paper Submission: December 19, 2011
Acceptance Notification: February 20, 2012
Final Manuscript Due: April 2, 2012

For other participants:
Results Submission Deadline: April 30, 2012
Note: The results should be send to ketang@ustc.edu.cn via email.


Competition 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

Zhenyu Yang
Department of Computer Science and Technology
East China Normal University, Shanghai, China
Email: zhyuyang@mail.ustc.edu.cn

Thomas Weise
Nature Inspired Computation and Applications Laboratory (NICAL)
School of Computer Science and Technology
University of Science and Technology of China, Hefei, Anhui, China
Email: tweise@gmx.de, Website: http://www.it-weise.de


Related Events


Preliminary results for your reference
Algorithms Quality F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
DECC-G Best 1.63e-07 1.25e+03 1.20e+00 7.78e+12 1.50e+08 3.89e+06 4.26e+07 6.37e+06 2.66e+08 1.03e+04
Median 2.86e-07 1.31e+03 1.39e+00 1.51e+13 2.38e+08 4.80e+06 1.07e+08 6.70e+07 3.18e+08 1.07e+04
Worst 4.84e-07 1.40e+03 1.68e+00 2.65e+13 4.12e+08 7.73e+06 6.23e+08 9.22e+07 3.87e+08 1.17e+04
Mean 2.93e-07 1.31e+03 1.39e+00 1.70e+13 2.63e+08 4.96e+06 1.63e+08 6.44e+07 3.21e+08 1.06e+04
Std 8.62e-08 3.26e+01 9.73e-02 5.37e+12 8.44e+07 8.02e+05 1.37e+08 2.89e+07 3.38e+07 2.95e+02
DECC-G* Best 6.33e-12 4.21e+02 2.23e-08 9.76e+11 2.08e+08 5.07e-03 3.45e+06 2.79e+07 1.18e+07 2.33e+03
Median 8.97e-12 4.43e+02 3.30e-08 1.96e+12 2.49e+08 8.85e-03 1.04e+07 4.07e+07 1.41e+07 2.49e+03
Worst 1.31e-11 4.57e+02 4.16e-08 5.39e+12 2.72e+08 1.40e-02 2.28e+07 1.50e+08 1.77e+07 2.64e+03
Mean 8.81e-12 4.42e+02 3.30e-08 2.29e+12 2.45e+08 8.77e-03 1.10e+07 6.14e+07 1.41e+07 2.48e+03
Std 1.49e-12 9.94e+00 5.20e-09 9.97e+11 1.64e+07 2.46e-03 5.44e+06 3.24e+07 1.39e+06 7.63e+01
MLCC Best 0.00e+00 1.73e-11 1.28e-13 4.27e+12 2.15e+08 5.85e+06 4.16e+04 4.51e+04 8.96e+07 2.52e+03
Median 0.00e+00 6.43e-11 1.46e-13 1.03e+13 3.92e+08 1.95e+07 5.15e+05 4.67e+07 1.24e+08 3.16e+03
Worst 3.83e-26 1.09e+01 1.86e-11 1.62e+13 4.87e+08 1.98e+07 2.78e+06 9.06e+07 1.46e+08 5.90e+03
Mean 1.53e-27 5.57e-01 9.88e-13 9.61e+12 3.84e+08 1.62e+07 6.89e+05 4.38e+07 1.23e+08 3.43e+03
Std 7.66e-27 2.21e+00 3.70e-12 3.43e+12 6.93e+07 4.97e+06 7.37e+05 3.45e+07 1.33e+07 8.72e+02
Algorithms Quality F11 F12 F13 F14 F15 F16 F17 F18 F19 F20
DECC-G Best 2.06e+01 7.78e+04 1.78e+03 6.96e+08 1.09e+04 5.97e+01 2.50e+05 5.61e+03 1.02e+06 3.59e+03
Median 2.33e+01 8.87e+04 3.00e+03 8.07e+08 1.18e+04 7.51e+01 2.89e+05 2.30e+04 1.11e+06 3.98e+03
Worst 2.79e+01 1.07e+05 1.66e+04 9.06e+08 1.39e+04 9.24e+01 3.26e+05 4.71e+04 1.20e+06 5.32e+03
Mean 2.34e+01 8.93e+04 5.12e+03 8.08e+08 1.22e+04 7.66e+01 2.87e+05 2.46e+04 1.11e+06 4.06e+03
Std 1.78e+00 6.87e+03 3.95e+03 6.07e+07 8.97e+02 8.14e+00 1.98e+04 1.05e+04 5.15e+04 3.66e+02
DECC-G* Best 5.82e-08 6.16e+01 3.78e+02 2.46e+07 3.62e+03 7.04e-08 8.09e+01 8.37e+02 9.90e+05 2.83e+03
Median 7.52e-08 7.72e+01 5.40e+02 2.90e+07 3.88e+03 1.04e-07 1.03e+02 1.08e+03 1.15e+06 3.21e+03
Worst 8.79e-01 1.19e+02 7.55e+02 3.56e+07 4.25e+03 2.18e+00 1.33e+02 1.53e+03 1.23e+06 6.23e+03
Mean 3.52e-02 7.87e+01 5.50e+02 2.91e+07 3.88e+03 4.01e-01 1.03e+02 1.08e+03 1.14e+06 3.33e+03
Std 1.76e-01 1.41e+01 9.78e+01 2.91e+06 1.76e+02 6.59e-01 1.38e+01 1.61e+02 5.85e+04 6.63e+02
MLCC Best 1.96e+02 2.42e+04 1.01e+03 2.62e+08 5.30e+03 2.08e+02 1.38e+05 2.51e+03 1.21e+06 1.70e+03
Median 1.98e+02 3.47e+04 1.91e+03 3.16e+08 6.89e+03 3.95e+02 1.59e+05 4.17e+03 1.36e+06 2.04e+03
Worst 1.98e+02 4.25e+04 3.47e+03 3.77e+08 1.04e+04 3.97e+02 1.86e+05 1.62e+04 1.54e+06 2.34e+03
Mean 1.98e+02 3.49e+04 2.08e+03 3.16e+08 7.11e+03 3.76e+02 1.59e+05 7.09e+03 1.36e+06 2.05e+03
Std 6.98e-01 4.92e+03 7.27e+02 2.77e+07 1.34e+03 4.71e+01 1.43e+04 4.77e+03 7.35e+04 1.80e+02
  1. DECC-G: The algorithm proposed in:
    • Zhenyu Yang, Ke Tang and X. Yao: "Large Scale Evolutionary Optimization Using Cooperative Coevolution", Information Sciences, 178(15):2985-2999, August 2008.
      Available as a PDF here.
    The parameter group size was set to s=100.
  2. DECC-G*: The same as DECC-G, except that the grouping structure was used as prior knownledge. The parameter group size was set to s=50, and the adaptive weighting strategy of DECC-G was not used.
    Note: DECC-G* is only used for reference purpose, please do not use the grouping structure information to design your own algorithm.
  3. MLCC: The algorithm proposed in:
    • Zhenyu Yang, Ke Tang and Xin Yao: "Multilevel Cooperative Coevolution for Large Scale Optimization", in Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC2008), Hongkong, China, 2008, pp. 1663-1670.
      Available as a PDF here
  4. The results were collected when all the 3 million FEs were used up. The results provided here will NOT be included in the final ranking of the competition.