Large Scale Global Optimization Challenge
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1 Results of the Large Scale Global Optimization Challenge at 2010 IEEE World Congress on Computational Intelligence Ke Tang, Thomas Weise, Zhenyu Yang Xiaodong Li P. N. Suganthan Nature Inspired Computation and Applications Laboratory School of Computer Science and School of Electrical and Electronic School of Computer Science and Technology Information Technology Engineering University of Science and Technology of China RMIT University Nanyang Technological University Hefei, Anhui, China Australia Singapore 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang / Thomas Weise Slide 1
2 Contents Introduction Challenge Participants Results Winners Summary 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang / Thomas Weise Slide 2
3 Introduction Numerical optimization one of the most important disciplines in optimization Number of decision variables = scale of a problem Large-Scale problems are challenging for optimization algorithms Runtime often quickly increases with scale Solution quality (objective value) often quickly decreases with scale Variable interactions (non-separability) makes problems hard Challenge: Find efficient optimization algorithm for large-scale problems under realistic separability assumptions 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 3
4 Large-Scale Global Optimization Challenge Scale: 1000 dimensions 20 benchmark functions: separable functions 5 single-group -non-separable functions group -non-separable functions 50 -group -non-separable functions fully non-separable functions Separable functions rotated by random rotation matrix non-separable Functions shifted by random vector optima center of search space Groups are not continuous fractions of solution vectors: instead random elements are grouped together 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 4
5 Large-Scale Global Optimization Challenge FEs= 1:2e5 FEs= 6:0e5 FEs= 3:0e6 FEs= 1:2e5 FEs= 6:0e5 FEs= 3:0e6 FEs= 1:2e5 FEs= 6:0e5 FEs= 3:0e6 F 1 F 2 F 3 F 4 F 5 F 6 F 7 Best 0.00e+00 x.xxe+xx x.xxe+xx x.xxe+xx x.xxe+xx x.xxe+xx x.xxe+xx Best Best F 8 F 9 F 10 F 11 F 12 F 13 F 14 Best Best Best F 15 F 16 F 17 F 18 F 19 F 20 Best Best Best 20 * 3 * 5 = 300 competition categories 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 5
6 Large-Scale Global Optimization Challenge For each of the 300 categories, we apply the Formula 1 point system 1 The participant with the highest score sum wins In all categories holds: the smaller the measured value, the better (small standard deviations, e.g., mean more reliable performance) 1 [ ] Place Points Table 1: Formula 1 point system 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 6
7 Participants C-7136 C-7273 C-7306 C-7330 C-7392 C-7597 C-7938 C-7406 Differential Ant-Stigmergy Algorithm Sequential DE Enhanced by Neighborhood Search Two-stage based Ensemble Optimization MA-SW-Chains: Memetic Algorithm Based on Local Search Chains Self-adaptive Differential Evolution Algorithm Cooperative Co-evolution with Delta Grouping Dynamic Multi-Swarm Particle Swarm Optimizer with Subregional Harmony Search Locust Swarms for Large Scale Global Optimization C-7939-A Classic Differential Evolution Algorithm 0.0 C-7939-B Classic Differential Evolution Algorithm IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang / Thomas Weise Slide 7
8 Results: Scores per Problem Class 1 in the 3 separable functions C 7136 C 7273C 7306C 7330C 7392 C 7597 C 7938 C 7406 C 7939 A C 7939 B 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 8
9 Results: Scores per Problem Class 2 in the 5 single-group -non-separable functions C 7136 C 7273C 7306C 7330C 7392C 7597 C 7938 C 7406 C 7939 A C 7939 B 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 9
10 Results: Scores per Problem Class 3 in the 5 -group -non-separable functions C 7136 C 7273C 7306C 7330C 7392C 7597 C 7938C 7406 C 7939 A C 7939 B 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 10
11 Results: Scores per Problem Class 4 in the 5 -group -non-separable functions C 7136C 7273 C 7306C 7330 C 7392C 7597 C 7938 C 7406 C 7939 A C 7939 B 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 11
12 Results: Scores per Problem Class 5 in the 2 fully non-separable functions C 7136 C 7273C 7306C 7330 C 7392 C 7597 C 7938 C 7406 C 7939 A C 7939 B 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 12
13 Results: Overall Scores 6000 fully non separable D/m group D/2m group 5000 single group fully separable C 7136 C 7273 C 7306 C 7330 C 7392 C 7597 C 7938 C 7406 C 7939 AC 7939 B 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 13
14 Winners C-7330 C Points MA-SW Chains 4323 Points 2-Stage Ensemble C Points Multi-PSO+ Harmony C Points Differential Ants 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang / Thomas Weise Slide 14
15 Summary Nine teams from nine countries (four continents) Most results are excellent and far superior to previous/traditional methods (such as C-7939) Clear winner: Memetic Algorithm based on Local Search Chains Places 2 to 4 very close: - Two-stage based Ensemble Optimization - Dynamic Multi-Swarm Particle Swarm Optimizer with Subregional Harmony Search - Differential Ant-Stigmergy Algorithm 2010 IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang (ketang@ustc.edu.cn) / Thomas Weise (tweise@gmx.de) Slide 15
16 Thank you very much for your attention! Any questions? IEEE World Congress on Evolutionary Intelligence, Barcelona, Spain, Ke Tang / Thomas Weise Slide 16
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