Some massively parallel algorithms from nature
We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes.
Key wordsevolutionary computation parallel algorithm imitating nature domain decomposition knowledge discovery in databases
CLC numberTP 301
Unable to display preview. Download preview PDF.
- Southwell R V.Relaxation Methods in Theoretical Physics. Oxford Oxford, University Press, 1946.Google Scholar
- Kang Li-shan.Parallel Algorithms and Domain Decomposition. Wuhan: Wuhan University Press, 1987 (Ch).Google Scholar
- Kang Li-shan, Xie Yun, You Shi-yong,et al. Non-Numerical Parallel Algorithms: (I) Simulated Annealing. Beijing: China Academic Publisher, 1994 (Ch).Google Scholar
- Kang Li-shan, Macleod I, Chen Lu-juan,et al. Evolutionary Computation Algorithms for Solving Traveling Salesman Problems, Intelligent Computer Theory’94. Beijing: Qinghua University Press, 1994, 23–35 (Ch).Google Scholar
- Kang Li-shan, Li Yan, Kang Zhuo,et al. Asynchronous Parallel Evolutionary Algorithms for Optimizations.Proceedings of 2001 International Symposium on Distributed Computing and Application to Business, Engineering and Science. Wuhan: Hubei Science and Technology Press, 2001, 1–4 (Ch).Google Scholar
- Kang Li-shan, Kang Zhuo, Li Yan,et al. Asynchronous Parallelization of Guo’s Algorithm for Function Optimization.Procedings of Evolutionary Computation Congress, July 16–19, 2000, Pisataway NJ: IEEE Press, 783–789.Google Scholar
- Michalewicz Z, Esguvel S. The Spirit of Evolutionary Algorithms.Journal of Computing and Information Technology, 1999,7: 1–18.Google Scholar
- Pan Zheng-jun, Kang Li-shan, He Jun,et al. An Evolutionary Approach to Adaptive Model-Building, In: X. Yao ed.Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag, 1995,956: 236–244.Google Scholar
- Cao Hong-qing, Kang Li-shan, Michalewicz Z,et al. A Two-Level Evolutionary Algorithm for Modeling System of Ordinary Differential Equations. In: David E, Iba, Hitoshi, et al (editors),Genetic Programming 1998:Proceedings of the Third Annual Conference. University of Wisconsin, Madison: Morgan Kaufmann Publishers, 17–22, 1998.Google Scholar