Abstract
The optimization algorithm based on interval analysis is a deterministic global optimization algorithm. However, Solving high-dimensional problems, traditional interval algorithm exposed lots of problems such as consumption of time and memory. In this paper, we give a parallel interval global optimization algorithm based on evolutionary computation. It combines the reliability of interval algorithm with the intelligence and nature scalability of mind evolution computation algorithm, effectively overcomes the shortcomings of Time-Consuming and Memory-Consuming of the traditional interval algorithm. Numerical experiments show that the algorithm has much high efficiency than the traditional interval algorithm.
This work is supported by Shanghai Leading Academic Discipline Project J50103.
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Lei, Y., Chen, S., Yan, Y. (2010). A Novel Parallel Interval Exclusion Algorithm. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds) High Performance Computing and Applications. Lecture Notes in Computer Science, vol 5938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11842-5_29
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DOI: https://doi.org/10.1007/978-3-642-11842-5_29
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