A Parallel Self-adaptive Subspace Searching Algorithm for Solving Dynamic Function Optimization Problems

Authors:
Yan Li;Zhuo Kang;Lishan Kang
Affiliations:
Computation Center, Wuhan University, Wuhan, China 430072;Computation Center, Wuhan University, Wuhan, China 430072;School of Computer Science, China University of Geosciences(Wuhan), Wuhan, China 430074
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

In this paper, a parallel self-adaptive subspace searching algorithm is proposed for solving dynamic function optimization problems. The new algorithm called DSSSEA uses a re-initialization strategy for gathering global information of the landscape as the change of fitness is detected, and a parallel subspace searching strategy for maintaining the diversity and speeding up the convergence in order to find the optimal solution before it changes. Experimental results show that DSSSEA can be used to track the moving optimal solutions of dynamic function optimization problems efficiently.