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

摘要

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.