In general the ordering of join-operations is quite sensitive and has a devastatingly negative effect on the efficiency of the DBMS.  Scheufele and Moerkotte proved that join-ordering is NP-complete in the general case [1]. The dynamic programming algorithm has a worst case running time, thus for queries with more than 10 joins, it becomes infeasible. To resolve this problem, random strategies are used.  Simulated annealing is an intelligent algorithm of developing very fast. In this paper, we introduce the traditional simulated annealing algorithm through discussing its theory and process, analyzes its shortcoming in detail, simple describe influence of key parameters to simulated annealing algorithm and provided feasible improvement. Especially, in order to avoid the deficiency resulted by the neighbors of state, and make the query optimization support complex non-inner join, the improved algorithm gives a semantics expression of query and a method of constructing the connected join pairs. Experimental results show that the improved algorithm outperforms the original algorithm in terms of both output quality and running time.

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<p class="Abstract">Most contemporary database systems query optimizers exploit System-R&rsquo;s bottom-up dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. The distinguished exceptions are Volcano/Cascades using transforms to generate new plans according to a top-down approach. As recent research has revealed, bottom-up dynamic programming can improve performance with respect to the shape of the join graph and parallelism. However top-down join enumeration dynamic programming method can derive upper bounds for the costs of the plans it generates which is not available to typical bottom-up DP method. In this paper, we propose a comprehensive and practical framework for parallelizing top-down dynamic programming query optimization with complex non-inner join in the multi-core processor architecture, referred as PTDhyp. We have implemented such a search strategy and experimental results show that can improve optimization time effective compared to known existing algorithms.</p>

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