  With the growing popularity, the number of data sources and the amount of
data has been growing very fast in recent years. The distribution of
operational data on disperse data sources impose a challenge on processing user
queries. In such database systems, the database relations required by a query
to answer may be stored at multiple sites. This leads to an exponential
increase in the number of possible equivalent or alternatives of a user query.
Though it is not computationally reasonable to explore exhaustively all
possible query plans in a large search space, thus a strategy is requisite to
produce optimal query plans in distributed database systems. The query plan
with most cost-effective option for query processing is measured necessary and
must be generated for a given query. This paper attempts to generate such
optimal query plans using a parameter less optimization technique
Teaching-Learner Based Optimization(TLBO). The TLBO algorithm was experiential
to go one better than the other optimization algorithms for the multi objective
unconstrained and constrained benchmark problems. Experimental comparisons of
TLBO based optimal plan generation with the multiobjective genetic algorithm
based distributed query plan generation algorithm shows that for higher number
of relations, the TLBO based algorithm is able to generate comparatively better
quality Top K query plans.
