Quantum-behaved Gravitational Search Algorithm (QGSA), a novel variant of GSA, is a global convergent algorithm whose search strategy makes it own stronger global search ability than classical GSA over unimodal problems. Like some other evolutionary optimization technique, premature convergence in the QGSA is also. In this paper, we propose a new kind of potential well evaluation, with a center which is weighted average of all Kbests based on their masses and distances. As results shown it helps the agent to escape the sub-optima more easily. The improved QGSA is evaluated on some benchmark function and results are reported.
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