are a few work focusing on a balance between two objectives. Taheri et al. [@bio] proposed a bi-objective optimization model minimizing both the execution time of a batch of jobs and the transfer time required to deliver their required data for hybrid clouds, and used a PSO-based approach to find the relevant pareto frontier. They do not take the finance expenditure into account. V. A. Leena et al. [@GABIO] proposed an algorithm for the simultaneous optimization of execution time and cost, in hybrid cloud, by determining whether tasks have to be scheduled to either the private cloud or the public cloud, employing a genetic algorithm. This work does not consider the mapping between VMs and PMs in the private cloud. The VM instances of the same type are homogeneous, which is not true in a heterogeneous data center. Wang et al. [@AsQ] proposed a dual-objective multi-dimension multichoice knapsack problem to model the task scheduling with the two objectives of minimizing the cost and minimizing the makespan in hybrid clouds. As the high complexity of solving the problem, the adaptive scheduling algorithm (AsQ) was proposed. AsQ used MAX-MIN strategy [@MAXMIN] to schedule task in private cloud and outsourcing the smallest task 