A reliable memory less parallel method for interval global optimization

Large memory cost, low speed of convergence and less accuracy of bounding objective function are three main problem in finding the global optimization. M. sun gives an algorithm that costs less memory [1]. But this method cannot always find the global optimization. Based on this algorithm, this paper presents a more reliable method by increasing accuracy of interval computation. This algorithm has more reliability. Compared to M. Sun´s memory less method, it can find the global optimization for most test function. This method does not maintain a list that it has les memory cost. Experiments will show advances of our method. Global optimization problem is NP-hard. When the objective function is complex, parallel method is used to quickly find the global optimization. The experiments are running on the “ZiQiang 3000” cluster of Shanghai High Education Grid-e-Grid.