Today, more and more complex tasks are emerging. To finish these tasks within a reasonable\ntime, using the complex embedded system which has multiple processing units is necessary.\nHardware/software partitioning is one of the key technologies in designing complex embedded\nsystems, it is usually taken as an optimization problem and be solved with different optimization\nmethods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied\nand have the advantages of strong robustness and excellent global search ability. Due to the high\ncomplexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve\nthe problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied\nto hardware/software partitioning. In order to improve the performance of the BSO, we analyzed\nits optimization process when solving the hardware/software partitioning problem and found the\ndisadvantages in terms of the clustering method and the updating strategy. Then we proposed the\nimproved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting\nthe cluster points and improved the updating strategy by decreasing the number of updated individuals\nin each iteration. Based on the simulation methods which are usually used to evaluate the performance\nof the hardware/software partitioning algorithms, we generated eight benchmarks which represent\ntasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and\ntwo improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the\nhighest quality within the shortest running time among these algorithms.
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