In the co-design process of hardware/software (HW/SW) system, especially for large and complicated embedded systems, HW/SW partitioning is a challenging step. Among different heuristic approaches, particle swarm optimization (PSO) has the advantages of simple implementation and computational efficiency, which is suitable for solving large-scale problems. This paper presents a conformity particle swarm optimization with fireworks explosion operation (CPSO-FEO) to solve large-scale HW/SW partitioning. First, the proposed CPSO algorithm simulates the conformist mentality from biology research. The CPSO particles with psychological conformist always try to move toward a secure point and avoid being attacked by natural enemy. In this way, there is a greater possibility to increase population diversity and avoid local optimum in CPSO. Next, to enhance the search accuracy and solution quality, an improved FEO with new initialization strategy is presented and is combined with CPSO algorithm to search a better position for the global best position. This combination can keep both the diversified and intensified searching. At last, the experiments on benchmarks and large-scale HW/SW partitioning demonstrate the efficiency of the proposed algorithm.

中文翻译：

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大规模软硬件协同设计系统的高效粒子群优化

在硬件/软件（HW/SW）系统的协同设计过程中，特别是对于大型复杂的嵌入式系统，HW/SW划分是一个具有挑战性的步骤。在不同的启发式方法中，粒子群优化（PSO）具有实现简单、计算效率高等优点，适用于解决大规模问题。本文提出了一种整合粒子群优化与烟花爆炸操作 (CPSO-FEO) 来解决大规模 HW/SW 分区问题。首先，所提出的 CPSO 算法模拟了生物学研究中的墨守成规心态。心理顺从的CPSO粒子总是试图向安全点移动，避免受到天敌的攻击。这样，在 CPSO 中增加种群多样性并避免局部最优的可能性更大。下一个，为了提高搜索精度和求解质量，提出了一种改进的具有新初始化策略的FEO，并结合CPSO算法在全局最佳位置中搜索更好的位置。这种组合可以保持多样化和强化搜索。最后，基准测试和大规模硬件/软件划分的实验证明了所提算法的有效性。