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针对系统约束下的片上网络映射如何建立低功耗和链路负载的多目标优化函数,提出一种基于融合离散粒子群算法(Discrete Particle Swarm Optimization Algorithm,DPSOA)和遗传算法(Genetic Algorithm,GA)的新型映射算法.该算法利用任务节点通信量大小及其连接关系,划分优先级,得到若干较优初始解集;利用离散粒子群算法的快速搜索能力迅速靠近最优解,利用遗传操作中的选择和变异防止算法掉入局部较优解陷阱,以较少的迭代次数完成最优解的寻找.实验结果表明:与遗传算法、粒子群算法和蚁群算法相比,该算法在功耗和链路负载优化上都能达到较好的结果.
Aiming at the multi-objective optimization function of low power consumption and link load in system-on-chip network mapping, a new algorithm based on DPSOA and genetic algorithm (GA) The algorithm uses the size of the task node traffic and its connection relationship to prioritize and obtain a number of better initial solution sets. The fast search capability of discrete particle swarm optimization approaches the optimal solution quickly, Selection and mutation avoidance algorithm fall into the local optimal solution trap and find the optimal solution with fewer iterations.The experimental results show that compared with the genetic algorithm, the particle swarm optimization algorithm and the ant colony algorithm, Link load optimization can achieve better results.