基于公共池自适应迁移策略的并行遗传算法

摘要

The limitations of traditional coarse grained parallel genetic algorithm are analyzed. Aiming at the disadvantages of high communication overhead caused by fixed or invalid migration, a parallel genetic algorithm is proposed, which is suitable for running on multi-core computers and based on the adaptive migration strategy of sharing pool. According to the current state of evolution, the proposed algorithm can adapt to the migration, and the sharing pool is used to dilute the topological structure of the individual exchange between the sub populations. Simulation results for extreme value of complex nonlinear function show that, compared with the traditional parallel genetic algorithm, the algorithm is fast convergence, high precision, early to obtain the evolutionary generation of optimal solution, and significantly improves the parallel efficiency.%分析了传统粗粒度并行遗传算法的局限性，针对其迁移固定不变及无效迁移造成的通信开销大等缺点，将公共池与自适应迁移策略相结合，提出了一种适合在多核计算机上运行的基于公共池自适应迁移策略的并行遗传算法。该算法根据当前的进化状态自适应地进行迁移，并利用公共池淡化了子种群间交换个体时的拓扑结构。对复杂非线性函数求极值的仿真结果表明，该算法与传统并行遗传算法相比，收敛速度快，求解精度高，得到最优解的进化代数提前，并行效率明显提升。