Hybrid parallel, evolutionary algorithms for constrained optimization utilizing PC clustering
Hybrid parallel, evolutionary algorithms for constrained optimization utilizing PC clustering
复制标题
利用 PC 集群进行约束优化的混合并行进化算法
DOI：
10.1109/cec.2001.934360
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Jong
中科院分区：
文献类型：
--
作者：
Chi;Kui;Jong
本文提出了一种混合并行进化算法（EA）利用PC集群环境来解决约束数值优化问题。在所提出的并行结构中，粗粒度的并行EA（PEAs）被牵连在上层和细粒度的PEAs被用于在较低的水平。设计有效的进化算法是为了在探索和利用之间取得适当的平衡。这种平衡可以通过扩散率和最佳个体的迁移来控制。在混合结构中，低层粗粒度结构的扩散率高，而高层全局结构的扩散率低。通过将个体划分为几个群体并在群体之间迁移个体来促进多样性。通过使用大量的处理器，优化性能以及计算时间得到了改善。仿真结果表明，混合并行进化算法使用所提出的结构有更好的性能在约束数值优化问题比粗粒度，或细粒度的并行进化算法，这是专用的并行化方法在以前的工作。
This paper proposes a hybrid parallelization of evolutionary algorithms (EAs) utilizing PC clustering environments to solve constrained numerical optimization problems. In the proposed parallel structure, the coarse-grained parallel EAs (PEAs) were implicated in upper level and the fine-grained PEAs were used in lower level. The design of effective evolutionary algorithms (EAs) is to obtain a proper balance between exploration and exploitation. The balance can be controlled by the spread rate and the migration of the best individuals. In the hybrid structure, the spread rate is high in lower level coarse-grained structure and low in upper level globally structure. The diversity is promoted by dividing individuals to several groups and migrating individual between them. By utilizing large number of processors, the optimization performance as well as the computation time were improved. Simulation results indicate that hybrid parallel EAs using the proposed structure have better performance in constrained numerical optimization problems than coarse-grained, or fine-grained parallel EAs, which are dedicated parallelization methods in previous work.