论文标题
可重新配置的异质平行岛模型
Reconfigurable Heterogeneous Parallel Island Models
论文作者
论文摘要
异质的平行岛模型（HEPIMS）在其岛屿中运行不同的生物启发算法（BAS）。从各种通信拓扑和移民政策进行了对同质PIMS（福利姆斯）的微调（福伊斯），它们在所有岛屿上运行相同的BA，先前的作品引入了HEPIMS，提供了有关霍皮姆人适应最佳的BA的竞争性质量解决方案。这项工作向前迈出了一步，维持赫皮姆人提供的人口多样性，并提高其灵活性，使BA在执行过程中对岛屿进行重新配置：根据他们的表现，岛屿可以在进化过程中动态地替代其基础。使用引入的体系结构（Rechepims）进行的实验被应用于通过使用四种不同的BAS对逆转进行排列的NP硬化问题，即简单的遗传算法，双点交叉遗传算法，差异性进化和自调整粒子粒子的优化。结果表明，新的可重构异质模型计算出比霍皮姆人在霍皮姆（Hepim）上运行最适合的BA的Hepims更好的质量解决方案。
Heterogeneous Parallel Island Models (HePIMs) run different bio-inspired algorithms (BAs) in their islands. From a variety of communication topologies and migration policies fine-tuned for homogeneous PIMs (HoPIMs), which run the same BA in all their islands, previous work introduced HePIMs that provided competitive quality solutions regarding the best-adapted BA in HoPIMs. This work goes a step forward, maintaining the population diversity provided by HePIMs, and increasing their flexibility, allowing BA reconfiguration on islands during execution: according to their performance, islands may substitute their BAs dynamically during the evolutionary process. Experiments with the introduced architectures (RecHePIMs) were applied to the NP-hard problem of sorting permutations by reversals, using four different BAs, namely, simple Genetic Algorithm, Double-point crossover Genetic Algorithm, Differential Evolution, and self-adjusting Particle Swarm Optimization. The results showed that the new reconfigurable heterogeneous models compute better quality solutions than the HePIMs closing the gap with the HoPIM running the best-adapted BA.
