Island Model (IM) is an alternative often used to parallel Evolutionary Algorithms (EA). In IM, the population is distributed between islands that evolve their solutions in parallel, connected by a topology. Periodically, solutions migrate between islands according to a migration policy. The IM can be seen as an ideal structure to combine different algorithms to be used in an organized and cooperative way to solve a problem. Motivated by the number and distinction of EAs proposed in the last decades, in terms of performance and evolutionary behavior, this work proposes a hybrid configuration for IM, called Stigmergy Island Model (Stgm-IM), inspired by the natural phenomenon of stigmergy. Stigmergy is present in groups of some social species, and, by it, their agents organize themselves and maintain a level of cooperation through indirect communication. The Stgm-IM was evaluated regarding its evolutionary behavior and its performance on a benchmark suite of fifteen optimization problems, showing expected results.

岛屿模型（IM）是经常用于并行进化算法（EA）的替代方法。在IM中，种群分布在各个岛之间，这些岛通过拓扑结构并行地扩展其解决方案。解决方案会根据迁移策略定期在孤岛之间迁移。IM可以看作是一种理想的结构，可以将不同的算法结合在一起以解决问题的方式以有组织的协作方式使用。受近几十年来在性能和进化行为方面提出的EA的数量和区别的影响，这项工作提出了一种IM的混合配置，称为Stigmergy Island Model（Stgm-IM），其灵感来自于自然的电tig散现象。Stigmergy存在于一些社会物种的群体中，据此，他们的代理商通过间接沟通来组织自己并保持合作水平。在15个优化问题的基准套件上评估了Stgm-IM的进化行为和性能，显示了预期的结果。