In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication. In this way, it essentially creates a parallel ensemble of optimization methods. At the same time, the system contains a planner that decides which of the available optimization methods should be used to solve the given optimization problem and changes the distribution of such methods during the run of the optimization. Thus, the system effectively solves the problem of online parallel portfolio selection.The proposed system is evaluated in a number of common benchmarks with various problem encodings as well as in two real-life problems — the optimization in recommender systems and the training of neural networks for the control of electric vehicle charging.

中文翻译：

---

异构岛模型及其在推荐系统和电动汽车充电中的应用

在本文中，我们描述了基于进化算法岛模型的并行优化的一般框架。该框架与定期通信并行运行许多优化方法。通过这种方式，它本质上创建了一个并行的优化方法集合。同时，系统包含一个规划器，它决定应该使用哪些可用的优化方法来解决给定的优化问题，并在优化运行期间改变这些方法的分布。因此，该系统有效地解决了在线并行组合选择问题。