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首页> 外文期刊>Applied Soft Computing >An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs
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An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs

机译：借助区域多目标进化算法的有效的多目标进化算法模型：VIPMOEA

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Division of the evolutionary search among multiple multi-objective evolutionary algorithms (MOEAs) is a recent advantage in MOEAs design, particularly in effective parallel and distributed MOEAs. However, most these algorithms rely on such a central (re) division that affects the algorithms' efficiency. This paper first proposes a local MOEA that searches on a particular region of objective space with its novel evolutionary selections. It effectively searches for Pareto Fronts (PFs) inside the given polar-based region, while nearby the region is also explored, intelligently. The algorithm is deliberately designed to adjust its search direction to outside the region - but nearby - in the case of a region with no Pareto Front. With this contribution, a novel island model is proposed to run multiple forms of the local MOEA to improve a conventional MOEA (e.g. NSGA-II or MOEA/D) running along - in another island. To dividing the search, a new division technique is designed to give particular regions of objective space to the local MOEAs, frequently and effectively. Meanwhile, the islands benefit from a sophisticated immigration strategy without any central (re) collection, (re) division and (re) distribution acts. Results of three experiments have confirmed that the proposed island model mostly outperforms to the clustering MOEAs with similar division technique and similar island models on DTLZs. The model is also used and evaluated on a real-world combinational problem, flexible logistic network design problem. The model definitely outperforms to a similar island model with conventional MOEA (NSGA-II) used in each island.
机译：在多个多目标进化算法（MOEA）之间进行进化搜索的划分是MOEA设计中的一项最新优势，特别是在有效的并行和分布式MOEA中。但是，大多数这些算法都依赖于这样的中央（重新）划分，这会影响算法的效率。本文首先提出了一种局部MOEA，它以新颖的进化选择在目标空间的特定区域进行搜索。它可以有效地搜索给定基于极点的区域内的帕累托阵线（PFs），同时也可以智能地探索该区域附近的帕累托阵线。对于没有Pareto Front的区域，该算法经过精心设计，可以将其搜索方向调整到该区域之外（但附近）。在此贡献的基础上，提出了一种新颖的岛屿模型，该模型可以运行多种形式的本地MOEA，以改善沿另一个岛屿运行的常规MOEA（例如NSGA-II或MOEA / D）。为了划分搜索范围，设计了一种新的划分技术，可以频繁有效地为本地MOEA提供目标空间的特定区域。同时，这些岛屿得益于复杂的移民策略，而无需任何集中的（重新）收集，（重新）划分和（重新）分配行为。三个实验的结果证实，在类似的分割技术和DTLZ上类似的岛模型下，所提出的岛模型在性能上要优于聚类MOEA。该模型还用于实际组合问题，灵活的物流网络设计问题并进行了评估。该模型绝对优于在每个岛中使用传统MOEA（NSGA-II）的类似岛模型。

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