The Estimation Distribution Algorithm (EDA) is an evolutionary algorithm that uses probabilistic models to create candidate solutions. Previous researchers have suggested various hybrid methods to avoid the premature convergence of EDA. This research conducts a comparative study between several variations of hybridization in EDA with regards to the descriptive statistics in the objective values.

This study also proposes a new hybrid approach, named Adapted EDA (AEDA), by adapting the structure of EDA by adding a lottery procedure, an elitism strategy, and a neighborhood search. The proposed AEDA, several hybridizations of EDA, and Genetic Algorithm (GA) plus Tabu Search (TS) are applied to the facility layout design in manufacture – Enhanced Facility Layout Problem (EFLP) – to analyze their solutions. The hybrid EDAs that are being compared are EDA plus GA (EDAGA), EDA plus Particle Swarm Optimization (EDAPSO), the combination of EDAPSO plus TS (EDAhybrid), and AEDA. The experimental results show that the AEDA can significantly improves the solution quality in solving all the EFLP instances compared to other algorithms.

估计分布算法 (EDA) 是一种进化算法，它使用概率模型来创建候选解决方案。以前的研究人员提出了各种混合方法来避免 EDA 的过早收敛。本研究针对 EDA 中的几种杂交变体在客观值中的描述性统计方面进行了比较研究。

本研究还提出了一种新的混合方法，称为自适应 EDA (AEDA)，通过添加抽签程序、精英策略和邻域搜索来调整 EDA 的结构。提议的 AEDA、EDA 的几种混合以及遗传算法 (GA) 加禁忌搜索 (TS) 应用于制造中的设施布局设计——增强型设施布局问题 (EFLP)——以分析它们的解决方案。正在比较的混合 EDA 是 EDA 加 GA (EDAGA)、EDA 加粒子群优化 (EDAPSO)、EDAPSO 加 TS 的组合 (EDAhybrid) 和 AEDA。实验结果表明，与其他算法相比，AEDA 可以显着提高求解所有 EFLP 实例的解质量。