Pool Model is an asynchronous, loosely coupled distributed evolutionary algorithm (dEA) design architecture. However, the classical Pool Model face some design problems, such as population control, work redundancy, rough selection/replacement strategies, and unreliable connections, etc. In this paper, a novel distributed pool evolutionary algorithm (EA) model with buffer (PEAB) is proposed. PEAB can solve the inherent problems of the Pool Model by using the buffer setting, the Reunion mechanism, and the Migration in Pool (MP) strategy. Besides, PEAB provides stronger population control and more global population selection/replacement strategies. In the experimental part, we compared PEAB with another Pool Model named EvoSpace using a common benchmark. The experiments showed that the convergence rate of PEAB is 59.7% faster than that of EvoSpace under the respective fastest conditions. PEAB also has a faster reception rate of the first generation and stronger population control. Besides, this paper also tests and analyzes the scalability of PEAB using two other benchmarks. The overall trend of the experiment results suggested that PEAB would be faster with more Workers. Last but not least, this paper studies the effect of the MP strategy on the performance of PEAB, and the results showed that the MP strategy can effectively improve the convergence efficiency.

池模型是一种异步、松散耦合的分布式进化算法 (dEA) 设计架构。然而，经典的池模型面临一些设计问题，如种群控制、工作冗余、粗选/替换策略和不可靠的连接等。 本文提出了一种新的带缓冲区的分布式池进化算法（EA）模型（PEAB）被提议。PEAB 可以通过使用缓冲区设置、Reunion 机制和Migration in Pool (MP) 策略来解决池模型的固有问题。此外，PEAB 提供了更强的种群控制和更全局的种群选择/替换策略。在实验部分，我们使用通用基准将 PEAB 与另一个名为 EvoSpace 的池模型进行了比较。实验表明，PEAB的收敛速度为59。在各自最快的条件下，比 EvoSpace 快 7%。PEAB还具有更快的第一代接收速度和更强的人口控制。此外，本文还使用另外两个基准测试和分析了 PEAB 的可扩展性。实验结果的总体趋势表明，如果有更多的工人，PEAB 会更快。最后，本文研究了 MP 策略对 PEAB 性能的影响，结果表明 MP 策略可以有效提高收敛效率。实验结果的总体趋势表明，如果有更多的工人，PEAB 会更快。最后，本文研究了 MP 策略对 PEAB 性能的影响，结果表明 MP 策略可以有效提高收敛效率。实验结果的总体趋势表明，如果有更多的工人，PEAB 会更快。最后，本文研究了 MP 策略对 PEAB 性能的影响，结果表明 MP 策略可以有效提高收敛效率。