进化算法
渡线
计算机科学
替代模型
水准点(测量)
稳健性(进化)
数学优化
人口
差异进化
粒子群优化
最优化问题
多目标优化
人工智能
机器学习
算法
数学
基因
生物化学
社会学
人口学
化学
大地测量学
地理
作者
Huixiang Zhen,Wenyin Gong,Ling Wang,Fei Ming,Zuowen Liao
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:53 (4): 2368-2379
被引量:8
标识
DOI:10.1109/tcyb.2021.3118783
摘要
Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving complex and computationally expensive optimization problems. However, most of the existing algorithms converge slowly in the later stage. This article proposes a novel two-stage data-driven evolutionary optimization (TS-DDEO) that meets the requirements of early exploration and later exploitation. In the first stage, a surrogate-assisted hierarchical particle swarm optimization method is used to find a promising area from the entire search space. In the second stage, we propose a best-data-driven optimization (BDDO) method with a strong exploitation ability to accelerate the optimization process. BDDO has a real-time update mechanism for the surrogate model and population and uses a predefined number of ranking-top solutions to update population and surrogates. BDDO combines three surrogate-assisted evolutionary sampling strategies: 1) surrogate-assisted differential evolution sampling; 2) surrogate-assisted local search; and 3) a surrogate-assisted full-crossover (FC) strategy which is proposed to integrate existing best genotypes in the population. Experiments and analysis have validated the effectiveness of the two-stage framework, the BDDO method, and the FC strategy. Moreover, the proposed algorithm is compared with five state-of-the-art SAEAs on high-dimensional benchmark functions. The result shows that TS-DDEO performs better both in effectiveness and robustness.
科研通智能强力驱动
Strongly Powered by AbleSci AI