A novel ensemble estimation of distribution algorithm with distribution modification strategies

• Published in: Complex & intelligent systems Vol. 9; no. 5; pp. 5377 - 5416
• Main Authors: Wang, Xiaofei, Li, Yintong, Liang, Yajun, Wu, Bi, Xuan, Yongbo
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2023; Springer Nature B.V; Springer
• Subjects: Algorithms; Archives & records; CEC 2014; CEC 2018; Complexity; Computational Intelligence; Data Structures and Information Theory; Engineering; Estimation of distribution algorithm; Global optimization; Numerical optimization; Original Article; Performance evaluation; Population distribution; Searching; Strategy; CEC 2014; Numerical optimization; Estimation of distribution algorithm; CEC 2018
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-023-00975-y

The canonical estimation of distribution algorithm (EDA) easily falls into a local optimum with an ill-shaped population distribution, which leads to weak convergence performance and less stability when solving global optimization problems. To overcome this defect, we explore a novel EDA variant with an ensemble of three distribution modification strategies, i.e., archive-based population updating (APU), multileader-based search diversification (MSD), and the triggered distribution shrinkage (TDS) strategy, named E 3 -EDA. The APU strategy utilizes historical population information to rebuild the search scope and avoid ill-shaped distributions. Moreover, it continuously updates the archive to avoid overfitting the distribution model. The MSD makes full use of the location differences among populations to evolve the sampling toward promising regions. TDS is triggered when the search stagnates, shrinking the distribution scope to achieve local exploitation. Additionally, the E 3 -EDA performance is evaluated using the CEC 2014 and CEC 2018 test suites on 10-dimensional, 30-dimensional, 50-dimensional and 100-dimensional problems. Moreover, several prominent EDA variants and other top methods from CEC competitions are comprehensively compared with the proposed method. The competitive performance of E 3 -EDA in solving complex problems is supported by the nonparametric test results.