Distributed and Expensive Evolutionary Constrained Optimization With On-Demand Evaluation

• Published in: IEEE transactions on evolutionary computation Vol. 27; no. 3; pp. 671 - 685
• Main Authors: Wei, Feng-Feng, Chen, Wei-Neng, Li, Qing, Jeon, Sang-Woon, Zhang, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Constraints; Differential evolution (DE); distributed optimization; Evolutionary algorithms; Evolutionary computation; Industries; on-demand evaluation; Optimization; Organizations; Predictive models; Sociology; surrogate-assisted evolutionary algorithm (SAEA)
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2022.3177936

Expensive optimization problems (EOPs) are common in industry and surrogate-assisted evolutionary algorithms (SAEAs) have been developed for solving them. However, many EOPs have not only expensive objective but also expensive constraints, which are evaluated through distributed ways. We define this kind of EOPs as distributed expensive constrained optimization problems (DECOPs). The distributed characteristic of DECOPs leads to the asynchronous evaluation of both objective and constraints. Though some researchers have studied the asynchronous evaluation of objectives, the asynchronous evaluation of constraints has not gained much attention. Therefore, this article gives a formal formulation of DECOPs and proposes a distributed evolutionary constrained optimization algorithm with on-demand evaluation (DEAOE). DEAOE can adaptively evolve different constraints in an asynchronous way through the on-demand evaluation strategy. The on-demand evaluation works from two aspects to improve the population convergence and diversity. From the aspect of individual selection, a joint sample selection strategy is adopted to determine which candidates are promising. From the aspect of constraint selection, an infeasible-first evaluation strategy is devised to judge which constraints need to be further evolved. Extensive experiments and analyses on benchmark functions and engineering problems demonstrate that DEAOE has better performance and higher efficiency compared to centralized state-of-the-art SAEAs.