Data-Driven State Transition Algorithm for Fuzzy Chance-Constrained Dynamic Optimization

Many actual industrial production processes are dynamic and uncertain. When uncertain information are described by subjective experience and experts' knowledge based on scanty or vague information, fuzzy uncertainty exists. Fuzzy chance-constrained dynamic programming are applicable to industrial production modeling accompanied by fuzzy uncertainty and dynamics, where constraints need not or cannot be completely satisfied. In this article, a fuzzy chance-constrained dynamic optimization (FCCDO) formulation on the basis of credibility theory is established, in which, the credibility is used to measure the fuzzy uncertainty level of constraints. To solve the FCCDO problem (FCCDOP), an improved fuzzy simulation technique based on Hammersley sequence sampling is raised to transform fuzzy chance constraints to their deterministic equivalents, and then a data-driven state transition algorithm (DDSTA) using deep neural networks (DNNs) is put forward to achieve a stable, global and robust optimization performance. Finally, the successful applications of the FCCDO method to industrial studies demonstrate its advantages.

Automated summary

This article presents a fuzzy chance-constrained dynamic optimization method for modeling uncertain and dynamic industrial production processes. Using credibility theory to quantify the fuzzy uncertainty level of constraints, an improved fuzzy simulation technique and a data-driven state transition algorithm based on deep neural networks are proposed to solve the FCCDO problem, achieving stable, global, and robust optimization performance.