Data-Driven Based State Transition Algorithm for Dynamic Optimization

Most evolutionary algorithms solve the optimization problems by iteratively calculating the values of the objective functions. However, such explicit objective functions may not exist in solving many process industry optimization problems, especially when these processes have time varying dynamics,...

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Veröffentlicht in:	2019 IEEE Symposium Series on Computational Intelligence (SSCI) S. 1091 - 1096
Hauptverfasser:	Zhang, Yunxiang, Zhou, Xiaojun, Yang, Chunhua
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2019
Schlagworte:	Approximation algorithms Control vector parametrization Data-driven Dynamic optimization Evolutionary computation Heuristic algorithms Linear programming Optimization Process control State transition algorithm
Online-Zugang:	Volltext
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