Adaptive Racing Sampling Based Immune Optimization Approach for Nonlinear Multi-Objective Chance Constrained Programming

This work investigates a multi-objective immune optimization approach to solve the general type of nonlinear multi-objective chance constrained programming without prior noise information. One such kind of model is first converted into a sample-dependent approximation one, while a sample bound estim...

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Vydané v:	IEEE access Ročník 12; s. 96231 - 96245
Hlavní autori:	Yang, Kai, Zhang, Renchong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Adaptive sampling Approximation methods Complexity Constraints danger theory Feasibility immune optimization Immune system Multi-objective chance constrained programming Multiple objective analysis Nonlinear systems Optimization Optimization methods Optimization models Probabilistic logic Programming profession Random variables sample-dependent approximation Sampling methods Stochastic processes Transportation
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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