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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Veröffentlicht in:IEEE access Jg. 12; S. 96231 - 96245
Hauptverfasser: Yang, Kai, Zhang, Renchong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 estimate model is theoretically acquired based on the empirical Bernstein bound, in order to control the sampling size of random variable. Secondly, a feasibility detection approach with adaptive sampling is designed to quickly justify whether an individual is empirically feasible. Inspired by the danger theory, an artificial immune optimization model is drawn in terms of immune response mechanisms in the immune system, which derives out a multi-objective chance constrained optimizer with small populations and multiple evolutionary strategies. The computational complexity of the optimizer depends mainly on the sample bound and the size of memory pool. Comparative experiments have validated that it is a robust, stable, and effective optimizer with high efficiency while helping for solving complex chance constrained problems.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3417952