Immune optimization approach solving multi-objective chance-constrained programming

This article presents one bio-inspired immune optimization approach for linear or nonlinear multi-objective chance-constrained programming with any a prior random vector distribution. Such approach executes in order sample-allocation, evolution and memory update within a run period. In these modules...

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Veröffentlicht in:Evolving systems Jg. 6; H. 1; S. 41 - 53
Hauptverfasser: Zhang, Zhuhong, Wang, Lei, Long, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2015
Schlagworte:
Artificial Intelligence
Complex Systems
Complexity
Engineering
Original Paper
Immune optimization
Dominance probability
Sample-allocation
Artificial immune systems
Multi-objective chance-constrained programming
ISSN:1868-6478, 1868-6486
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This article presents one bio-inspired immune optimization approach for linear or nonlinear multi-objective chance-constrained programming with any a prior random vector distribution. Such approach executes in order sample-allocation, evolution and memory update within a run period. In these modules, the first ensures that those high-quality elements can attach large sample sizes in the noisy environment. Thereafter, relying upon one proposed dominance probability model to justify whether one individual is superior to another one; the second attempts to find those diverse and excellent individuals. The last picks up some individuals in the evolving population to update low-quality memory cells in terms of their dominance probabilities. These guarantee that excellent and diverse individuals evolve towards the Pareto front, even if strong noises influence the process of optimization. Comparative and experimental results illustrate that the Monte Carlo simulation and important sampling make the proposed approach expose significantly different characteristics. Namely, the former ensures it a competitive optimizer, but the latter makes it effective only for uni-modal or linear chance-constrained programming. The sensitivity analysis claims that such approach performs well when two sensitive parameters takes values over specific intervals.
ISSN:1868-6478
1868-6486
DOI:10.1007/s12530-013-9101-x