Paper Info
Title
Danger theory inspired micro-population immune optimization for probabilistic constrained programming
Abstract
This work solves the problem of a general kind of single-objective probabilistic constrained programming without any a prior stochastic distribution information, after probing into an adaptive sampling-based micro immune optimization approach inspired by the danger theory in immunology. In the whole design of the algorithm, the current population is divided into uninfected, susceptible and infected sub-populations based on the version of individuals’ dominance, relying upon the schemes of sample-dependent constraint handling and objective evaluation. Those uninfected and susceptible sub-populations proliferate their clones and execute adaptive mutation with small variable mutation rates, whereas the infected sub-population directly participates in mutation at a large and variable mutation rate. Two mutation strategies, together with the version of life cycle on individual, are simultaneously designed to evolve those sub-populations along different directions in order to explore those diverse and high-quality solutions. It is shown that the complexity of the algorithm depends mainly on the number of iterations. Experimental results have validated that one such approach is a competitive and potential optimizer because of few parameters, fast convergence and its ability of effective noise suppression.
Year
DOI
Venue
2020
10.1007/s12530-019-09277-6
Evolving Systems
Keywords
DocType
Volume
Probabilistic constrained optimization, Danger theory, Micro immune optimization, Adaptive sampling, Life cycle
Journal
11
Issue
ISSN
Citations 
2
1868-6478
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Zhuhong Zhang118616.41
Renchong Zhang200.34