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This work investigates a micro immune optimization algorithm originated from the danger theory for single-objective probabilistic constrained optimization without any prior stochastic distribution information. In the whole process of population evolution, the current population is divided into species with different danger levels in terms of constraint dominance and danger radius update. Those species...
In this paper, we propose an artificial immune system (AIS) based on the danger theory in immunology for solving dynamic nonlinear constrained single-objective optimization problems with time-dependent design spaces. Such proposed AIS executes orderly three modules—danger detection, immune evolution and memory update. The first module identifies whether there are changes in the optimization environment...
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