Danger Theory Based Micro Immune Optimization Algorithm Solving Probabilistic Constrained Optimization

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 with low danger levels proliferate their clones and execute mutation with small variable mutation rates, whereas others directly participate in mutation with large mutation rates. Experimental results have validated that one such approach is a competitive and potential optimizer with structural simplicity and effective noise suppression.