Artificial Immune System

Algorithm Process:

1. Parameter Configuration

2. Random generation of initial population - population = initpop(popsize, chromlength) This function initializes the population with specified size and chromosome length, typically using binary encoding for immune cells representation.

3. Fault type encoding, where each row represents: code(1,:) for normal condition; code(2,:) for 50% fault; code(3,:) for 1.5% fault. Actual fault measurement data encoding, referred to as Unnormalcode, 188%. The encoding scheme maps different fault conditions to binary patterns for immune system recognition.

4. Iteration Initialization (M):

1) Calculate objective function value: Euclidean distance [objvalue] = calobjvalue(population, i) The calobjvalue function computes Euclidean distance between current population antibodies and antigen patterns.

2) Calculate fitness value for each individual in population: fitvalue = calfitvalue(objvalue) Fitness evaluation transforms distance values into selection probabilities using appropriate scaling methods.

3) Selection: newpop = selection(population, fitvalue); objvalue = calobjvalue(newpop, i); Selection operation employs roulette wheel or tournament selection to choose antibodies based on fitness.

Crossover: newpop = crossover(newpop, pc); objvalue = calobjvalue(newpop, i); Crossover operation with probability pc combines antibody features to generate new solutions.

Mutation: newpop = mutation(newpop, pm); objvalue = calobjvalue(newpop, i); Mutation operation with probability pm introduces diversity by flipping bits in antibody encoding.