Artificial Immune Algorithm Source Code with Implementation

This artificial immune algorithm source code implements the following computational workflow: 1. Parameter Initialization: Setting up algorithm parameters including population size, chromosome length, and operational probabilities 2. Random Population Generation: Creating initial population using pop=initpop(popsize, chromlength) function that generates random binary strings representing potential solutions 3. Fault Type Encoding: Each row represents a specific fault condition - code(1,:) for normal operation, code(2,:) for 50% fault level, code(3,:) for 150% fault level. The measured fault data is encoded as Unnoralcode representing 188% fault condition 4. Iterative Optimization Process (M iterations): - Objective Function Calculation: Computes Euclidean distance between solutions using [objvalue]=calobjvalue(pop,i) function to measure solution quality - Fitness Evaluation: Determines individual fitness values using fitvalue=calfitvalue(objvalue) function that maps objective values to selection probabilities - Selection Operation: Performs newpop=selection(pop,fitvalue) using roulette wheel or tournament selection to choose parents for next generation - Crossover Operation: Applies newpop=crossover(newpop,pc,k) with probability pc and parameter k to combine parent chromosomes - Mutation Operation: Executes newpop=mutation(newpop,pm) with probability pm to introduce genetic diversity through bit flipping 5. Optimal Solution Identification: Extracts the individual with maximum fitness value from the final population 6. Termination Condition Check: Implements convergence criteria to determine when to stop the iterative optimization process