A New Artificial Immune Algorithm for Solving Function Optimization Problems
To overcome the drawbacks of Simple Genetic Algorithm (SGA) and Particle Swarm Optimization (PSO) in
multi-peak function optimization, such as insufficient diversity and local optimization tendency, a new artificial immune
algorithm (IAIA) is proposed. Based on the traditional framework of the artificial immune algorithm, the algorithm redesigns
the two key clone and mutation operators to address these drawbacks. Last, test functions were used to compare the IAIA
with the SGA and the PSO. The research results showed that the IAIA algorithm has certain advantages in both convergence
speed and accuracy in solving such optimization problems.
Keywords - immune algorithms, multi-peak function, clone selection, Thermal Fatigue, function optimization.