large-scale optimization using immune algorithm
Immune-inspired optimization algorithms encoded the parameters into individuals where each individual represents a search point in the space of potential solutions. A large number of parameters would result in a large search space. Nowadays, there is little report about immune algorithms effectively solving numerical optimization problems with more than 100 parameters. In this paper, we introduce an improved immune algorithm, termed as Dual-Population Immune Algorithm (DPIA), to solve large-scale optimization problems. DPIA adopts two side-by-side populations, antibody population and memory population. The antibody population employs the cloning, affinity maturation, and selection operators, which emphasizes the global search. The memory population stores current representative antibodies and the update of the memory population pay more attention to maintain the population diversity. Normalized decimal-string representation makes DPIA more suitable for solving large-scale optimization problems. Special mutation and recombination methods are adopted to simulate the somatic mutation and receptor editing process. Experimental results on eight benchmark problems show that DPIA is effective to solve large-scale numerical optimization problems.
large-scale optimization immune algorithm evolutionary algorithm