Constrained Optimization via Artificial Immune System

An artificial immune system inspired by the fundamental principle of the vertebrate immune system, for solving constrained optimization problems, is proposed. The analogy between the mechanism of biological immune response and constrained optimization formulation is drawn. Individuals in population are classified into feasible and infeasible groups according to their constraint violations that closely match with the two states, inactivated and activated, of B-cells in the immune response. Feasible group focuses on exploitation in the feasible areas through clonal selection, recombination, and hypermutation, while infeasible group facilitates exploration along the feasibility boundary via location update. Direction information is extracted to promote the interactions between these two groups. This approach is validated by the benchmark functions proposed most recently and compared with those of the state of the art from various branches of evolutionary computation paradigms. The performance achieved is considered fairly competitive and promising.