Constrained optimization using artificial immune system

This research exploits the fact embedded in immune system to communicate between innate immune response and adaptive immune response levels. We first sort infeasible antibodies based on their constraint violation values and feasible antibodies based on their objective function values. The next processes assesses in what directions individuals with high affinities should move to improve their objective function values and reduce constraint violations. This information is used to estimate the mutation direction to infeasible antibodies. This approach is validated and tested using benchmark functions used in related researches and the results obtained are compared with studies made in similar area. The performance acquired is competitive and in some cases even better than those of state-of-the-art. The algorithm is also designed to obtain feasible solutions in every run executed.