An optimization algorithm inspired by the immune mechanism and its applications

Immune system protects living body from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. Our overall goal for this paper is twofold: to understand the real immune system from the information processing perspective, and to use idea generated from the immune system to construct new engineering application. As an example of the latter, we propose an optimization algorithm inspired by the immune response mechanism and apply it to Traveling Salesman Problem (TSP). We test the proposed algorithm by the simulations on randomly generated 100 data sets of 10-city problems and two of TSPLIB [1] benchmark problems: ulysses22 (22-city) and eil51 (51-city). The simulation results illustrate that the proposed algorithm can find one hundred percent valid solutions in short computation time, and the solution quality is very good; meanwhile, the shortest path of ulysses22 and eil51 generated by the proposed algorithm are 7013 and 426, respectively, which are the same as the best paths proclaimed at TSPLIB [1].