An Immune Inspired-based Optimization Algorithm: Application to the Traveling Salesman Problem

The clonal selection is a mechanism used by the natural immune system to select cells that recognize the antigens to proliferate. The proliferated cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. The concept of clonal selection is a vitally important one to the success of the human immune system, and it provides an excellent example of the principles of selection at work. The Positive and negative selection is another interesting mechanism in the immune system that work together to both retain cells that recognize the self peptides, while also removing cells that recognize any self peptides. In this paper, a cloning-based algorithm inspired by the clonal and the positive/negative selection mechanism of the natural immune system is presented. This algorithm is inherently parallel and the cloning strategy employs greedy criteria which lends to an adaptive approach. The well known TSP is used to illustrate the approach with experimental comparison with Ant approach. Simulations demonstrate that this approach generates good solutions to traveling salesman problem and greatly improve the convergence speed compared to the Ant-based optimization approach.