2002 Volume 14 Issue 6 Pages 616-629
The purposes of this paper are to propose and evaluate an immune optimization algorithm using a biological immune co-evolutionary phenomenon and cell-cooperation. The co-evolutionary models searches the solution through the interactions between two kinds of agents, one of the agents is called immune agent which optimizes the cost of its own work. The other is called antigen agent which realizes the equal work assignment. This algorithm solves the division-of-labor problems in multi-agent system (MAS) through the three kinds of interactions:division-and-integration processing is used for optimization of the work-cost of immune agents and, escape processing is used to perform equal work assignment as a result of evolving the antigen agents. The immune agent optimizes own cost using division as well as integration processing based on the immune cell-cooperation which is considered a kind of parallel-distributed system with role differentiation. The 'splicing' is one of the re-combination operator of genes, whose function is used for forming the role. The division as well as integration processing in our method is based on the splicing. And the antigen agent computes even division of work domain using escape processing based on a phenomenon that the antigen evolves to escape from the elimination of immune system. In order to investigate the validity of the proposed method, this algorithm is applied to the "N-th agent's Travelling Salesmen Problem (called the n-TSP)" as a typical problem of MAS. The property that is believed to function as solution driver for MAS shall be clarified using several simulations.
