A Distributed Immune Algorithm for Solving Optimization Problems

The mammal immune system is a distributed multiagent system. Its properties of distributive control and self organization have created interest in using immune principles to solve complex engineering tasks such as decentralized robot control, pattern recognition, multimodal and combinatorial optimization. In this paper a new immunity-based algorithm for solving optimization problems is proposed. The algorithm differs from the representative immune algorithm CLONALG. The agents participating in distributed problem solving enrich their knowledge about the solution via communication with other agents. Moreover they are decomposed into groups of specialists that can modify only some decision variables and/or use their own method of local improvement of the solution. The empirical results confirming usability of the algorithm and its advantage over CLONALG are presented. Obtained estimates of the global optima of multimodal test functions and traveling salesperson problem (TSP) are closer to the theoretical solutions and require fewer tentative computations.