An Optimization Algorithm Based on Multi-population Artificial Immune Network

With the intrinsic properties of multimodal optimization problems, a multi-population artificial immune network algorithm (mopt-aiNet) is proposed to improve the performance of static and time-varying multimodal optimization problems by making use of biologic immune mechanism in this paper. Compared with other immune network search methods, several novel operations such as multi-population dynamic hypermutation, asynchronous colony evolution, dynamic memory solutions management and a hill-valley exploring are designed which can speed up searching the environment in an optimal way. Two other immune network algorithms are compared against mopt-aiNet by using static and dynamic benchmarks. Comparative analysis illustrates mopt-aiNet´s potential value.