A Novel Adaptive Immune-Based Multi-modal Function Optimization Algorithm

The multi-modal function optimization is an important problem with a wide-ranging application. In order to find out all optimal solutions and local optimal solutions as many as possible, an adaptive immune-based optimization algorithm is proposed based on analyzing the characteristics and disadvantages of clonal selection algorithm, and combining memory cells producing, network suppression and valley searching method. Testing typical multi-modal functions show this algorithm not only has the less computational efforts and the better search capability, but also can realize adaptive searching without any transcendental presumptions.