Novel immune algorithm and its application to multi-modal function optimization
一种新的免疫算法及其在多模态函数优化中的应用

A novel immune algorithm, suitable for function optimization problems with either multimodal or noncontinuous objective functions, was proposed based on four immune operators of memory cell keeping, clone selection, somatic mutation and population updating from a niching technique and several immune mechanisms. The algorithm, of which had capability of global and local searching and characteristics of parallel searching, not only provided a practical method for solving optimization problems, but also reflected on some simple performance characteristics of antibodies to antigens. The key of its design was to construct a memory cell-keeping operator based on a niching method that could maintain diversity of population and keep good antibodies of current populations, and to design a mutation operator by utilizing the metaphor of affinity maturation. Further, its convergence was proved in theory. Simulation comparison showed that the algorithm can accomplish effectively optimization tasks, and specially search rapidly more than one of optimal solutions for certain optimization problems.