A New Immune Clone Algorithm to solve the constrained optimization problems

In recent years, the constrained optimization problems have become a hot topic among the interest of scholars. In this paper, a new improved artificial immune algorithm is proposed and then used for solving constrained optimizations problems (COPs). This algorithm will treat these COPs as multi-objective optimization problems, and it is based on the concept of Pareto optimization to solve COPs. The mechanism of clone is imported into this new immune algorithm, at the same time, the new improved immune algorithm consists some new concepts, such as linear non-equilibrium recombination operator and preference difference, which can build an efficient immune model for solving this kind of multi-object problems. Finally, simulation on some test functions show that the new immune clone algorithm can obtain better results compared with the existing algorithms. Key-Words: constrained optimization, Multi-object optimization, linear non-equilibrium recombination operator, immune, clone, preference difference, Pareto optimization