关键词: 多目标优化, Pareto最优解, AIS

Abstract: To overcome shortcomings of traditional GA（Genetic Algorithm） and reduce the complexity of optimizing process，Artificial Immune System（AIS） was introduced to optimize multiple conflicting objective functions.Both random weight method and adaptive weight method were used to calculate individual fitness，which not only made solutions distributed on the front of the Pareto solution equably，but also let this algorithm convergent more quickly；AIS accelerated the convergent speed and kept the group diversity；a new method was used to set up a Pareto set to keep the approximate Pareto solutions in every evolving population.Two typical multi-objective testing functions validated the algorithm at the end of the article.Results indicate that the approach is competitive and it can be considered a viable alternative to solve multi-objective optimization problems.

Key words: multi-objective optimization, Pareto optimal solution, AIS