In this paper, an adaptive multi-objective optimization algorithm based on the clonal selection principle is proposed. This algorithm uses the adaptive mutation guided by non-dominated sorting and a weighted aggregation function. In the iteration process, the antigens to be recognized are dynamically adjusted, and the size of the Pareto memory set is adaptively changed according to the concentration metric based on the crowding distance. As a result, the diversity of the Pareto solutions is improved while keeping a good convergence performance in relation to the "true" Pareto front. Direct comparisions with other evolutionary multi-objective optimization techniques such as SPEA, NSGAII show that the proposed algorithm is better in terms of convergence and diversity along the Pareto front respectively.