Until recently, the main focus of researchers that develop algorithms for evolutionary multi-objective optimization has been the creation of mechanisms capable of obtaining sets of solutions that are as close as possible to the true Pareto front of the problem and also as diverse as possible in the objective space, to properly cover such front. However, an adequate maintenance of diversity in the decision space is also important, to efficiently solve several classes of problems and even to facilitate the post-optimization decision making process. This aspect has been widely studied in evolutionary single-objective optimization, what led to the development of several diversity maintenance techniques. Among them, the recently proposed concentration-based artificial immune network (cob-aiNet), which is capable of self-regulating the population size, presented promising results in multimodal problems. So, it is extended here to deal with multi-objective problems that require a proper maintenance of diversity in the decision space.