Due to its concise formation, fast convergence, and flexible parameters, particle swarm optimization (PSO) with the ability to gain multiple solutions at a run and to approximate the Pareto front of those non-convex or discontinuous multiobjective optimization problems (MOPs) is considered to be one of the most promising techniques for MOPs. However, several challenges, such as maintaining the archive, selecting the global and personal best solutions, and balancing the exploration and exploitation, occur when extending PSO from single-objective optimization problems to MOPs. In this paper, the distribution entropy and its difference of an approximate Pareto front in a new objective space, named parallel cell coordinate system (PCCS), are proposed to assess the diversity and evolutionary status of the population. The feedback information from evolutionary environment is served in the evolutionary strategies to balance the convergence and diversity of an approximate Pareto front. Meanwhile, the new concepts, such as cell dominance and individual density based on cell distance in the PCCS, are introduced to evaluate the individual environmental fitness which is the metric using in updating the archive and selecting the global best solutions. The experimental results illustrate that the proposed algorithm in this paper significantly outperforms the other eight peer competitors in terms of IGD on 12 test instances chosen from the ZDT and DTLZ test suites.