The　convergence　and　diversity　of　the　Pareto　optimal　solutions　is　of　great　importance　for　multiobjective　evolutionary　algorithms.　Based　on　parallel　cell　balanceable　fitness　estimation　(PCBFE),　a　novel　bare-bones　multiobjective　particle　swarm　optimization　(NBBMOPSO)　algorithm　is　proposed　in　this　paper.　First,　the　PCBFE　strategy,　which　is　based　on　the　parallel　cell　mapping　approach,　is　developed　to　retain　the　balance　between　the　proximity　and　the　diversity.　After　that,　the　PCB1-E　strategy　is　adopted　to　maintain　external　archive　and　update　leaders.　Second,　an　adaptive　update　strategy　for　crossover　probability　is　designed　to　repair　the　weakness　of　particle　search.　Finally,　an　elitism　learning　strategy　is　performed　to　exchange　useful　information　among　solutions　in　the　external　archive,　which　can　enhance　the　capability　of　dropping　out　of　the　local　Pareto　front.　To　demonstrate　the　merits　of　NBBMOPSO　for　multiobjective　optimization,　Zitzler-Deb-Thiele　(ZDT)　and　Deb-Thiele-Laumanns-Zitzler　(DTLZ)　test　suits　are　examined　with　comparisons　against　the　other　seven　state-of-the-art　competitors.　Experimental　results　show　that　the　proposed　NBBMOPSO　outperforms　all　the　other　methods　in　terms　of　the　chosen　performance　metrics.