An　adaptive　gradient　multiobjective　particle　swarm　optimization　(AGMOPSO)　algorithm,　based　on　a　multiobjective　gradient　(stocktickerMOG)　method　and　a　self-adaptive　flight　parameters　mechanism,　is　developed　to　improve　the　computation　performance　in　this　paper.　In　this　AGMOPSO　algorithm,　the　stocktickerMOG　method　is　devised　to　update　the　archive　to　improve　the　convergence　speed　and　the　local　exploitation　in　the　evolutionary　process.　Meanwhile,　the　self-adaptive　flight　parameters　mechanism,　according　to　the　diversity　information　of　the　particles,　is　then　established　to　balance　the　convergence　and　diversity　of　AGMOPSO.　Attributed　to　the　stocktickerMOG　method　and　the　self-adaptive　flight　parameters　mechanism,　this　AGMOPSO　algorithm　not　only　has　faster　convergence　speed　and　higher　accuracy,　but　also　its　solutions　have　better　diversity.　Additionally,　the　convergence　is　discussed　to　confirm　the　prerequisite　of　any　successful　application　of　AGMOPSO.　Finally,　with　regard　to　the　computation　performance,　the　proposed　AGMOPSO　algorithm　is　compared　with　some　other　multiobjective　particle　swarm　optimization　algorithms　and　two　state-of-the-art　multiobjective　algorithms.　The　results　demonstrate　that　the　proposed　AGMOPSO　algorithm　can　find　better　spread　of　solutions　and　have　faster　convergence　to　the　true　Pareto-optimal　front.