In　this　paper,　an　improved　multiobjective　optimal　control　(MOOC)　strategy　is　developed　to　improve　the　operational　efficiency,　satisfy　the　effluent　quality　(EQ)　and　reduce　the　energy　consumption　(EC)　in　wastewater　treatment　process　(WWTP).　First,　the　adaptive　kernel　function　models　of　the　process,　which　can　describe　the　complex　dynamics　of　EQand　EC,　are　developed　for　the　proposed　MOOC　strategy.　Meanwhile,　a　multiobjective　optimization　problem　is　constituted　to　account　for　WWTP.　Second,　an　improved　multiobjective　particle　swarm　optimization　(MOPSO)　algorithm,　using　a　self-adaptive　flight　parameters　mechanism　and　a　multiobjective　gradient　(MOG)　method,　is　designed　to　minimize　the　established　objectives.　And　then　the　optimal　set-points　of　dissolved　oxygen　(So)　and　nitrate　(SNQ)　are　obtained　in　the　treatment　process.　Third,　an　adaptive　fuzzy　neural　network　controller　(FNNC)　is　applied　for　realizing　the　tracking　control　of　the　obtained　set-points　in　the　proposed　MOOC　strategy.　Finally,　Benchmark　Simulation　Model　No.1　(BSM1)　is　introduced　to　evaluate　the　effectiveness　of　the　proposed　MOOC　strategy.　Experimental　results　show　the　efficacy　of　the　proposed　method.　(C)　2018　Elsevier　B.V.　All　rights　reserved.