Through　the　analysis　of　the　biological　wastewater　treatment　process　(WWTP),　a　multiobjective　optimal　control　strategy　is　developed　with　the　usage　of　energy　consumption　(EC)　and　effluent　quality　(EQ)　as　objectives　to　be　optimized.　To　effectively　handle　the　multiobjective　optimization　problem　(MOP)　with　complex　Pareto-optimal　front　(POF),　an　adaptive　multiobjective　evolutionary　algorithm　based　on　decomposition　(AMOEA/D)　is　proposed　in　this　paper.　Since　the　efficiency　of　the　multiple　reference　points　and　two-phase　optimization　strategies　in　solving　MOPs　with　complex　POFs　has　been　proved.　In　the　proposed　AMOEA/D,　an　auto-switching　strategy　based　on　the　aggregation　function　enhancement　is　designed　to　automatically　make　the　algorithm　switch　from　the　first　phase　to　the　second　phase.　Besides,　an　adaptive　differential　evolution　strategy　is　introduced　into　AMOEA/D　to　balance　exploration　and　exploitation　during　the　evolutionary　process.　Finally,　the　dynamic　optimization,　intelligent　decision　and　bottom　tracking　control　of　the　set-points　of　the　dissolved　oxygen　and　nitrate　nitrogen　in　the　WWTP　are　achieved　via　the　combination　of　AMOEA/D　with　the　self-organizing　fuzzy　neural　network　approximator　and　the　self-organizing　fuzzy　neural　network　controller.　The　international　benchmark　simulation　model　No.　1　(BSM1)　is　utilized　for　experimental　verification.　Simulation　results　demonstrate　that　the　proposed　AMOEA/D　can　effectively　reduce　the　EC　of　the　WWTP　under　the　premise　of　ensuring　effluent　parameters　to　meet　the　effluent　discharge　standards.