Through　analysis　of　biological　wastewater　treatment　process　(WWTP),　a　multi-objective　optimal　control　strategy　was　developed　with　targets　of　minimizing　both　energy　consumption　and　amercement.　A　hybrid　multi-objective　barebones　particle　swarm　optimization　(HBBMOPSO)　algorithm　based　on　Pareto　dominance　and　decomposition　was　proposed　to　improve　convergence　and　diversity　of　optimized　set　of　Pareto　solutions.　In　HBBMOPSO,　selection　of　personal　leaders　was　determined　from　self-adaptive　penalty　factor　decomposition　while　maintenance　of　external　dossiers　and　selection　of　global　leaders　were　determined　from　dominance　and　crowded　distance.　Furthermore,　elitism　learning　strategy　was　adopted　to　facilitate　particle　escaping　from　local　Pareto　fronts.　Finally,　HBBMOPSO　was　combined　with　self-organizing　fuzzy　nerve　network　modeler　and　controller　to　realize　dynamic　optimization,　intelligent　decision,　and　background　monitoring　on　dissolved　oxygen　and　nitrate　nitrogen　in　biological　WWTP.　Experimental　study　on　international　standardized　simulator　platform　BSM1　showed　that　HBBMOPSO　method　can　effectively　reduce　energy　consumption　under　the　premise　of　ensuring　effluent　to　meet　quality　standard.　©　All　Right　Reserved.