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.