Based on the characteristics of gasoline blending optimization
an active constraints aided objective method was proposed. Active constraints were distinguished by the feasible solutions and infeasible solutions in the population of an evolutionary algorithm. With these active constraints
an active constraints aided objective was added to the gasoline blending optimization and the problem was transformed into a multi-objective constrained optimization problem. This method incorporated the knowledge of active constraints into the selection operation of the evolutionary algorithm to improve its selection efficiency and guide its search away from local optima. The application of the strategy was demonstrated by a case study.
Progress of solar photo-thermo-catalysis for ammonia synthesis and decomposition under mild conditions
Mixed matrix membranes based on nitrogen-rich Schiff base network polymer and their CO2 separation performance
Experimental study of heat transfer enhancement by pressure waves in cross flow tube bundles
Multi objective optimization of flow and heat transfer characteristics of modified manifold microchannel heat sink based on machine learning
Controlled synthesis of Bi2MoO6/MoO3 composite fibers via in-situ hydrothermal method and their high-efficiency photocatalytic hydrogen production performance