Multi-Objective Genetic Algorithms for Chemical Engineering Applications

摘要: The current environmental and social awareness leads optimization researchers to be more concerned with multi-objective problems (MOOPs). Evolutionary methods particularly genetic algorithms are commonly used in chemical engineering, where the generally involve complex models embedded an outer loop. This paper first presents two tackle continuous mixed-integer engineering problems. then illustrated by classical benchmark often literature for mono-objective studies: three bi-objective ones (ammonia synthesis reactor, alkylation plant, natural gas transportation network), a structural design problem (Williams–Otto process, new product development pharmaceutical industry economic study of HDA process). results compared reported literature, analysis highlights efficiency proposed either case or one.