@book{scholars4290,
            note = {cited By 2},
             doi = {10.4018/978-1-4666-6252-0.ch017},
            year = {2014},
           title = {Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution},
       publisher = {IGI Global},
         journal = {Contemporary Advancements in Information Technology Development in Dynamic Environments},
           pages = {340--359},
            isbn = {9781466662537; 1466662522; 9781466662520},
          author = {Ganesan, T. and Elamvazuthi, I. and Shaari, K. Z. K. and Vasant, P.},
        abstract = {Many industrial problems in process optimization are Multi-Objective (MO), where each of the objectives represents different facets of the issue. Thus, having in hand multiple solutions prior to selecting the best solution is a seminal advantage. In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). These methods are then employed to trace the approximate Pareto frontier to the bioethanol production problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies are then carried out with the algorithms developed in this chapter. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here. {\^A}{\copyright} 2014, IGI Global.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949844929&doi=10.4018\%2f978-1-4666-6252-0.ch017&partnerID=40&md5=c6f2075cff4cc866690718a62e6ae87b},
        keywords = {Bioethanol; Ethanol; Evolutionary algorithms; Heuristic algorithms; Multiobjective optimization, Bio-ethanol production; Comparative studies; Differential Evolution; Gravitational search algorithm (GSA); Hypervolume indicators; Industrial problem; Meta heuristic algorithm; Scalarization approach, Optimization}
}