Abstract
The main characteristic feature of evolutionary multiobjective optimization (EMO) is that no a priori information about the decision maker's preference is utilized in the search phase. EMO algorithms try to find a set of well-distributed Pareto-optimal solutions with a wide range of objective values. It is, however, very difficult for EMO algorithms to find a good solution set of a multiobjective combinatorial optimization problem with many decision variables and/or many objectives. In this paper, we propose an idea of incorporating the decision maker's preference into EMO algorithms to efficiently search for Pareto-optimal solutions of such a hard multiobjective optimization problem.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
