Abstract
In this paper, we propose a calculation method of local dominance and enhance multiobjective evolutionary algorithms by performing a distributed search based on local dominance. In this method, we first transform all fitness vectors of individuals to polar coordinate vectors in the objective function space. Then we divide the population into several sub-populations by using declination angles. We calculate local dominance for individuals belonging to each sub-population based on the local search direction, and apply selection, recombination, and mutation to individual within each sub-population. We pick up NSGA-II and SPEA2 as two representatives of the latest generation of multiobjective evolutionary algorithms and enhance them with our model. We verify the effectiveness of the proposed method obtaining Pareto optimal solutions satisfying diversity conditions by comparing the search performance between the conventional algorithms and their enhanced versions.
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.
