Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
An Artificial Physics Optimization Algorithm for Multi-Objective Problems Based on Virtual Force Sorting Proceedings
verfasst von : Yan Wang, Jian-chao Zeng, Ying Tan
Erschienen in: Swarm, Evolutionary, and Memetic Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In order to maintain the diversity of non-dominated solutions in multi-objective optimization algorithms efficiently the authors have proposed a multi-objective artificial physics optimization algorithm based on virtual force sorting (VFMOAPO). Adopting quick-sort idea, the individuals in non-dominated solutions set were sorted by the total virtual force exerting on the other individuals. So the non-dominated solution set was pruned and the individual with the maximal sum of virtual force exerting on the other individuals was selected as the global best solution. Some benchmark functions were tested for comparing the performance of VFMOAPO with MOPSO, NSGA and SPEA. The simulation results show the algorithm is feasible and competitive.