Detail publikace

Comparison of Novel Multi-Objective Self Organizing Migrating Algorithm with Conventional Methods

Originální název

Comparison of Novel Multi-Objective Self Organizing Migrating Algorithm with Conventional Methods

Anglický název

Comparison of Novel Multi-Objective Self Organizing Migrating Algorithm with Conventional Methods

Jazyk

en

Originální abstrakt

In the paper, three algorithms for the multi-objective optimization based on the strategy of a self-organized migration are compared. The first two algorithms - Weighted Sum Method and Rotated Weighted Metric Method - transform multiple objectives into a single fitness function. The third method - a novel MOSOMA - combines the principle of the non-dominated sorting of population in the objective space and the survey of the decision space of input variables based on the self-organized migration. All three algorithms are compared on the test problem with the Pareto front, which contains both convex and non-convex parts. Monitored parameters are generational distance, spread of solutions and CPU time.

Anglický abstrakt

In the paper, three algorithms for the multi-objective optimization based on the strategy of a self-organized migration are compared. The first two algorithms - Weighted Sum Method and Rotated Weighted Metric Method - transform multiple objectives into a single fitness function. The third method - a novel MOSOMA - combines the principle of the non-dominated sorting of population in the objective space and the survey of the decision space of input variables based on the self-organized migration. All three algorithms are compared on the test problem with the Pareto front, which contains both convex and non-convex parts. Monitored parameters are generational distance, spread of solutions and CPU time.

BibTex


@inproceedings{BUT36066,
  author="Petr {Kadlec} and Zbyněk {Raida}",
  title="Comparison of Novel Multi-Objective Self Organizing Migrating Algorithm with Conventional Methods",
  annote="In the paper, three algorithms for the multi-objective optimization based on the strategy of a self-organized migration are compared. The first two algorithms - Weighted Sum Method and Rotated Weighted Metric Method - transform multiple objectives into a single fitness function. The third method - a novel MOSOMA - combines the principle of the non-dominated sorting of population in the objective space and the survey of the decision space of input variables based on the self-organized migration. All three algorithms are compared on the test problem with the Pareto front, which contains both convex and non-convex parts. Monitored parameters are generational distance, spread of solutions and CPU time.",
  address="Department of Radio Electronics, Brno University of Technology",
  booktitle="Proceedings of 21st International Conferrence Radioelktronika 2011",
  chapter="36066",
  howpublished="print",
  institution="Department of Radio Electronics, Brno University of Technology",
  year="2011",
  month="april",
  pages="97--100",
  publisher="Department of Radio Electronics, Brno University of Technology",
  type="conference paper"
}