Detail publikace

Multi-Objective Self-Organizing Migrating Algorithm: Sensitivity on Controlling Parameters

Originální název

Multi-Objective Self-Organizing Migrating Algorithm: Sensitivity on Controlling Parameters

Anglický název

Multi-Objective Self-Organizing Migrating Algorithm: Sensitivity on Controlling Parameters

Jazyk

en

Originální abstrakt

In this paper, we investigate the sensitivity of a novel Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) on setting its control parameters. Usually, efficiency and accuracy of searching for a solution depends on the settings of a used stochastic algorithm, because multi-objective optimization problems are highly non-linear. In the paper, the sensitivity analysis is performed exploiting a large number of benchmark problems having different properties (the number of optimized parameters, the shape of a Pareto front, etc.). The quality of solutions revealed by MOSOMA is evaluated in terms of a generational distance, a spread and a hyper-volume error. Recommendations for proper settings of the algorithm are derived: These recommendations should help a user to set the algorithm for any multi-objective task without prior knowledge about the solved problem.

Anglický abstrakt

In this paper, we investigate the sensitivity of a novel Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) on setting its control parameters. Usually, efficiency and accuracy of searching for a solution depends on the settings of a used stochastic algorithm, because multi-objective optimization problems are highly non-linear. In the paper, the sensitivity analysis is performed exploiting a large number of benchmark problems having different properties (the number of optimized parameters, the shape of a Pareto front, etc.). The quality of solutions revealed by MOSOMA is evaluated in terms of a generational distance, a spread and a hyper-volume error. Recommendations for proper settings of the algorithm are derived: These recommendations should help a user to set the algorithm for any multi-objective task without prior knowledge about the solved problem.

Dokumenty

BibTex


@article{BUT99185,
  author="Petr {Kadlec} and Zbyněk {Raida} and Jiří {Dřínovský}",
  title="Multi-Objective Self-Organizing Migrating Algorithm: Sensitivity on Controlling Parameters",
  annote="In this paper, we investigate the sensitivity of a novel Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) on setting its control parameters. Usually, efficiency and accuracy of searching for a solution depends on the settings of a used stochastic algorithm, because multi-objective optimization problems are highly non-linear. In the paper, the sensitivity analysis is performed exploiting a large number of benchmark problems having different properties (the number of optimized parameters, the shape of a Pareto front, etc.). The quality of solutions revealed by MOSOMA is evaluated in terms of a generational distance, a spread and a hyper-volume error. Recommendations for proper settings of the algorithm are derived: These recommendations should help a user to set the algorithm for any multi-objective task without prior knowledge about the solved problem.",
  address="Brno University of Technology, Faculty of Electrical Engineering and Communication, Dept. of Radio Electronics",
  chapter="99185",
  institution="Brno University of Technology, Faculty of Electrical Engineering and Communication, Dept. of Radio Electronics",
  number="1",
  volume="22",
  year="2013",
  month="april",
  pages="296--308",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication, Dept. of Radio Electronics",
  type="journal article in Web of Science"
}