Detail publikace

A Novel Multi-Objective Self-Organizing Migrating Algorithm

Originální název

A Novel Multi-Objective Self-Organizing Migrating Algorithm

Anglický název

A Novel Multi-Objective Self-Organizing Migrating Algorithm

Jazyk

en

Originální abstrakt

In the paper, a novel stochastic Multi-Objective Self Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MOSOMA performance was tested on benchmark problems and selected electromagnetic structures. MOSOMA performance was compared with the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). MOSOMA excels in the uniform distribution of solutions and their completeness.

Anglický abstrakt

In the paper, a novel stochastic Multi-Objective Self Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MOSOMA performance was tested on benchmark problems and selected electromagnetic structures. MOSOMA performance was compared with the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). MOSOMA excels in the uniform distribution of solutions and their completeness.

BibTex


@article{BUT74914,
  author="Petr {Kadlec} and Zbyněk {Raida}",
  title="A Novel Multi-Objective Self-Organizing Migrating Algorithm",
  annote="In the paper, a novel stochastic Multi-Objective Self Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MOSOMA performance was tested on benchmark problems and selected electromagnetic structures. MOSOMA performance was compared with the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). MOSOMA excels in the uniform distribution of solutions and their completeness.",
  address="Brno University of Technology, Faculty of Electrical Engineering and Communication, Dept. of Radio Electronics",
  chapter="74914",
  institution="Brno University of Technology, Faculty of Electrical Engineering and Communication, Dept. of Radio Electronics",
  number="4",
  volume="20",
  year="2011",
  month="december",
  pages="804--816",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication, Dept. of Radio Electronics",
  type="journal article in Web of Science"
}