Iterated Multi-Swarm: A Multi-Swarm Algorithm Based on Archiving Methods

被引：0

作者：

Britto, Andre ^{[1
]}

Mostaghim, Sanaz ^{[2
]}

Pozo, Aurora ^{[3
]}

机构：

[1] Minist Educ Brazil, CAPES Fdn, Brasilia, DF, Brazil

[2] KIT, Inst AIFB, Karlsruhe, Germany

[3] Univ Fed Parana, Dept Comp Sci, Curitiba, Parana, Brazil

来源：

GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2013年

关键词：

Many-Objective Optimization; Particle Swarm Optimization; Multi-Objective Optimization; OPTIMIZATION; RANKING;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Usually, Multi-Objective Evolutionary Algorithms face serious challengers in handling many objectives problems. This work presents a new Particle Swarm Optimization algorithm, called Iterated Multi-Swarm (I-Multi Swarm), which explores specific characteristics of PSO to face Many-Objective Problems. The algorithm takes advantage of a Multi-Swarm approach to combine different archiving methods aiming to improve convergence to the Pareto-optimal front and diversity of the non-dominated solutions. I-Multi Swarm is evaluated through an empirical analysis that uses a set of many-Objective problems, quality indicators and statistical tests.

引用

页码：583 / 590

页数：8

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