A Location Gradient Induced Sorting Approach for Multi-objective Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252011" target="_blank" >RIV/61989100:27240/22:10252011 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-16-8048-9_15" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-16-8048-9_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-16-8048-9_15" target="_blank" >10.1007/978-981-16-8048-9_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Location Gradient Induced Sorting Approach for Multi-objective Optimization

Popis výsledku v původním jazyce

In the field of population-based multi-objective optimization, a non-dominated sorting approach amounts to sort a set of candidate solutions with multiple objective function values, based on their dominance relations, and to find out solutions distributed into the first front set, second front set, and so on. A fast non-dominated sorting approach used within the framework of Non-dominated Sorting Genetic Algorithm (NSGA-II) reaches a O(MN2) time complexity (N is the population size, M is the number of the objectives). In this paper, we show an approach based on the Location Gradient (LG) number (LG sorting). Our LG sorting method is especially efficient in dealing with duplicate solutions, which only costs O(N) in front assignment process when all the solutions are duplicate. Except that, in many cases, the LG sorting method can reach O(N log N) time complexity. But in the worst case, it still costs O(MN2). We demonstrate the efficacy of the LG-based sorting method comparison against several existing non-dominated sorting procedures.

Název v anglickém jazyce

A Location Gradient Induced Sorting Approach for Multi-objective Optimization

Popis výsledku anglicky

In the field of population-based multi-objective optimization, a non-dominated sorting approach amounts to sort a set of candidate solutions with multiple objective function values, based on their dominance relations, and to find out solutions distributed into the first front set, second front set, and so on. A fast non-dominated sorting approach used within the framework of Non-dominated Sorting Genetic Algorithm (NSGA-II) reaches a O(MN2) time complexity (N is the population size, M is the number of the objectives). In this paper, we show an approach based on the Location Gradient (LG) number (LG sorting). Our LG sorting method is especially efficient in dealing with duplicate solutions, which only costs O(N) in front assignment process when all the solutions are duplicate. Except that, in many cases, the LG sorting method can reach O(N log N) time complexity. But in the worst case, it still costs O(MN2). We demonstrate the efficacy of the LG-based sorting method comparison against several existing non-dominated sorting procedures.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING (ECC 2021)

ISBN

978-981-16-8048-9

ISSN

2190-3018

e-ISSN

2190-3026

Počet stran výsledku

10

Strana od-do

157-166

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Commun Univ Zhejiang

Datum konání akce

29. 5. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000773051200015