Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099111" target="_blank" >RIV/61989100:27240/16:86099111 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1155/2016/6329530" target="_blank" >http://dx.doi.org/10.1155/2016/6329530</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2016/6329530" target="_blank" >10.1155/2016/6329530</a>

Result language

angličtina

Original language name

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

Original language description

The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds. (C) 2016 Vojtěch Uher et al.

Czech name

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

—

Project

<a href="/en/project/GA15-06700S" target="_blank" >GA15-06700S: Unconventional Control of Complex Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

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

Name of the periodical

Computational Intelligence and Neuroscience

ISSN

1687-5265

e-ISSN

—

Volume of the periodical

2016

Issue of the periodical within the volume

2016

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000388857000001

EID of the result in the Scopus database

2-s2.0-84999792391