A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

Autor: Eryang Guo, Yuelin Gao, Chenyang Hu, Jiaojiao Zhang

Publikováno v: Mathematics, Vol 11, Iss 3, p 522 (2023)

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of

Externí odkaz: https://doaj.org/article/ca29756314d44f96a6342f1b0e3e755d

Distributed constraint optimization for autonomous multi AUV mine counter-measures

In this paper, Mine Counter-Measures (MCM) operations with multiple cooperative Autonomous Underwater Vehicles (AUVs) are examined within the Distributed Constraint optimization Problem (DCOP) framework. The goal of an MCM-operation is to search for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dris___00893::6fcf99c0a5b04816df73bc5706df380d
http://resolver.tudelft.nl/uuid:b5a74e6c-e795-46b3-abb7-11698e0015f0

Distributed constraint optimization for continuous mobile sensor coordination

Autor: Erik Theunissenc, Jeroen Fransmana, Joris Sijs, Bart De Schuttera, Henry Dolb

Publikováno v: ECC
2018 European Control Conference, ECC 2018, 16th European Control Conference, ECC 2018, 12 June 2018 through 15 June 2018, 1100-1105

DCOP (Distributed Constraint optimization Problem) is a framework for representing distributed multi- agent problems. However, it only allows discrete values for the decision variables, which limits its application for real-world problems. In this pa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9a65455fbc88c708b27a2c29aedd898
https://doi.org/10.23919/ecc.2018.8550486

Partition-based clustering using constraint optimization

Autor: Mirco Nanni, Valerio Grossi, Anna Monreale, Tias Guns, Siegfried Nijssen

Publikováno v: Data Mining and Constraint Programming. Foundations of a Cross-Disciplinary Approach, edited by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi, pp. 282–299, 2016
info:cnr-pdr/source/autori:Grossi V.; Guns T.; Monreale A.; Nanni M./titolo:Partition-based clustering using constraint optimization/titolo_volume:Data Mining and Constraint Programming. Foundations of a Cross-Disciplinary Approach/curatori_volume:Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi/editore:/anno:2016
Vrije Universiteit Brussel
Data Mining and Constraint Programming ISBN: 9783319501369
Data Mining and Constraint Programming

Partition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the liter- at

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9c44bbaf41bdbed1e91118580b4deae
https://biblio.vub.ac.be/vubir/partitionbased-clustering-using-constraint-optimization(2d113c25-41f2-4834-a12e-537ecb661ff0).html

FSRFT-Fast Simplified Real Frequency Technique via Selective Target Data Approach for Broadband Double Matching

Autor: Ramazan Kopru

WOS: 000395489200009 This brief introduces a broadband double-matching (DM) solver called fast simplified real frequency technique (FSRFT). FSRFT is essentially a greatly accelerated variant of the well-known classical simplified real frequency techn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da2b8bd08cb9ee2474c2a6664730124e
https://hdl.handle.net/11729/1711