About: Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm
Creator	Huang, Guangqiu
Source	Elsevier; Medline; PMC
abstract	To solve some complicated function optimization problems, an artificial infectious disease optimization algorithm based on the SEIQR epidemic model is constructed, it is called as the SEIQR algorithm, or SEIQRA in short. The algorithm supposes that some human individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease (SARS) exists in the ecosystem and spreads among individuals, the disease attacks only a part of features of an individual. Each infected individual may pass through such states as susceptibility (S), exposure (E), infection (I), quarantine (Q) and recovery (R). State S, E, I, Q and R can automatically and dynamically divide all people in the ecosystem into five classes, it provides the diversity for SEIQRA; that people can be attacked by the infectious disease and then transfer it to other people can cause information exchange among people, information exchange can make a person to transit from one state to another; state transitions can be transformed into operators of SEIQRA; the algorithm has 13 legal state transitions, which corresponds to 13 operators; the transmission rules of the infectious disease among people is just the logic to control state transitions of individuals among S, E, I, Q and R, it is just the synergy of SEIQRA, the synergy can be transformed into the logic structure of the algorithm. The 13 operators in the algorithm provide a native opportunity to integrate many operations with different purposes; these operations include average, differential, expansion, chevy, reflection and crossover. The 13 operators are executed equi-probably; a stable heart rhythm of the algorithm is realized. Because the infectious disease can only attack a small part of organs of a person when it spreads among people, the part variables iteration strategy (PVI) can be ingeniously applied, thus enabling the algorithm to possess of high performance of computation, high suitability for solving some kinds of complicated optimization problems, especially high dimensional optimization problems. Results show that SEIQRA has characteristics of strong search capability and global convergence, and has a high convergence speed for some complicated functions optimization problems.
has issue date	2015-10-09(xsd:dateTime)
bibo:doi	10.1016/j.swevo.2015.09.007
bibo:pmid	32288989
has license	no-cc
sha1sum (hex)	d3f85c1543e6b050641dadc9c27157a719908234
schema:url	https://doi.org/10.1016/j.swevo.2015.09.007
resource representing a document's title	Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm
has PubMed Central identifier	PMC7104270
has PubMed identifier	32288989
schema:publication	Swarm Evol Comput
resource representing a document's body	covid:d3f85c1543e6b050641dadc9c27157a719908234#body_text
is schema:about of	named entity 'AUTOMATICALLY' named entity 'CAUSE' named entity 'STRUCTURE' named entity 'STATE' named entity 'PROBLEMS' named entity 'FEATURES' named entity 'DIFFERENTIAL' named entity 'TRANSFER' named entity 'infection' named entity 'infectious disease' named entity 'optimization problems' named entity 'optimization problems' named entity 'quarantine' named entity 'state transitions' named entity 'infectious disease' named entity 'logic' named entity 'high performance' named entity 'ecosystem' named entity 'state transitions' named entity 'algorithm' named entity 'SARS' named entity 'optimization problems' named entity 'infectious disease' named entity 'optimization' named entity 'optimization algorithm' named entity 'high-dimensional' named entity 'infection' named entity 'infection' named entity 'ecosystem' named entity 'optimization problems' named entity 'objective function' named entity 'probability' named entity 'state transition' named entity 'infectious disease' named entity 'mortality rate' named entity 'differentiable' named entity 'SaDE' named entity 'probability' named entity 'global optimum' named entity 'infectious disease' named entity 'Formula' named entity 'search space' named entity 'CPU time' named entity 'linear programming problems' named entity 'optimization algorithm' named entity 'objective function' named entity 'CPU time' named entity 'algorithm' named entity 'algorithm' named entity 'ecosystem' named entity 'ecosystem' named entity 'algorithm' named entity 'heuristic search' named entity 'objective function' named entity 'ecosystem' named entity 'state transitions' named entity 'infectious disease' named entity 'infectious diseases' named entity 'SARS' named entity 'evolution' named entity 'Randomly select' named entity 'convex function' named entity 'optimization problem' named entity 'infection' named entity 'Vaccination' named entity 'objective function' named entity 'infection' named entity 'state transitions' named entity 'PSO' named entity 'SARS' named entity 'SARS' named entity 'vaccination' named entity 'optimization algorithm' named entity 'global optimum' named entity 'optimization problems' named entity 'optimization problem' named entity 'infection' named entity 'infection' named entity 'epidemic model' named entity 'Population Dynamics' named entity 'infectious disease'

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