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SIS epidemic model-based optimization

JOURNAL OF computationAL SCIENCE 2014年第1期5卷 32-50页

作者： Huang, Guang-qiu Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

To solve complicated function optimization problems, a function optimization algorithm is constructed based on the Susceptible-Infective-Susceptible (SIS) epidemic model, the function optimization algorithm is called SIS algorithm, or SISA in short. The algorithm supposes that some male and female organisms exist in an ecosystem;each individual is characterized by a number of features;an infectious disease exists in the ecosystem and infects among individuals, the infection rule is that female individuals infect male individuals or male individuals infect female individuals, the disease attacks a part of features of an individual. The infected individuals can be cured;the cured individuals can be infected again after a period of time. The physique strength of an individual is decided synthetically by the infection, cure and susceptibility of certain features. The S-I operator is used to transfer feature information from male to female or female to male, the I-S operator is used to transfer feature information from male to male or female to female, the I-S operator and S-S operator are used to transfer feature information among individuals without sex difference. The individuals with strong physique can continue to grow, while the individuals with weak physique stop growing. Results show that the algorithm has characteristics of global convergence and high convergence speed for the complicated functions optimization problems, especially for high dimensional function optimization problems. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Function optimization Population-based optimization algorithm intelligent optimization computation SIS epidemic model

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

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SWARM AND EVOLUTIONARY computation 2016年 27卷 31-67页

作者： Huang, Guangqiu Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

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 5, 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

关键词： Function optimization intelligent optimization computation Epidemic dynamics SEIQR epidemic model Artificial infectious disease optimization

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Function optimization algorithm based on SIRQV epidemic dynamic model

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JOURNAL OF computationAL SCIENCE 2015年 8卷 62-92页

作者： Huang, Guang-qiu Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

To solve some complicated function optimization problems, the SIRQV algorithm is constructed based on the SIRQV epidemic model. The algorithm supposes that some animal individuals exist in an ecosystem;each individual is characterized by a number of features;an infectious disease exists in the ecosystem and spreads among individuals, the disease attacks a part of features of an individual at each time. Each infected individual may pass through such states as susceptibility (S), infection (I), recovery (R), quarantine (Q) and vaccination (V), which can synthetically decide the physique strength of an individual. Individuals in the algorithm have 5 states such as S, I, R, Q and V, and 13 state transitions, each of which is equivalent to an operator. the 13 operators are logically organized together by the disease transmission logic of the SIRQV epidemic model so as to form a good cooperation and sufficient information exchange among individuals. The algorithm uses the activation, average, combination, reinforcement and assimilation operator to exchange feature information among individuals. The reinforcement operator transfers feature information from some strong individuals with higher individual physique index (IPI) to a weak individual with lower IPI index so as to make the latter grow better;the average operator ensures an individual to obtain average feature information from other individuals so as to reduce the probability that the individual drops into local optima;the activation operator expands an individual's search scope by increasing its vitality;the combination operator has the characteristics of both the activation operator and the average operator;the assimilation operator enables the search to possess of jumping ability along dimension direction;the REINIT operator has exploration and exploitation ability to overcome sticky state of individuals and enhance precision of global optima;the growth operator enables the algorithm to converge globally. Result

关键词： Function optimization intelligent optimization computation Epidemic dynamics SIRQV epidemic model SIRQV algorithm SIS algorithm

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