Non-stationary Function Optimization Using Polygenic Inheritance

The pathogenesis of autoimmune diseases is very complex and multi-factorial. The concept of Mosaics of Autoimmunity was introduced to the scientific community 30 years ago by Y. Shoenfeld and D.A. Isenberg, and since then new tiles to the puzzle are continuously added. This concept specifies general pathological ideas about the multifactorial threshold model for polygenic inheritance with a threshold effect by the action of a number of external causal factors as applied to the field of autoimmunology. Among the external factors that can excessively stimulate the immune system, contributing to the development of autoimmune reactions, researchers are particularly interested in chemical substances, which are widely used in pharmacology and medicine. In this review we highlight the autoimmune dynamics i.e. a multistep pathogenesis of autoimmune diseases and the subsequent development of lymphoma in some cases. In this context several issues are addressed namely, genetic basis of autoimmunity; environmental immunostimulatory risk factors; gene/environmental interaction; pre-clinical autoimmunity with the presence of autoantibodies; and the mechanisms, underlying lymphomagenesis in autoimmune pathology. We believe that understanding the common model of the pathogenesis of autoimmune diseases is the first step to their successful management.

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Differentiation Coherence Algorithm for Steady Power Flow Control

The Open Electrical & Electronic Engineering Journal ◽

10.2174/1874129001509010107 ◽

2015 ◽

Vol 9 (1) ◽

pp. 107-116 ◽

Cited By ~ 1

Author(s):

Yang Liu-Lin ◽

Hang Nai-Shan

Keyword(s):

Flow Control ◽

Extreme Point ◽

Power Flow ◽

Reactive Power ◽

Small Deviation ◽

Function Optimization ◽

Flow Mode ◽

Power Flow Control ◽

Active And Reactive Power ◽

Variable Inequality

This paper researched steady power flow control with variable inequality constraints. Since the inverse function of power flow equation is hard to obtain, differentiation coherence algorithm was proposed for variable inequality which is tightly constrained. By this method, tightly constrained variable inequality for variables adjustment relationships was analyzed. The variable constrained sensitivity which reflects variable coherence was obtained to archive accurate extreme equation for function optimization. The hybrid power flow mode of node power with branch power was structured. It also structured the minimum variable model correction equation with convergence and robot being same as conventional power flow. In fundamental analysis, the effect of extreme point was verified by small deviation from constrained extreme equation, and the constrained sensitivity was made for active and reactive power. It pointed out possible deviation by using simplified non-constrained sensitivity to deal with the optimization problem of active and reactive power. The control solutions for power flow for optimal control have been discussed as well. The examples of power flow control and voltage management have shown that the algorithm is simple and concentrated and shows the effect of differential coherence method for extreme point analysis.

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A microbial treatment population dynamics optimization approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210127 ◽

2021 ◽

pp. 1-15

Author(s):

Jinding Gao

Keyword(s):

Population Dynamics ◽

Information Exchange ◽

Information Diffusion ◽

Optimization Problems ◽

Microbial Control ◽

Function Optimization ◽

Optimization Approach ◽

Dynamics Model ◽

Population Dynamics Model ◽

Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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