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Racing Sampling based Microimmune Optimization Approach Solving Constrained Expected Value Programming


Affiliations
1 College of Computer Science, Guizhou University, Guiyang 550025, China
2 Department of Big Data Science and Engineering, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
 

This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higherdimensional benchmark problems and is of potential for further applications.
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  • Racing Sampling based Microimmune Optimization Approach Solving Constrained Expected Value Programming

Abstract Views: 100  |  PDF Views: 2

Authors

Kai Yang
College of Computer Science, Guizhou University, Guiyang 550025, China
Zhuhong Zhang
Department of Big Data Science and Engineering, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Abstract


This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higherdimensional benchmark problems and is of potential for further applications.