Professor Kai Zhang Has Published Academic Papers On IEEE Transactions on Cybernetics

Editors:EnglishRelease time:2019-11-25Clicks:11

    Recently,The research paper Evolutionary Algorithm for Knee Based Multiple Criteria Decision Making by Professor Zhang Kai from the School of Computer Science and Technology is published in the international authoritative academic journal IEEETransactions on Cybernetics.Professor Kai Zhang is the first author of this paper, and Professor Gary G. En of Oklahoma State University is the corresponding author of this paper.

  In recent years, a variety of effective evolutionary algorithms have been proposed to solve multi-objective optimization problems. These multi-objective evolutionary algorithms are dedicated to search for non-dominated Pareto optimal solution sets with good convergence and diversity.Generally, these algorithms require a large population to search and maintain the non-dominated Pareto optimal solution set, so the computational time is very large.However, when a satisfactory Pareto optimal solution set is obtained at huge computational cost, the decision maker still needs to choose from a large number of mutually non-dominated solution sets, and usually only one or a few are selected to solve practical problems.For large-scale multi-variable, high-dimensional multi-objective optimization problems, the traditional multi-objective evolutionary algorithm is difficult to solve the satisfactory Pareto optimal solution set.

    In this paper, a multi-objective evolutionary algorithm for solving global and local KneeSolution is designed directly for multi-objective decision making. It does not need to keep a large population to search and maintain the non-dominated Pareto optimal solution set, which greatly reduces the computing overhead and the decision maker's choice burden.Experimental results show that the proposed algorithm can efficiently find global and local KneeSolutions of multi-variable and multi-inflection multi-objective optimization problems, and provide support for multi-objective decision making. It has lower time complexity and better performance than similar algorithms.

    IEEETransactionsonCybernetics (IEEETCYB) is Q1 section of the Chinese Academy of Sciences journal, belongs to the Chinese computer artificial intelligence class B recommended by the international journals, class A recommended by the international journal of automation in China, the latest impact factor of 10.387 in 2019.This is a computer college for the first time on the IEEETransactionsonCybernetics in a unit of the first author published papers, it will effectively promote the artificial intelligence subject frontier research work, expanding computer college academic influence in the field of artificial intelligence.