文献类型：期刊文献

英文题名：Interactive Markov Models of Optimization Search Strategies

作者：Ma, Haiping[1,2];Simon, Dan[3];Fei, Minrui[2];Mo, Hongwei[4]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing 312000, Peoples R China;[2]Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China;[3]Cleveland State Univ, Dept Elect Engn & Comp Sci, Cleveland, OH 44115 USA;[4]Harbin Engn Univ, Dept Automat, Harbin 150001, Peoples R China

年份：2017

卷号：47

期号：5

起止页码：808

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000399790600009)、、EI(收录号：20171703606770)、Scopus(收录号：2-s2.0-85018463871)、WOS

基金：This work was supported in part by the National Science Foundation (NSF) under Grant 1344954, in part by the National Natural Science Foundation of China under Grant 61305078 and Grant 61533010, and in part by the Shaoxing City Public Technology Applied Research Project under Grant 2013B70004. The work of D. Simon was supported in part by the NASA Glenn Research Center, in part by the Cleveland Clinic, in part by the NSF, and in part by the several industrial organizations. The work of H. Ma and M. Fei was supported in part by the NSF, and in part by several industrial organizations.

语种：英文

外文关键词：Evolutionary algorithm (EA); interactive Markov model; Markov model; optimization search strategy; population-proportion-based selection

外文摘要：This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method.