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PARALLELIZATION APPROACHES OF MODIFIED TEACHING LEARNING BASED SEARCH OPTIMIZATION TECHNIQUE FOR VARIABLE SELECTION

机译:基于变量选择的搜索优化技术的并行化学习方法

摘要

Systems and methods include initializing a trainees population (TP), calculating an objective function (OF) of the TP to identify a trainer. A teaching pool is created using variables of each trainee and the identified trainer, and unique variables are added to obtain an updated teaching pool (UTP), a search is performed in parallel on UTPs to obtain ‘m’ subset of variables and OFs. OFs of ‘m’ subset are compared with OFs of the trainee's and variables of a first trainee in each thread are updated accordingly. In parallel, an updated learning pool (ULP) is created for selected trainee and the trainees, by adding unique variables to obtain ‘n’ subset which are compared with objective functions of selected trainee and the trainees and variables of a second trainee are updated accordingly. These steps are iteratively performed to obtain an optimal subset of variables that is selected for teaching and learning phase.
机译:系统和方法包括初始化受训者人数(TP),计算TP的目标函数(OF)以识别培训者。使用每个受训者和所标识的培训者的变量创建一个教学池,并添加唯一变量以获得更新的教学池(UTP),在UTP上并行执行搜索以获得变量和OF的“ m”个子集。将“ m”子集的OF与受训者的OF进行比较,并相应地更新每个线程中第一受训者的变量。并行地,通过添加唯一变量来获得“ n”子集,从而为选定的受训者和受训者创建一个更新的学习池(ULP),将其与选定受训者的目标函数进行比较,并相应地更新第二个受训者的变量。重复执行这些步骤,以获得选择用于教学阶段的变量的最佳子集。

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