A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

通过拓展大学化学实验培养学生探索创新能力——以“硫酸铜中铜含量测定(碘量法)”为例

在大学化学实验“硫酸铜中铜含量测定(碘量法)”过程中,探索加入KSCN时机及不加入KSCN对滴定测试结果的影响,并且采用不同浓度的硫酸铜溶液进行验证。实验结果显示,碘量法测定铜含量过程中,临近滴定终点加入KSCN、滴定到浅黄色时加入淀粉之后加入KSCN,两者测试结果基本一致;但在加入KI之后加入KSCN、不加入KSCN,铜含量测定结果明显偏低。该研究可应用于本科实验教学,按不同实验条件将学生分组,在分别实验基础上,引导学生对各组实验结果进行对比、分析及讨论,从而培养学生探索创新意识,有效改进“唯书本”的教学方法。

分光光度法测定铬黑T解离常数实验设计

在分析化学实验教学基础上,引入光谱分析和数据处理,设计了用紫外-可见分光光度法测定铬黑T解离常数的实验。根据系列pH下铬黑T溶液的吸光度,利用ORIGIN进行数学拟合,推导出相应的数学模型,计算铬黑T水溶液的解离常数pKa1、pKa2、pKa3。实验方案简单高效,结果准确度高,可以拓展本科化学实验内容,深入理解酸碱解离过程,加强对仪器分析的认识和应用,培养学生实验设计、数据处理、拟合建模、推导判断的综合能力。