基于自适应反馈机制的精英教学优化算法

发布时间：2021-02-22 17:41

　　精英教学优化算法（Elitist teaching-learning-based optimization,ETLBO）是一种基于实际班级教学过程的新型优化算法。针对ETLBO算法存在的寻优精度低、稳定性差的问题,提出一种基于自适应反馈机制的精英教学优化算法（Adaptive Feedback ETLBO,AFETLBO）。在学生阶段之后,通过添加自适应反馈机制,将学生分为优等生和差生,且动态调整两者的规模,对差生实行与教师之间的反馈交流,快速向教师靠拢,加强收敛能力;对优等生实行自我学习,进行局部精细搜索。自适应反馈阶段的加入,增加了学习方式,保持了学生的多样性特性,提高全局搜索能力。对6个无约束及5个标准函数的测试结果表明,与其他优化算法相比,AFETLBO算法具有更高的寻优精度和收敛能力。 

【文章来源】：系统仿真学报. 2018,30(08)北大核心

【文章页数】：8 页

【部分图文】：

各算法在f1的表现Fig.2Performanceofalgorithmsinf1

第30卷第8期Vol.30No.82018年8月李荣雨,等:基于自适应反馈机制的精英教学优化算法Aug.,2018http:∥www.china-simulation.com2955表2无约束测试函数对比结果平均值(标准差)Tab.2Comparisonofresultsforunconstrainedbenchmarkfunctionsmean(std)函数维数TLBOETLBOFETLBOAFETLBOf1309.86E–13(7.36E–15)3.97E–165(2.54E–164)3.43E–231(3.21E–231)0.00E+000(0.0E+000)1002.31E–10(9.43E–12)1.50E–163(2.26E–163)2.61E–230(1.12E–230)0.00E+000(0.0E+000)f2306.62E–07(2.35E–08)6.21E–015(1.87E–015)4.44E–015(0.0E+00)1.01E–017(4.23E–016)1009.26E–08(1.01E–07)6.21E–015(1.99E–014)4.44E–015(0.0E+00)1.52E–017(1.68E–017)f3301.52E–16(5.76E–15)0.0E+000(0.0E+000)0.0E+000(0.0E+000)0.0E+000(0.0E+000)1003.64E–12(4.36E–12)0.0E+000(0.0E+000)0.0E+000(0.0E+000)0.0E+000(0.0E+000)f4305.87E+01(1.68E+1)2.67E+001(2.26E–01)2.57E+001(1.23E–01)1.05E+000(6.32E–001)1007.35E+00(1.98E+0)2.67E+001(3.31E–01)2.60E+001(2.16E–01)5.16E+000(1.3E+000)f5304.67E–25(5.12E–24)0.0E+000(0.0E–000)0.0E+000(0.0E–000)0.0E+000(0.0E–000)1008.63E–23(1.93E–24)0.0E+000(0.0E–000)0.0E+000(0.0E–000)0.0E+000(0.0E–000)f6307.27E–07(6.61E–08)1.26E–083(1.76E–083)4.16E–116(1.07E–115)1.09E–139(1.01E–139)1008.91E–07(1.99E–7)2.73E–083(3.96E–083)4.49E–115(8.74E–115)2.71E–138(1.56E–137)图2各算法在f1的表现图3各算法在f2的表现Fig.2Performanceofalgorithmsinf1Fig.3Performanceofalgorithmsinf2图4各算法在f3的

第30卷第8期Vol.30No.82018年8月李荣雨,等:基于自适应反馈机制的精英教学优化算法Aug.,2018http:∥www.china-simulation.com2955表2无约束测试函数对比结果平均值(标准差)Tab.2Comparisonofresultsforunconstrainedbenchmarkfunctionsmean(std)函数维数TLBOETLBOFETLBOAFETLBOf1309.86E–13(7.36E–15)3.97E–165(2.54E–164)3.43E–231(3.21E–231)0.00E+000(0.0E+000)1002.31E–10(9.43E–12)1.50E–163(2.26E–163)2.61E–230(1.12E–230)0.00E+000(0.0E+000)f2306.62E–07(2.35E–08)6.21E–015(1.87E–015)4.44E–015(0.0E+00)1.01E–017(4.23E–016)1009.26E–08(1.01E–07)6.21E–015(1.99E–014)4.44E–015(0.0E+00)1.52E–017(1.68E–017)f3301.52E–16(5.76E–15)0.0E+000(0.0E+000)0.0E+000(0.0E+000)0.0E+000(0.0E+000)1003.64E–12(4.36E–12)0.0E+000(0.0E+000)0.0E+000(0.0E+000)0.0E+000(0.0E+000)f4305.87E+01(1.68E+1)2.67E+001(2.26E–01)2.57E+001(1.23E–01)1.05E+000(6.32E–001)1007.35E+00(1.98E+0)2.67E+001(3.31E–01)2.60E+001(2.16E–01)5.16E+000(1.3E+000)f5304.67E–25(5.12E–24)0.0E+000(0.0E–000)0.0E+000(0.0E–000)0.0E+000(0.0E–000)1008.63E–23(1.93E–24)0.0E+000(0.0E–000)0.0E+000(0.0E–000)0.0E+000(0.0E–000)f6307.27E–07(6.61E–08)1.26E–083(1.76E–083)4.16E–116(1.07E–115)1.09E–139(1.01E–139)1008.91E–07(1.99E–7)2.73E–083(3.96E–083)4.49E–115(8.74E–115)2.71E–138(1.56E–137)图2各算法在f1的表现图3各算法在f2的表现Fig.2Performanceofalgorithmsinf1Fig.3Performanceofalgorithmsinf2图4各算法在f3的

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