基于自适应反向学习的多目标分布估计算法

针对基于规则模型的多目标分布估计算法全局收敛性较弱的缺陷,提出了一种基于自适应反向学习(OBL)的多目标分布估计算法.该算法根据函数变化率的大小来决定是否进行OBL:当函数变化率较小时,算法可能陷入局部最优,所以进行OBL以提高当前种群中个体的多样性;当函数变化率较大时,运行基于规则模型的多目标分布估计算法.所提算法通...

Multi-objective estimation of distribution algorithm with adaptive opposition-based learning

Aiming at the defect of poor global convergence of the regularity model-based multi-objective estimation of distribution algorithm,a multi-objective estimation of distribution algorithm based on adaptive Opposition-Based Learning(OBL)was proposed.In the algorithm,whether to carry out OBL was judged according to the change rate of the function.When the change rate of the function was small,the algorithm was easily to fall into the local optimum,so that OBL was performed to increase the diversity of individuals in current population.When the change rate of the function was large,the regularity model-based multi-objective estimation of distribution algorithm was run.In the proposed algorithm,with the timely introduction of OBL strategy,the influences of population diversity and individual distribution on the overall convergence quality and speed of optimization algorithm were reduced.In order to verify the performance of the improved algorithm,Regularity Model-based Multi-objective Estimation of Distribution Algorithm(RM-MEDA),Hybrid Wading across Stream Algorithm-Estimation Distribution Algorithm(HWSA-EDA)and Inverse Modeling based multiObjective Evolutionary Algorithm(IM-MOEA)were selected as comparison algorithms to carry out the test with the proposed algorithm on ZDT and DTLZ test functions respectively.The test results show that the proposed algorithm not only has good global convergence,but also improves the distribution and uniformity of solutions except on DTLZ2 function.