多种群伪正态分布估计算法

为了提高基于正态分布模型的分布估计算法子代候选解的质量,防止早熟收敛,文中提出多种群伪正态分布估计算法.首先,采用佳点集方法进行种群初始化,将种群分为3个子群.然后,采用样本重心取代样本均值的方式,获得伪正态分布模型.最后,融合种群与子群伪正态分布模型,得到子群进化的概率模型.23个基准函数的对比测试表明,文中算法在求解质量和收敛速度上较优.针对多约束条件下的并行装配优化问题,提出工序池、员工池、罚函数等措施,将具有工序约束和人员约束的离散组合优化问题转化为无约束的多种群伪正态分布估计优化问题.工程应用结果表明,只需要将候选解的无限集合修正为有限集合,文中算法可方便地用于离散组合优化问题的快速求解.

Multiple Populations Based Estimation of Pseudo-Normal Distribution Algorithm

To improve the quality of the candidate solutions and prevent the premature convergence simultaneously, a multiple populations based estimation of pseudo-normal distribution algorithm(MEPDA) is presented. The population is initialized by the good point set method and it is divided into three subgroups. By replacing sample mean with the gravity center of the samples, a pseudo-normal distribution model is obtained consequently. The probabilistic model for the subgroup evolution is built up by a linear combination of the pseudo-normal distribution models of the population and the subgroup. The comparative optimization tests on 23 benchmark functions show that MEPDA produces higher convergence speed and accuracy of the solutions. To solve the parallel assembly optimization problem with multiple constraints, the process pool, employee pool, penalty function and other measures are proposed to transform the discrete combinational optimization problem with constrained procedures and operators to an unconstrained multi-population based estimation of pseudo-normal distribution optimization problem. An engineering application demonstrates that MEPDA can be applied to the discrete combination optimization problem by just replacing the infinite set of the candidate solutions with a finite one.