一种新的混合优化算法及其在车间调度中的应用

提出了一种解决车间调度问题的新方法, 该方法将序优化思想融入巢分区算法框架, 采用"序比较"的方法进行算法的局部寻优. "序"的指数收敛性加快了巢分区算法的局部收敛速度, 从而提高了算法整体的优化效率. 最优计算量分配技术则依据在线数据对计算量进行合理的分配, 进一步提高算法的收敛速度和结果的可靠性. 混合算法继承了巢分区算法的全局搜索特性以及序优化的快速收敛性. 用该算法解决标准 Jobshop 调度问题, 并与序优化方法和模拟退火算法进行比较, 发现本文算法在收敛速度与优化质量方面均优于这些算法.     

A new hybrid optimization algorithm

We develop a new optimization algorithm that combines the genetic algorithm and a recently proposed global optimization algorithm called the nested partitions method. The resulting hybrid algorithm retains the global perspective of the nested partitions method and the local search capabilities of the genetic algorithm. We also present a detailed application of the new algorithm to a NP-hard product design problem and it is found empirically to outperform a pure genetic algorithm implementation, particularly for large problems.     

Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization

Ordinal Optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. In this paper, we present a new approach that can further enhance the efficiency of ordinal optimization. Our approach determines a highly efficient number of simulation replications or samples and significantly reduces the total simulation cost. We also compare several different allocation procedures, including a popular two-stage procedure in simulation literature. Numerical testing shows that our approach is much more efficient than all compared methods. The results further indicate that our approach can obtain a speedup factor of higher than 20 above and beyond the speedup achieved by the use of ordinal optimization for a 210-design example.     

Computing efforts allocation for ordinal optimization and discreteevent simulation

Ordinal optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. In this paper, we present a new approach that can further enhance the efficiency of ordinal optimization. Our approach intelligently determines the optimal number of simulation replications (or samples) and significantly reduces the total simulation cost. Numerical illustrations are included. The results indicate that our approach can obtain an additional 74% computation time reduction above and beyond the reduction obtained through the use of ordinal optimization for a 10-design example     

Optimal computing budget allocation for Monte Carlo simulation with application to product design

Ordinal optimization has emerged as an efficient technique for simulation and optimization, converging exponentially in many cases. In this paper, we present a new computing budget allocation approach that further enhances the efficiency of ordinal optimization. Our approach intelligently determines the best allocation of simulation trials or samples necessary to maximize the probability of identifying the optimal ordinal solution. We illustrate the approach’s benefits and ease of use by applying it to two electronic circuit design problems. Numerical results indicate the approach yields significant savings in computation time above and beyond the use of ordinal optimization.     

基于嵌套分区算法的立体仓库货位分配优化

根据小型立体化仓库运营特点,基于顺序单目标优化思想,提出一种新的仓库货位分配策略。将考虑存储能耗、货架稳定性、运行效率的多目标仓库货位优化问题,转化为单目标优化,建立了仓库货位优化数学模型。根据数学模型特点,采用嵌套分区算法进行优化求解。通过算例分析证明该分配策略与优化方法,可有效处理多目标仓库库位优化问题,优化效果显著。     

嵌套分割算法：一种新的并行随机优化算法*

嵌套分割算法是一种新的系统优化计算方法,它可以应用于确定型和随机型、离散系统和连续系统的优化问题.综述了嵌套分割算法的概念原理、方法步骤,介绍了算法的应用情况,并探讨了算法未来的研究方向.     

一种求解QAP 问题的混合嵌套分区优化算法

提出一种基于嵌套分区算法(NPM)框架求解二次分配问题(QAP)的混合优化算法.算法利用嵌套分区树来描述二次分配过程,对可行域进行系统性分区,采用禁忌抽样算子对分区进行抽样并评估各个分区的性能.在每次迭代中,算法重点跟踪和搜索优良解最有希望出现的分区,并结合禁忌搜索算法来实现分区转移.数值仿真实验表明,引入更加有效的禁忌抽样算子后,NPM算法具有更好的寻优能力.     

基于禁忌搜索的复合嵌套分割算法*

本文介绍了嵌套分割算法（NP）的基本思想,提出了提高其优化效率的途径。介绍了禁忌搜索算法（TS）,并将禁忌搜索的思想引入嵌套分割算法的抽样和选取算子中,提出了一种复合优化算法(TSNP)来解决函数优化问题。TSNP算法结合了嵌套分割算法和禁忌搜索算法的优点,使其在优化性能、优化效率和可靠性方面具有明显的优越性。通过对几个函数优化实例的测试,并和其他算法进行了比较,结果表明该算法具有较好的计算效率和较快的全局寻优能力。     

基于维度分区的果蝇优化新算法

为提高果蝇算法的收敛稳定性,提出了一种基于维度分区的果蝇优化新算法。将果蝇种群均分为两组:跟随果蝇和搜索果蝇。跟随果蝇在全局最优果蝇附近实现精细化局部搜索,而搜索果蝇则将位置向量的每个维度搜索范围划分为若干个区间,通过比较各个区间的最优位置来更新果蝇位置。为加快算法收敛速度,若某搜索果蝇在连续若干次迭代过程中 均 表现最差,则在当前最优果蝇位置附近产生该果蝇的新位置。针对8种典型函数的仿真实验表明:与传统算法相比, 所提算法所需参数较少,收敛稳定性高,并且在收敛精度及收敛速度等方面具有明显优势。