离散非线性规划问题的改进遗传算法

针对实际离散非线性规划问题,分析了离散与连续变量优化问题和求解方法的不同及特性．根据离散变量与遗传算法的特点,将单纯形搜索与算术交叉思想相结合,提出离散单纯形交叉算子以提高遗传算法的局部寻优能力,将种群逐步向离散极值点进行引导,实现算法的快速离散寻优．同时,设计了离散变异算子,使遗传算子真正在离散空同中进行搜索．基于梯度下降思想提出离散修复算子,提高算法对非线性约束的处理能力．实际离散非线性规划问题的应用研究验证了方法的有效性．     

求解带硬时间窗的多目标车辆路径问题的多种混合蝙蝠算法

针对多目标车辆路径问题的研究,考虑了车载量限制和硬时间窗的约束条件,以最小派车数和最小车辆行驶距离为目标建立了数学模型。在分析基本蝙蝠算法求解离散问题局限性的基础上,混合蝙蝠法加入交叉算子和重组算子,提高算法性能。利用遗传算法的特点,构建出三种混合蝙蝠算法,算例测试结果表明,混合蝙蝠算法是解决离散型问题的一种有效方法。与基本蝙蝠算法相比,混合蝙蝠算法具有较高的计算效率和持续优化能力,其中单点重组精英遗传混合蝙蝠算法解决算例寻优能力最佳。 关键词：混合蝙蝠算法;车辆路径问题;多目标;硬时间窗     

解0—1背包问题的混合编码贪婪DE算法

提出一种混合编码差异演化算法来求解0—1背包问题。通过增加边界约束处理算子和编码映射函数,构建混合编码差异演化算法,求解离散优化问题,并利用贪婪变换方法对演化过程中的不可行解进行修复。仿真实验结果表明了该算法求解0-1背包问题的有效性与适用性。     

无线传感器网络覆盖优化算法研究

无线传感器网络覆盖控制是研究高密度部署的无线传感器节点集选问题.针对网络严重的冗余问题,影响网络的优化机制.为了有效控制网络节点能量、提高感知质量、大大延长网络的生存时间,采用覆盖优化算法.以网络有效覆盖率和节点最小利用率为优化目标,提出一种混合杂交算法的优化覆盖机制,在算法中扩展杂交算子可增强混合算术杂交算子局部搜索能力.采用改进方法进行仿真,结果表明混合杂交方法能快速收敛到更精确的解,从而更好地降低了网络冗余,延长网络的生存时间.     

一类动态非线性约束优化问题的新解法

动态非线性约束优化是一类复杂的动态优化问题,其求解的困难主要在于如何处理问题的约束及时间（环境）变量。给出了一类定义在离散时间（环境）空间上的动态非线性约束优化问题的新解法,从问题的约束条件出发构造了一个新的动态熵函数,利用此函数将原优化问题转化成了两个目标的动态优化问题。进一步设计了新的杂交算子和带局部搜索的变异算子,提出了一种新的多目标优化求解进化算法。通过对两个动态非线性约束优化问题的计算仿真,表明该算法是有效的。     

基于混合离散粒子群优化的Slew约束下X结构Steiner最小树算法

Steiner最小树是超大规模集成电路中布线阶段的最佳模型,进一步考虑能够有效防止信号失真的电压转换速率(Slew)约束这一个更为贴近实际芯片设计模型和更具线长优化能力的X结构,首次提出基于混合离散粒子群优化的Slew约束下X结构Steiner最小树算法.首先,为了避免频繁的Slew约束计算,提出了高效的预处理策略,并且提出一种能够有效考虑Slew约束的针对性的惩罚机制.其次,为了能够有效求解该离散问题,基于遗传算子重新设计了粒子群优化算法的离散更新机制,并提出一种更适合遗传算子的引脚对编码方式.然后,为了进一步优化布线树的长度,提出一种有效的精炼策略.最终,提出一种混合修正策略以完全满足Slew约束.实验表明,所提算法可完全满足电压转换速率约束并取得同类工作中最佳的布线结果.     

改进型PSO算法在VRP中的应用

运用罚函数法将约束优化问题转化为无约束优化问题,同时采用实数编码方案,将离散的车辆路径问题转化成准连续优化问题,在此基础上,用改进的粒子群优化算法求解最优值.改进的粒子群算法引入了杂交PSO模型和变异算子.仿真实验结果表明,该算法在保持粒子种群多样性、提高收敛速度和搜索精度、扩大搜索范围、避免过早收敛于局部极值点等方面...     

基于演化策略算法的作业车间调度优化

提出一种基于工件操作次序的二维实数编码方法,采用演化策略算法求解作业车间调度问题。设计一种基于三点交叉互换的重组算子用于生成子代个体,并采用个体编码基因随机重新生成的方法设计变异算子。实验结果证明,演化策略算法能有效优化作业车间调度问题,与遗传算法和粒子群优化算法相比,其优化性能更好,并且基于三点交叉互换重组算子的演化策略算法的性能好于基于两点交叉和基于四点交叉互换重组算子的演化策略算法。     

解非线性方程组的多目标优化进化算法

如何有效地求解复杂非线性方程组是进化计算领域一个新的研究问题。将非线性方程组等价地转化成多目标优化问题,同时设计了求解的多目标优化进化算法。为了提高算法的搜索能力及避免算法陷入局部最优,采用了自适应Levy变异进化算子和均匀杂交算子。计算机仿真表明该算法对非线性方程组的求解是有效的。     

一种带混合杂交算子的遗传算法及其收敛性

本文将传统遗传算法中的杂交算子与一种新设计的优化方法相结合,提出了一种能改善种群中个体适应度的混合杂交算子,并通过修正适应度函数给出了一种新的求解连续型数值优化问题的遗传算法,并证明了其全局收敛性。数据试验表明,该算法对这些测试函数的结果优于文献中的方法     

Hybrid crossover operators for real-coded genetic algorithms: an experimental study

Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques which combine multiple crossovers have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population. Therefore, the study of the synergy produced by combining the different styles of the traversal of solution space associated with the different crossover operators is an important one. The aim is to investigate whether or not the combination of crossovers perform better than the best single crossover amongst them. In this paper we have undertaken an extensive study in which we have examined the synergetic effects among real-parameter crossover operators with different search biases. This has been done by means of hybrid real-parameter crossover operators, which generate two offspring for every pair of parents, each one with a different crossover operator. Experimental results show that synergy is possible among real-parameter crossover operators, and in addition, that it is responsible for improving performance with respect to the use of a single crossover operator.     

Effects of nongeometric binary crossover on multiobjective 0/1 knapsack problems

Standard binary crossover operators (e.g., one-point, two-point, and uniform) tend to decrease the diversity of solutions while they improve the convergence to the Pareto front. This is because standard binary crossover operators, which are called geometric crossovers, always generate an offspring in the line segment between its parents under the Hamming distance in the genotype space. In our former study, we have already proposed a nongeometric binary crossover operator to generate an offspring outside the line segment between its parents. In this article, we examine the effect of our crossover operator on the performance of evolutionary multiobjective optimization (EMO) algorithms through computational experiments on various multiobjective knapsack problems. Experimental results show that our crossover operator improves the search ability of EMO algorithms for a wide range of test problems. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

遗传算法中遗传算子的启发式构造策略

遗传算法是影响遗传算法搜索性能的重要因素，本文研究交配算子与其搜索子空间的关系，提出了设计良好算子的指导性原则，并构造出一种启发式交配算子。     

Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process

Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.     

正交M ult i-agen t 遗传算法及其性能分析

将Multi—agent系统、遗传算法与正交试验设计方法相结合，提出一种新的遗传算法——正交Multi—agent遗传算法，其主要思想是：利用正交设计的方法产生初始化种群；用正交交叉算子代替传统的算术交叉算子；利用agent间的竞争作用与每个agent所具有的知识和自学习能力进行启发式搜索，以提高进化的速度，仿真试验和性能分析表明，正交Multi—agent遗传算法不但具有很强的全局优化能力和较快的收敛速度，而且具有很强的鲁棒性。     

On the performance of the LP-guided Hopfield network-genetic algorithm

This paper proposes a linear programming (LP)-guided Hopfield-genetic algorithm for a class of combinatorial optimization problems which admit a 0–1 integer linear programming. The algorithm modifies the updating order of the binary Hopfield network in order to obtain better performance of the complete hybrid approach. We theoretically analyze several different updating orders proposed. We also include in the paper a novel proposal to guide the Hopfield network using the crossover and mutation operators of the genetic algorithm. Experimental evidences that show the good performance of the proposed approach in two different combinatorial optimization problems are also included in the paper.     

Multi-objective Genetic Algorithms for grouping problems

Linear Linkage Encoding (LLE) is a convenient representational scheme for Genetic Algorithms (GAs). LLE can be used when a GA is applied to a grouping problem and this representation does not suffer from the redundancy problem that exists in classical encoding schemes. LLE has been mainly used in data clustering. One-point crossover has been utilized in these applications. In fact, the standard recombination operators are not suitable to be used with LLE. These operators can easily disturb the building blocks and cannot fully exploit the power of the representation. In this study, a new crossover operator is introduced for LLE. The operator which is named as group-crossover is tested on the data clustering problem and a very significant performance increase is obtained compared to classical one-point and uniform crossover operations. Graph coloring is the second domain where the proposed framework is tested. This is a challenging combinatorial optimization problem for search methods and no significant success has been obtained on the problem with pure GA. The experimental results denote that GAs powered with LLE can provide satisfactory outcomes in this domain, too.     

遗传算法的混合算子策略

在一般遗传算法中,求最优解时既可避免早熟收敛,又能提高收敛速度是困难的,因为算法中使用了单独一组交叉算子／变异算子。本文提出一种新的基于混合算子的遗传算法执行策略。在求解旅行商问题（TSP）中,为了提高局部搜索能力和收敛速度,给出了一种基于边重组的启发式交叉算子。仿真实验表明了这种算法的有效性。     

A computationally efficient evolutionary algorithm for real-parameter optimization

Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (we call the G3 model). The performance of the G3 model with the PCX operator is investigated on three commonly used test problems and is compared with a number of evolutionary and classical optimization algorithms including other real-parameter GAs with the unimodal normal distribution crossover (UNDX) and the simplex crossover (SPX) operators, the correlated self-adaptive evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), the differential evolution technique, and the quasi-Newton method. The proposed approach is found to consistently and reliably perform better than all other methods used in the study. A scale-up study with problem sizes up to 500 variables shows a polynomial computational complexity of the proposed approach. This extensive study clearly demonstrates the power of the proposed technique in tackling real-parameter optimization problems.