利用遗传算法实现试题库自动组卷问题

提出并实现了利用遗传算法求解试题库组卷的数学模型,定义了组卷问题的适应度函数,讨论了运用遗传算法求解在一定约束条件下的多目标参数优化问题,通过初始化种群、选择算子、交叉算子和变异算子,等过程不断进化,最后得到最优解,实验结果表明,遗传算法相对于其它算法更能有效的解决试题库自动组卷问题,提出了实现不相邻试卷分配的补遗随机算法,为求解类似的多目标约束问题及不相邻组合问题提供一种新的方法。     

多目标遗传算法及在过程优化综合中的应用

化工过程的多目标优化综合问题可归结为多目标混合整数非线性规划(MOMINLP)模型的求解,求解方法主要有数学规划法和多目标进化算法。以多目标遗传算法(MOGA)为代表的进化算法被认为是特别适合求解此类问题。遗传算法大多用于单目标问题的优化,近十几年来将遗传算法应用到多目标优化的研究得到了很大的发展。本文对多目标遗传算法的一些重要概念、发展历程进行了回顾。针对化工过程的模型特点,对MOGA在过程综合中的应用研究进行了讨论,并认为混合遗传算法应是求解此类问题的有效算法。     

约束优化问题的改进遗传算法设计

遗传算子是影响遗传算法优化效果的重要因素,针对目前遗传算法研究中对约束优化问题求解的不足,提出基于退火思想的退火选择算子和加权适应度算子,并给出了退火选择算子和加权适应度算子设计方法及其计算过程.在此基础上与现有的遗传算子结合,提出一种新的改进遗传算法,分析了改进遗传算法与基于罚函数遗传算法之间在原理上的区别.最后以两个测试函数为算例对算法进行了性能测试,结果表明改进的遗传算法具有良好的优化性能,能获得更好的优化结果.     

基于多目标遗传算法的侦察资源优化

基于地理信息系统的侦察资源优化,是个多目标多约束的资源分配问题。文章根据相关知识提出侦察资源优化模型,在地形分析结果基础上利用多目标遗传算法进行求解。算法采用多参数映射编码,通过启发式初始化方法和专门的遗传算子保证初始个体的有效,此外惩罚函数对应问题的约束条件,可以确保适应度函数对算法进化的正确引导。仿真结果证明该方法有效。     

多目标优化设计在杀螺剂配方研究中的应用

针对某生物杀螺剂制作中多目标约束问题,提出了一种应用Pareto遗传算法来解决问题的优化方法。建立了用于多目标优化的适应度函数,使用排列选择方法将带约束的多目标问题转换为无约束优化问题;并根据计算中的收敛情况引入了适当的移民算子,改善了遗传算法的进化性能,得到了Pareto最优解集,成功地解决了该生物杀螺剂的最优配方问题。     

经费分配中基于多目标优化的遗传规划模型

在传统遗传规划中引入多目标优化原理,探索新的经费分配方法和管理模式,建立了一种多目标优化的非线性遗传规划模型,提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题.对求解过程中的选择算子、交叉算子和变异算子等进行正交试验,得到的种群个体明显优于基本遗传算法的个体.这种基于多目标优化的遗传规划模型能产生精度更高的最优解,通过对经费分配问题的实验验证,得到了较好的结果.     

多目标设备经费分配的混合遗传优化方法

为了探索新的经费分配方法和管理模式,建立了一种新的多目标非线性规划优化模型,提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题。对求解过程中的选择算子、交叉算子和变异算子等进行正交试验,得到的种群个体明显优于基本遗传算法的个体。仿真结果表明,该算法收敛寻优能力强,并能产生很多次优解,是一种高效的方法。     

多目标设备经费分配的混合遗传优化方法

为了探索新的经费分配方法和管理模式，建立了一种新的多目标非线性规划优化模型。提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题。对求解过程中的选择算子、交叉算子和变异算子等进行正交试验，得到的种群个体明显优于基本遗传算法的个体。仿真结果表明，该算法收敛寻优能力强，并能产生很多次优解，是一种高效的方法。     

解非线性规划的多目标遗传算法及其收敛性

给出非线性约束规划问题的一种新解法。它既不需用传统的惩罚函数,又不需区分可行解和不可行解,新方法把带约束的非线性规划问题转化成为两个目标函数优化问题,其中一个是原约束问题的目标函数,另一个是违反约束的度函数,并利用多目标优化中的Pareto优劣关系设计了一种新的选择算子,通过对搜索操作和参数的合理设计给出了一种新型遗传算法,且给出了算法的收敛性证明,最后数据实验表明该算法对带约束的非线性规划问题求解是非常有效的。     

用于约束多目标优化问题的热力学遗传算法

为了保持所求得的约束多目标优化问题Pareto最优解的适应度与多样性,在NSGA-Ⅱ基础上提出了一种用于求解有约束的多目标优化问题的热力学遗传算法.结合热力学中自由能与熵的概念,利用热力学中熵与能量的竞争来保持种群的适应度与多样性的平衡,设计了热力学算子.根据非支配排序Pareto分层结构建立分层小生境来改进选择算子,弥补了选择算子不足.实验结果表明:该算法不仅得到的解在空间分布均匀,收敛性好,同时解集具有较广的分布空间.     

A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization

The difficulties associated with using classical mathematical programming methods on complex optimization problems have contributed to the development of alternative and efficient numerical approaches. Recently, to overcome the limitations of classical optimization methods, researchers have proposed a wide variety of meta-heuristics for searching near-optimum solutions to problems. Among the existing meta-heuristic algorithms, a relatively new optimization paradigm is the Shuffled Complex Evolution at the University of Arizona (SCE-UA) which is a global optimization strategy that combines concepts of the competition evolution theory, downhill simplex procedure of Nelder-Mead, controlled random search and complex shuffling. In an attempt to reduce processing time and improve the quality of solutions, particularly to avoid being trapped in local optima, in this paper is proposed a hybrid SCE-UA approach. The proposed hybrid algorithm is the combination of SCE-UA (without Nelder-Mead downhill simplex procedure) and a pattern search approach, called SCE-PS, for unconstrained optimization. Pattern search methods are derivative-free, meaning that they do not use explicit or approximate derivatives. Moreover, pattern search algorithms are direct search methods well suitable for the global optimization of highly nonlinear, multiparameter, and multimodal objective functions. The proposed SCE-PS method is tested with six benchmark optimization problems. Simulation results show that the proposed SCE-PS improves the searching performance when compared with the classical SCE-UA and a genetic algorithm with floating-point representation for all the tested problems. As evidenced by the performance indices based on the mean performance of objective function in 30 runs and mean of computational time, the SCE-PS algorithm has demonstrated to be effective and efficient at locating best-practice optimal solutions for unconstrained optimization.     

A relative difference quotient algorithm for discrete optimization

According to the characteristics of discrete optimization, the concept of a relative difference quotient is proposed, and a highly accurate heuristic algorithm, a relative difference quotient algorithm, is developed for a class of discrete optimization problems with monotonic objective functions and constraint functions. The algorithm starts from the minimum point of the objective function outside the feasible region and advances along the direction of minimum increment of the objective function and maximum decrement of constraint functions to find a better approximate optimum solution. In order to evaluate the performance of the algorithm, a stochastic numerical test and a statistical analysis for the test results are also completed. The algorithm has been successfully applied to the discrete optimization of structures.     

Directed searching optimization algorithm for constrained optimization problems

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.     

基于进化算法的优化平台设计

线性规划非线性规划等优化软件在社会、经济、工程等领域应用潜力巨大。现有优化软件大都采用的是经典的局部优化技术或者简单的全局优化技术。论文将进化算法引入称为优化平台的优化软件设计。对平台的关键技术进行了分析,提出了相应的平台方案,并予以了实现。该平台方案的特点是:界面动态调整增广目标函数中的惩罚因子,使用两个特别的进化算子,采用了特别的并行计算机制和退回机制。经测试,按所提方案实现的平台,操作方便,求解精度高而稳定,有显著的优越性。所提的优化平台方案是令人满意的。     

A hybrid intelligent genetic algorithm

Application of genetic algorithms to optimization of complex problems can lead to a substantial computational effort as a result of the repeated evaluation of the objective function(s) and the population-based nature of the search. This is often the case where the objective function evaluation is costly, for example, when the value is obtained following computationally expensive system simulations. Sometimes a substantially large number of generations might be required to find optimum value of the objective function. Furthermore, in some cases, genetic algorithm can face convergence problems. In this paper, a hybrid optimization algorithm is presented which is based on a combination of the neural network and the genetic algorithm. In the proposed algorithm, a back-propagation neural network is used to improve the convergence of the genetic algorithm in search for global optimum. The efficiency of the proposed computational methodology is illustrated by application to a number of test cases. The results show that, in the proposed hybrid method, the integration of the neural network in the genetic algorithm procedure can accelerate the convergence of the genetic algorithm significantly and improve the quality of solution.     

A hybrid PSO-GA algorithm for optimization of laminated composites

This paper presents a new hybrid Particle Swarm Algorithm (PSO) for optimization of laminated composite structures. The method combines the standard PSO heuristics with Genetic Algorithm operators in order to improve the algorithm performance. Thus, operations that are important to the optimization of laminated composites such as mutation and layer swap are incorporated into the method. A specially designed encoding scheme is used to represent the laminate variables and the associated velocities. A study is carried-out to select the best variant of the proposed method for the optimization of laminated composites, considering different swarm topologies and genetic operators. Both strength maximization and weight minimization problems are considered. A meta-optimization procedure is used to tune the parameters of each variant in order to avoid biased results. The results showed that the proposed method led to excellent results for both traditional and dispersed laminates, representing a significant improvement over the standard PSO algorithm.     

A line up evolutionary algorithm for solving nonlinear constrained optimization problems

In this work a complete framework is presented for solving nonlinear constrained optimization problems, based on the line-up differential evolution (LUDE) algorithm which is proposed for solving unconstrained problems. Linear and/or nonlinear constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The LUDE algorithm maintains a population of solutions, which is continuously improved as it thrives from generation to generation. In each generation the solutions are lined up according to the corresponding objective function values. The position's in the line are very important, since they determine to what extent the crossover and the mutation operators are applied to each particular solution. The efficiency of the proposed methodology is illustrated by solving numerous unconstrained and constrained optimization problems and comparing it with other optimization techniques that can be found in the literature.     

Combining genetic algorithms with BESO for topology optimization

This paper proposes a new algorithm for topology optimization by combining the features of genetic algorithms (GAs) and bi-directional evolutionary structural optimization (BESO). An efficient treatment of individuals and population for finite element models is presented which is different from traditional GAs application in structural design. GAs operators of crossover and mutation suitable for topology optimization problems are developed. The effects of various parameters used in the proposed GA on the optimization speed and performance are examined. Several 2D and 3D examples of compliance minimization problems are provided to demonstrate the efficiency of the proposed new approach and its capability of obtaining convergent solutions. Wherever possible, the numerical results of the proposed algorithm are compared with the solutions of other GA methods and the SIMP method.     

An investigation of genetic algorithms for the optimization of multi-objective fisheries bioeconomic models

The use of genetic algorithms (GA) for optimization problems offers an alternative approach to the traditional solution methods. GA follow the concept of solution evolution, by stochastically developing generations of solution populations using a given fitness statistic, for example the achievement function in goal programs. They are particularly applicable to problems which are large, non-linear and possibly discrete in nature, features that traditionally add to the degree of complexity of solution. Owing to the probabilistic development of populations, GA do not distinguish solutions, e.g. local optima from other solutions, and therefore cannot guarantee optimality even though a global optimum may be reached. In this paper, a non-linear goal program of the North Sea demersal fisheries is used to develop a genetic algorithm for optimization. Comparisons between the GA approach and traditional solution methods are made, in order to measure the relative effectiveness. General observations of the use of GA in multi-objective fisheries bioeconomic models, and other similar models, are discussed.