动态多目标优化的预测遗传算法

为了在动态环境中很好地跟踪最优解,考虑动态优化问题的特点,提出一种新的多目标预测遗传算法.首先对 Pareto 前沿面进行聚类以求得解集的质心;其次应用该质心与参考点描述 Pareto 前沿面;再次通过预测方法给出预测点集,使得算法在环境变化后能够有指导地增加种群多样性,以便快速跟踪最优解;最后应用标准动态测试问题进行算法测试,仿真分析结果表明所提出算法能适应动态环境,快速跟踪 Pareto 前沿面.     

A pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization

Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods.     

基于混合果蝇-遗传算法求解柔性作业车间调度问题

根据柔性作业车间调度问题的特点,针对不同生产效率的并行设备,以完工时间最小化为目标建立优化模型,提出了混合果蝇优化算法和遗传算法的两阶段组合算法(FOA-GA). 在嗅觉阶段,通过局部路径搜索技术进行生产路径寻优;在视觉阶段,结合遗传算法的交叉和竞争机制,进行个体间的信息交换,利用寻优变异算子和常规变异算子进行两部分变异,再引入自适应动态转移算子进行调整以加快收敛速度. 在生产实例中,将FOA-GA算法与果蝇优化算法和遗传算法的结果进行比较,证明了其可行性和有效性.     

Improved genetic algorithm inspired by biological evolution

The process of mutation has been studied extensively in the field of biology and it has been shown that it is one of the major factors that aid the process of evolution. Inspired by this a novel genetic algorithm (GA) is presented here. Various mutation operators such as small mutation, gene mutation and chromosome mutation have been applied in this genetic algorithm. In order to facilitate the implementation of the above-mentioned mutation operators a modified way of representing the variables has been presented. It resembles the way genetic information is coded in living beings. Different mutation operators pose a challenge as regards the determination of the optimal rate of mutation. This problem is overcome by using adaptive mutation operators. The main purpose behind this approach was to improve the efficiency of GAs and to find widely distributed Pareto-optimal solutions. This algorithm was tested on some benchmark test functions and compared with other GAs. It was observed that the introduction of these mutations do improve the genetic algorithms in terms of convergence and the quality of the solutions.     

模拟退火算法与遗传算法结合及多目标优化求解研究

多目标优化问题是目前遗传算法应用研究的一个重点。本文针对经典遗传算法在多目标优化计算中,难以获得足够的比较均匀的Ｐａｒｅｔｏ优集的不足,提出一种热力学遗传算法,研究热力学中熵和温度的概念,并综合利用约束交叉、适应度共享技术来进行目标函数的优化计算。实验结果显示,这种改进型遗传算法能得到一个较好的Ｐａｒｅｔｏ优集。     

数据立方体选择的改进遗传算法

数据立方体选择问题是一个NP完全问题。研究了利用遗传算法来解决立方体选择问题,提出了一个结合局部搜索机制的遗传算法。这一算法的核心思想在于,首先运用一个基于单位空间最大收益值的预处理算法来生成初始解,然后该初始解经结合了局部搜索机制的遗传算法进行提高。实验结果表明,该算法在寻优性能上优于启发式算法和经典遗传算法。     

适应值共享拥挤遗传算法

保持遗传算法在演化过程中的种群多样性,是将遗传算法成功应用于解决多峰优化问题和多目标优化问题的关键。适应值共享遗传算法和拥护遗传算法分别从不同角度改善了遗传算法的搜索能力,是寻找多个最优解的常用算法。将这两种算法的优点加以结合,提出适应值共享拥护遗传算法。数值测试结果表明,该算法比标准适应值共享遗传算法和确定性拥挤遗传算法具有更强的搜索能力。     

A novel parallel hybrid intelligence optimization algorithm for a function approximation problem

A novel parallel hybrid intelligence optimization algorithm (PHIOA) is proposed based on combining the merits of particle swarm optimization with genetic algorithms. The PHIOA uses the ideas of selection, crossover and mutation from genetic algorithms (GAs) and the update velocity and situation of particle swarm optimization (PSO) under the independence of PSO and GAs. The proposed algorithm divides the individuals into two equation groups according to their fitness values. The subgroup of the top fitness values is evolved by GAs and the other subgroup is evolved by the PSO algorithm. The optimal number is selected as a global optimum at every circulation which shows better results than both PSO and GAs, then improves the overall performance of the algorithm. The PHIOA is used to optimize the structure and parameters of the fuzzy neural network. Finally, the experimental results have demonstrated the superiority of the proposed PHIOA to search the global optimal solution. The PHIOA can improve the error accuracy while speeding up the convergence process, and effectively avoid the premature convergence to compare with the existing methods.     

A hybrid immigrants scheme for genetic algorithms in dynamic environments

Dynamic optimization problems are a kind of optimization problems that involve changes over time.They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time.Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years.Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments.One approach is to maintain the diversity of the population via random immigrants.This paper proposes a hybrid immigrants scheme that combines the concepts of elitism,dualism and random immigrants for genetic algorithms to address dynamic optimization problems.In this hybrid scheme,the best individual,i.e.,the elite,from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme.These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population,replacing the worst individuals in the population.These three kinds of immigrants aim to address environmental changes of slight,medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes.Based on a series of systematically constructed dynamic test problems,experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme.Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.     

基于局部进化的Hopfield神经网络的优化计算方法

提出一种基于局部进化的Hopfield神经网络优化计算方法，该方法将遗传算法和Hopfield神经网络结合在一起，克服了Hopfield神经网络易收敛到局部最优值的缺点，以及遗传算法收敛速度慢的缺点。该方法首先由Hopfield神经网络进行状态方程的迭代计算降低网络能量，收敛后的Hopfield神经网络在局部范围内进行遗传算法寻优，以跳出可能的局部最优值陷阱，再由Hopfield神经网络进一步迭代优化。这种局部进化的Hopfield神经网络优化计算方法尤其适合于大规模的优化问题，对图像分割问题和规模较大的200城市旅行商问题的优化计算结果表明，其全局收敛率和收敛速度明显提高。     

QoS全局最优的多目标Web服务选择算法*

针对现有方法的不足,提出一种基于QoS全局最优的多目标动态Web服务选择算法。在给出动态服务组合模型的基础上,以“抽象服务规划”为输入,以用户的非功能性需求为全局约束,将动态服务选择问题转换为一个带QoS约束的多目标服务组合优化问题;利用多目标蚁群算法,多个目标函数被同时优化并产生一组满足约束条件的Pareto优化解。通过运用实验与基于多目标遗传算法的Web服务选择算法进行对比,证明了该方法的可行性和有效性。     

高斯扰动粒子群算法的数据库查询优化

针对量子粒子群算法存在的不足,将变异算子引入其中,提出一种高斯变异量子粒子群算法（GM-QPSO）,并将其应用于数据库查询优化中。首先建立数据库查询优化数学模型,然后采用量子粒子代表一个可行的数据库查询方案,然后通过量子粒子之间的信息交流,找到数据库查询最优解,最后在 Matlab 2012上进行了仿真实验。仿真结果表明, GM-QPSO克服了量子粒子群算法存在的不足,不仅提高了数据库查询速度,而且获得了更加理想的查询优化方案。     

遗传算法在钟表机芯设计中的应用

在钟表机芯设计中，齿轮参数的优化设计是一个组合优化问题，很难用传统优化方法解决.遗传算法是一种基于生物进化原理的启发式搜索方法，近年来，它成功地解决了许多计算难题.使用该算法的难点是如何将具体问题映射成适于该算法的编码以及根据编码进行各种操作.该文对传动系统各齿轮参数序号进行编码，成功地解决了齿轮参数的优化设计问题，也为一般机械设计中传动系统参数的优化提供了经验.通过比较，利用遗传算法得出的参数比用专家系统得出的参数更优.     

基于特征选择的智能化分组遗传算法

典型遗传算法在进化过程中易陷入局部收敛、过早收敛,效率低,针对这些问题,提出一种基于特征选择的智能化分组遗传算法,利用特征选择原理和分组优化思想对进化过程中的基因进行智能分组的遗传操作,在适应度函数中引入个体特征构建动态的环境适应度评价模型。算法通过分组的遗传操作,保证了父代的优秀模式遗传到下一代,加快了收敛速度,分组变异算子扩大了搜索范围,使结果容易走出局部最优解。应用实验验证表明,算法对局部最优解有较强的免疫能力,有效搜索到全局最优解的进化代数较典型遗传算法明显减少,收敛精度高,证明了算法的有效性。     

免疫克隆多目标优化算法求解约束优化问题

针对现有的约束处理技术的一些不足之处,提出一种用于求解约束优化问题的算法——免疫克隆多目标优化算法(immune clonal multi-objective optimization algorithm,简称ICMOA).算法的主要特点是通过将约束条件转化为一个目标,从而将问题转化为两个目标的多目标优化问题.引入多目标优化中的Pareto-支配的概念,每一个个体根据其被支配的程度进行克隆、变异及选择等操作.克隆操作实现了全局择优,有利于得到高质量的解;变异操作提高算法的局部搜索能力,有利于所得解的多样性;选择操作有利于算法向着最优搜索,而且加快了收敛速度.基于抗体群的随机状态转移过程,证明该算法具有全局收敛性.通过对13个标准测试问题的测试,并与已有算法进行比较。结果表明,该算法在收敛速度和求解精度上均具有一定的优势.     

An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality

Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The decomposed approach improves scalability of the all-in-one problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. A preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference.     

基于改进的遗传算法在函数优化中的应用

针对传统遗传算法在函数优化过程中容易陷入局部最优解、收敛慢等缺点,提出了一种新的自适应遗传算法NAGA。该算法考虑了种群适应度的多种集中分散程度,并且非线性地自适应调节遗传算法的交叉概率与变异概率;为了加快寻优效率,在选择算子方面将引进的选择算子与最优保存策略相结合;为了使遗传操作过程中种群数量恒定,又提出了保留亲本的策略。通过仿真实验发现,与经典遗传算法GA和IAGA相比,改进的自适应遗传算法在收敛速度与精准度等方面都有较大的进步。     

A novel optimization approach for minimum cost design of trusses

This paper describes new optimization strategies that offer significant improvements in performance over existing methods for bridge-truss design. In this study, a real-world cost function that consists of costs on the weight of the truss and the number of products in the design is considered. We propose a new sizing approach that involves two algorithms applied in sequence – (1) a novel approach to generate a “good” initial solution and (2) a local search that attempts to generate the optimal solution by starting with the final solution from the previous algorithm. A clustering technique, which identifies members that are likely to have the same product type, is used with cost functions that consider a cost on the number of products. The proposed approach gives solutions that are much lower in cost compared to those generated in a comprehensive study of the same problem using genetic algorithms (GA). Also, the number of evaluations needed to arrive at the optimal solution is an order of magnitude lower than that needed in GAs. Since existing optimization techniques use cost functions like those of minimum-weight truss problems to illustrate their performance, the proposed approach is also applied to the same examples in order to compare its relative performance. The proposed approach is shown to generate solutions of not only better quality but also much more efficiently. To highlight the use of this sizing approach in a broader optimization framework, a simple geometry optimization algorithm that uses the sizing approach is presented. This algorithm is also shown to provide solutions better than the existing results in literature.     

遗传算法用于结晶过程动力学参数辩识

遗传算法是一类随机优化方法。常被用于解决复杂的优化问题,基于群体的搜索,重组和变异是遗传算法区别于其他优化方法的主要特征。文章中将遗传算法应用于过饱和溶液Ｌｉ２Ｏ．３Ｂ２Ｏ３－Ｈ２Ｏ体系结晶过程动力学参数辨识,确定了结晶反应速率常数、热力学平衡浓度和表观反应级数。     

Accurate sub-swarms particle swarm optimization algorithm for service composition

Service composition (SC) generates various composite applications quickly by using a novel service interaction model. Before composing services together, the most important thing is to find optimal candidate service instances compliant with non-functional requirements. Particle swarm optimization (PSO) is known as an effective and efficient algorithm, which is widely used in this process. However, the premature convergence and diversity loss of PSO always results in suboptimal solutions. In this paper, we propose an accurate sub-swarms particle swarm optimization (ASPSO) algorithm by adopting parallel and serial niching techniques. The ASPSO algorithm locates optimal solutions by using sub-swarms searching grid cells in which the density of feasible solutions is high. Simulation results demonstrate that the proposed algorithm improves the accuracy of the standard PSO algorithm in searching the optimal solution of service selection problem.