多目标遗传算法及其在化工领域的应用

多目标优化在工程优化领域占有较大比重，这些目标之间大多是相互冲突的，常用的方法是将这些目标通过不同的方式转化成单一目标进行求解，然而这样将使一些有用的信息丢失。多目标遗传算法可避免信息丢失，通过优化它给出一组非劣解供决策者根据不同需要进行选择。本文首先介绍了常用的多目标优化方法，然后详细介绍了目前研究较多的多目标遗传算法，着重讨论了多目标优化方法在化学工程领域中的应用，并对多目标遗传算法的发展进行了展望。     

均匀设计在遗传算法中的研究和应用

随着优化问题的复杂化、多目标化，先前的遗传算法在其问题的寻优搜索处理上往往达不到工程实际需要的满意效果。针对这种难以优化的多目标问题，该文先从遗传算法的理论着手，在均匀设计的基础上，提出了一种采用多目标遗传算法求解Pareto最优解集的方法，即均匀矩阵法，它是将均匀设计运用到遗传算法中来，以前随机选取的权值矢量就可以均匀地选取；最后用实例进行仿真，得到了满意的效果。结果表明该方法简单、易于实现，能够很好地解决多目标问题的优化。     

多种人工智能算法的数据库技术课程自动组卷比较

在线考试被广泛应用在远程教育上,自动化组卷是在线考试的关键技术,组卷问题即是多目标期望值的求解问题,其往往存在多个解,人工智能算法对于求解多目标函数有明显优势.采用遗传算法及蚁群算法的多目标优化求解更加高效,能更好胜任于本文数据库技术课程的自动化组卷.在讨论人工智能算法在组卷应用基础上,构建了组卷指标体系,建立多目标约束数学模型,并对多目标期望值进行优化求解.多次实验结果论证表明,人工智能算法的成功率最高,平均达到98%以上（含蚁群算法100%,遗传算法96%）,而非人工智能的算法成功率较低,随机变量法62%,回溯试探法84%.应用人工智能方法特别是遗传算法和蚁群算法,提升了自动化组卷效率,满足了实际各种组卷的需要,使其在远程教育和在线考试中有很好的应用前景.     

玻璃排版优化中遗传算法的应用

研究了应用遗传算法求解非线性多目标组合优化问题———玻璃排版优化问题 ,详细讲解了如何设计求解该优化系统中三个典型组合优化子问题的遗传算法 ,并对三个子问题的求解关系进行了分析 ,总结出遗传算法的不同构造方法对系统优化结果的影响。     

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。     

多目标优化遗传算法的收敛性定义及实例研究

寻找非劣解集合是遗传算法求解多目标优化问题的目标，而标准的遗传算法收敛性分析方法对多目标遗传算法的分析并不合适。本文利用有限马尔科夫链给出了遗传算法求解多目标优化问题的两个收敛性定义，并给出了一个实例研究及进一步的工作计划。     

基于双重特征空间的人脸识别

多目标遗传算法(MOGA)是求解多目标优化问题的有效工具,因而在求解实际问题中得到越来越广泛的应用.PCA是一种基于二阶统计的最小均方误差意义上的最优维数压缩技术,PCA方法所抽取特征的各分量之间是统计不相关的.在人脸识别的实际应用中,将多目标遗传算法引入到PCA所生成的特征空间的优化中,提出基于双重特征空间的人脸识别算法.通过对剑桥ORL库实验表明,该方法与传统的PCA相比,识别率得到明显提高.     

遗传算法求解工程项目方案选择问题

传统解析法在处理多目标问题时存在容易陷入局部最优,计算量大等缺点,限制了其在实际问题中的应用,遗传算法具有很多优良性质,本文就遗传算法对工程项目方案选择问题实际问题进行求解,并对结果进行对比分析,结果表明算法是有效,合适的。     

遗传算法在多目标优化应用中的对比研究

多目标优化应用研究在过程工程领域越来越受重视。本文首先给出了多目标优化问题的一般形式,指出多目标问题求解任务：引导搜索向整个的Pareto优化范围;Pareto优化前沿上保持解集的多样性。在简要论述遗传算法求解多目标技术的基础上,对应用了遗传算法求解多目标的两种方法进行了对比研究,并给出了线性加权遗传算法和一种多目标遗传算法的计算框图。指出线性加权法求解Pareto最优解时不能不能很好地处理非凸区域、均匀分布的权重值不能生成均匀分布的Pareto前沿等局限性,以及多目标遗传算法生成种群多样性及Pareto最优解均匀分布的优点,并用实例进行了验证说明。     

进化优化小生境遗传算法控制参数的研究

小生境遗传算法与遗传算法相比，在求解多峰函数等最优化问题上具有显著的优势，但是小生境距离参数的确定缺乏理论依据，限制了小生境遗传算法的应用。该文提出了一种求解小生境之间距离参数的新方法——基于遗传算法进化优化小生境距离参数。根据多峰目标函数的具体情况，应用遗传算法随机寻优得到若干个最优值，由这些最优值的最小欧氏距离指导小生境距离参数的取值。依据此方法确定小生境之间的距离参数，应用小生境遗传算法成功求解了Shubert多峰函数的所有全局最优值以及六峰值驼背数Back Function的所有局部极小值。     

Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems.     

Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm

There is an ever increasing need to use optimization methods for thermal design of data centers and the hardware populating them. Airflow simulations of cabinets and data centers are computationally intensive and this problem is exacerbated when the simulation model is integrated with a design optimization method. Generally speaking, thermal design of data center hardware can be posed as a constrained multi-objective optimization problem. A popular approach for solving this kind of problem is to use Multi-Objective Genetic Algorithms (MOGAs). However, the large number of simulation evaluations needed for MOGAs has been preventing their applications to realistic engineering design problems. In this paper, details of a substantially more efficient MOGA are formulated and demonstrated through a thermal analysis simulation model of a data center cabinet. First, a reduced-order model of the cabinet problem is constructed using the Proper Orthogonal Decomposition (POD). The POD model is then used to form the objective and constraint functions of an optimization model. Next, this optimization model is integrated with the new MOGA. The new MOGA uses a “kriging” guided operation in addition to conventional genetic algorithm operations to search the design space for global optimal design solutions. This approach for optimal design is essential to handle complex multi-objective situations, where the optimal solutions may be non-obvious from simple analyses or intuition. It is shown that in optimizing the data center cabinet problem, the new MOGA outperforms a conventional MOGA by estimating the Pareto front using 50% fewer simulation calls, which makes its use very promising for complex thermal design problems. Recommended by: Monem Beitelmal     

Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling

Applications of multi-objective genetic algorithms (MOGAs) in engineering optimization problems often require numerous function calls. One way to reduce the number of function calls is to use an approximation in lieu of function calls. An approximation involves two steps: design of experiments (DOE) and metamodeling. This paper presents a new approach where both DOE and metamodeling are integrated with a MOGA. In particular, the DOE method reduces the number of generations in a MOGA, while the metamodeling reduces the number of function calls in each generation. In the present approach, the DOE locates a subset of design points that is estimated to better sample the design space, while the metamodeling assists in estimating the fitness of design points. Several numerical and engineering examples are used to demonstrate the applicability of this new approach. The results from these examples show that the proposed improved approach requires significantly fewer function calls and obtains similar solutions compared to a conventional MOGA and a recently developed metamodeling-assisted MOGA.     

A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding

This paper proposes a new multi objective genetic algorithm (MOGA) for solving unequal area facility layout problems (UA-FLPs). The genetic algorithm suggested is based upon the slicing structure where the relative locations of the facilities on the floor are represented by a location matrix encoded in two chromosomes. A block layout is constructed by partitioning the floor into a set of rectangular blocks using guillotine cuts satisfying the areas requirements of the departments. The procedure takes into account four objective functions (material handling costs, aspect ratio, closeness and distance requests) by means of a Pareto based evolutionary approach. The main advantage of the proposed formulation, with respect to existing referenced approaches (e.g. bay structure), is that the search space is considerably wide and the practicability of the layout designs is preserved, thus improving the quality of the solutions obtained.     

An On-line Multi-CBR Agent Dispatching Algorithm

Case-based reasoning (CBR) is an effective and fast problem-solving methodology, which solves new problems by remembering and adaptation of past cases. With the increasing requests for useful references for all kinds of problems and from different locations, keeping a single CBR system seems to be outdated and not practical. Multi-CBR agents located in different places are of great support to fast meet these requests. In this paper, the architecture of a multi-CBR agent system is proposed, where the CBR agents locate at different places, and are assumed to have the same ability to deal with new problem independently. When the requests in a request queue from different places are coming one by one, we propose a new policy of dispatching which agent to satisfy the request queue. Throughout the paper, we assume that the system must solve the coming request by considering only past requests. In this context, the performance of traditional greedy algorithms is not satisfactory. We apply a new but simple approach – competitive algorithm for on-line problem (called On-line multi-CBR agent dispatching algorithm) to determine the dispatching policy to keep comparative low cost. The corresponding on-line dispatching algorithm is proposed and the competitive ratio is given. Based on the competitive algorithm, the dispatching of multi-CBR agents is optimized.     

An On-Line Multi-CBR Agent Dispatching Algorithm

Case-based reasoning (CBR) is an effective and fast problem-solving methodology, which solves new problems by remembering and adaptation of past cases. With the increasing requests for useful references for all kinds of problems and from different locations, keeping a single CBR system seems to be outdated and not practical. Multi-CBR agents located in different places are of great support to fast meet these requests. In this paper, the architecture of a multi-CBR agent system is proposed, where each CBR agent locates at different places, and is assumed to have the same ability to deal with new problem independently. When requests in a request queue are coming one by one from different places, we propose a new policy of agent dispatching to satisfy the request queue. Throughout the paper, we assume that the system must solve the coming request by considering only past requests. In this context, the performance of traditional greedy algorithms is not satisfactory. We apply a new but simple approach – competitive algorithm for on-line problem (called ODAL) to determine the dispatching policy to keep comparative low cost. The corresponding on-line dispatching algorithm is proposed and the competitive ratio is given. Based on the competitive algorithm, the dispatching of multi-CBR agents is optimized.     

Entropy-based multi-objective genetic algorithm for design optimization

Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, one that aims at obtaining the Pareto solutions with maximum possible coverage and uniformity along the Pareto frontier. The new method, called an Entropy-based MOGA (or E-MOGA), is based on an application of concepts from the statistical theory of gases to a baseline MOGA. Two demonstration examples, the design of a two-bar truss and a speed reducer, are used to demonstrate the effectiveness of E-MOGA in comparison to the baseline MOGA.     

模拟电路的多目标优化与演化设计

对模拟电路设计中涉及的多个目标进行了定义与量化,并针对这些目标提出一种面向模拟电路演化设计的多目标遗传算法,该方法利用非支配排序和适应值共享策略来提高搜索方向的空间均匀性,引入基于电路构造指令的编码方案来支持电路自动生成和提高电路演化的效率,并且该编码方案也同样适用于数字电路。利用协同演化的适应值评估策略来增强种群的学习能力,提高演化效率。实验结果表明,该方法可以设计出更实用、简单的模拟电路。     

基于多目标遗传算法的RITS物理结构优化设计

提出了RITS物理结构优化设计问题模型和基于多目标优化的解决思路。RITS结构优化设计待优化函数具有高维、多峰、非线性等特点，求解难度人。重点研究了一类基于Pareto机制的具有快速全局收敛能力的多目标遗传算法，并以紧急救援系统为例进行结构优化设计，验证了算法的有效性。