Application of genetic algorithms to fault diagnosis in nuclear power plants

A nuclear power plant (NPP) is a complex and highly reliable special system. Without expert knowledge, fault confirmation in the NPP can be prevented by illusive and real-time signals. A new method of fault diagnosis, based on genetic algorithms (GAs) has been developed to resolve this problem. This NPP fault diagnosis method combines GAs and classical probability with an expert knowledge base. By assessing the state of the NPP, the GA colony undergoes a transformation that produces an individual adapted to the NPP's condition. Experiments performed on the 950 MW full size simulator at the Beijing NPP simulation training center show that this method has comparative adaptability to diagnose signals and faults changed with time, imperfect expert knowledge, illusive signals and other phenomena.     

Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm

Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing and open architecture for services. The global grid technologies and the Globus Toolkit in particular, are evolving toward an open grid service architecture (OGSA) with which a grid system provides an extensible infrastructure so that various organizations can offer their own services and integrate their resources. Hence, this paper aims at solving the problem of optimally allocating services on the grid to maximize the grid service reliability. Since no existing study has analyzed the grid service reliability, this paper develops initial modeling and evaluation algorithms to evaluate the grid service reliability. Based on the grid service reliability evaluation, we present an optimization model for the grid service allocation problem and develop a genetic algorithm (GA) to effectively solve it. A numerical example is given to show the modeling procedures and efficiency of the GAs.     

A comprehensive reliability allocation method for design of CNC lathes

In the design and development of computerized numerical control lathes, an effective reliability allocation method is needed to allocate system level reliability requirements into subsystem and component levels. During the allocation process, many factors have to be considered. Some of these factors can be measured quantitatively while others have to be assessed qualitatively. In this paper, we consider seven criteria for conducting reliability allocation. A comprehensive failure rate allocation method is proposed for conducting the task of reliability allocation. Example data from field studies are used to illustrate the proposed method.     

Structured information analysis for human reliability analysis of emergency tasks in nuclear power plants

Being supported by scarce empirical data, most of the performance influencing factors in human reliability analysis (HRA) have to be assessed on the basis of the analyst's knowledge on the human performance in given tasks and their context. Therefore, the outcome of HRA may only be warranted by a proper application of their knowledge based on sufficient information about the tasks and situations. However, most of the HRA methodologies, including the newly developed ones, focus on the provision of cognitive models, error mechanisms, error types and analysis method while leaving the information collection mostly in the hands of the analyst. This paper suggests structured information analysis (SIA), which helps HRA analysts in collecting and structuring such information on tasks and contexts. The SIA consists of three parts: the scenario analysis, the goal-means analysis, and the cognitive function analysis. An expert evaluation showed that this three-part information analysis allowed more expressiveness and hence more confidence on the error prediction than ASEP HRA.     

遗传算法在分数阶Fourier变换域极值优化中的应用

分数阶Fourier变换采用线性调频基,因此,线性调频（Linear Frequency Modulation,LFM）信号在分数阶Fourier域平面能够聚焦,并形成峰值。为了克服传统步进式搜索法在LFM信号峰值搜索中效率低下的缺点,将遗传算法引入到分数阶Fourier变换极值搜索中。仿真结果表明,该方法优于传统的步进式搜索法。     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

一种新颖的多用户OFDM资源分配算法

在多用户正交频分复用(OFDM)系统中设计一种新颖的子载波-功率分配方法来最大化用户数据传输速率.这个方案分成两部分,子载波分配和功率分配.其中,子载波分配方法是在信道容量矩阵中将信道容量最好的子载波首先进行分配,功率分配采用注水方法.研究和模拟结果表明,该算法在只改变各个子载波增益系数的基础上,基本保持了较低的复杂度,并且极大地提高了用户数据传输速率.     

改进遗传单纯形混合算法在光纤对接中的应用

本文首先给出了纤端光场分布的3D模型并针对单模光纤与激光器的对接分析了错位、角度偏差等对接效率影响因素。通过与传统的爬山法进行比较,提出将遗传单纯形混合算法应用到光纤对接中。单纯形法是一种局部搜索方法,这种寻优方法收敛速度快,它与遗传算法组合起来可以改善单独使用遗传算法收敛速度慢的缺陷。同时,针对光纤对接要求初始种群小所造成的个体多样性差的缺点,改进了单纯形的反射、压缩系数,有效地避免了遗传操作的过早收敛,并通过仿真结果证明了混合算法可以在10代之内找到光纤对接的最佳耦合点。     

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.     

A bat algorithm for the redundancy allocation problem

This article uses a recently developed bat algorithm (BA) meta-heuristic optimization method to solve the reliability redundancy allocation problem (RAP). The RAP is a well-known NP-hard problem which has been the subject of much prior work, generally of a restricted form where each component must consist of identical components in parallel to make computations tractable. Meta-heuristic methods overcome this limitation and allow for larger instances to be solved for a more general case where different components can be placed in parallel. The BA has not yet been used in reliability design, as it was a method initially designed for continuous problems. A BA is devised and tested on a well-known suite of problems from the literature. It is shown that the BA is competitive with the best known heuristics for redundancy allocation.     

Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method.     

Fault-tolerant embedded system design and optimization considering reliability estimation uncertainty

In this paper, we model embedded system design and optimization, considering component redundancy and uncertainty in the component reliability estimates. The systems being studied consist of software embedded in associated hardware components. Very often, component reliability values are not known exactly. Therefore, for reliability analysis studies and system optimization, it is meaningful to consider component reliability estimates as random variables with associated estimation uncertainty. In this new research, the system design process is formulated as a multiple-objective optimization problem to maximize an estimate of system reliability, and also, to minimize the variance of the reliability estimate. The two objectives are combined by penalizing the variance for prospective solutions. The two most common fault-tolerant embedded system architectures, N-Version Programming and Recovery Block, are considered as strategies to improve system reliability by providing system redundancy. Four distinct models are presented to demonstrate the proposed optimization techniques with or without redundancy. For many design problems, multiple functionally equivalent software versions have failure correlation even if they have been independently developed. The failure correlation may result from faults in the software specification, faults from a voting algorithm, and/or related faults from any two software versions. Our approach considers this correlation in formulating practical optimization models. Genetic algorithms with a dynamic penalty function are applied in solving this optimization problem, and reasonable and interesting results are obtained and discussed.     

遗传算法在曲柄摇杆机构设计中的应用

本文讨论曲柄摇杆机构优化设计的数学模型及其简化 ,利用遗传算法得到优化结果     

基于自相似流的无线Mesh网络信道分配算法

根据业务流的自相似性提出了一种新颖的信道分配算法。该算法首先根据流的自相似性,估计网络中的节点的业务量。然后,根据业务量多少确定占用信道的优先级。业务量大的节点可以优先占用信道。这样,解决了“热点”信道资源紧张的问题。同时,算法对“非热点”的吞吐量影响很小。理论分析证明,这种算法可以有效提高网络吞吐量。仿真结果表明,运用这种算法,网络的吞吐量比最小干扰固定信道分配算法和最小干扰混合信道分配算法可分别提高70％和40％。     

Application of the fault tree analysis for assessment of power system reliability

A new method for power system reliability analysis using the fault tree analysis approach is developed. The method is based on fault trees generated for each load point of the power system. The fault trees are related to disruption of energy delivery from generators to the specific load points. Quantitative evaluation of the fault trees, which represents a standpoint for assessment of reliability of power delivery, enables identification of the most important elements in the power system. The algorithm of the computer code, which facilitates the application of the method, has been applied to the IEEE test system. The power system reliability was assessed and the main contributors to power system reliability have been identified, both qualitatively and quantitatively.     

Price negotiation for capacity sharing in a two-factory environment using genetic algorithm

Uncertain and lumpy demand forces capacity planners to maximize the profit of individual factory by simultaneously taking advantage of outsourcing to and/or being outsourced from its supply chain and even competitors. This study develops a resource-planning model of a large manufacturer with two profit-centered factories. The proposed model enables a collaborative integration for resource and demand sharing which is highly attractive to the high-tech industries against the challenges of short product life cycle, intensive capital investment and decreasing marginal profit. Each of the individual factories applies an economic resource-planning model and a genetic algorithm to improve its objective while purchasing extra capacity requirement from its peer factory or selling extra capacity of resources to the others through a negotiation algorithm. This study makes a contribution in successfully building a mutual negotiation model for a set of customer tasks to be realized by the negotiating parties, each with private information regarding company objectives, cost and price. Experimental results reveal that near-optimal solutions for both of the isolated (a single factory) and negotiation-based (between two factories) environments are obtained.     

Multiple multi-level redundancy allocation in series systems

This paper deals with a multiple multi-level redundancy allocation problem (MMRAP) in which all available items for redundancy (system, module and component) can be simultaneously chosen. Due to a well-known principle that the redundancy at the component level (component redundancy) is more effective than the redundancy at the system level (system redundancy), the object of redundancy allocation has been limited to the component redundancy. In addition, multi-level redundancy allocation problems (MRAP) have been addressed, considering the redundancy at the subsystem level (modular redundancy). In this paper, the MMRAP is formulated and a GA algorithm is presented to solve the problem. Some numerical examples are illustrated.     

Scheduling trucks in container terminals using a genetic algorithm

Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.     

Optimal reserve management for restructured power generating systems

This paper presents a technique to determine the optimal reserve structure (reserve providers and the corresponding reserve capacity) for a restructured power generating system (GS). The reserve of a GS can be provided by its own generating units and can also be purchased from other GSs through the reserve agreements. The objective of reserve management for a GS is to minimize its total reserve cost while satisfying the reliability requirement. The reserve management is a complex optimization problem, which requires a large amount of calculations. In order to simplify the evaluation, a complex generating system (CGS) consisting of different GSs and the corresponding transmitting network is represented by its multi-state reliability equivalents. The universal generating functions (UGFs) of these equivalents are developed and the special operators for these UGFs are defined to evaluate the reliability of a particular GS, which has reserve agreements with other GSs in the CGS. The genetic algorithm (GA) has been used to solve the optimization problem. An improved power system-IEEE reliability test system is used to illustrate the technique.     

Hybrid genetic algorithm for group technology economic lot scheduling problem

The concept of group technology has been successfully applied to many production systems, including flexible manufacturing systems. In this paper we apply group technology principles to the economic lot scheduling problem, which has been studied for over 40 years. We develop a heuristic algorithm and a hybrid genetic algorithm for the group technology economic lot scheduling problem. Numerical experiments show that the developed algorithms outperform the existing heuristics.