Active-guided evolution strategies for large-scale capacitated vehicle routing problems

We present an adaptation of the active-guided evolution strategies metaheuristic for the capacitated vehicle routing problem. The capacitated vehicle routing problem is a classical problem in operations research in which a set of minimum total cost routes must be determined for a fleet of identical capacitated vehicles in order to service a number of demand or supply points. The applied metaheuristic combines the strengths of the well-known guided local search and evolution strategies metaheuristics into an iterative two-stage procedure. The computational experiments were carried out on a set of 76 benchmark problems. The results demonstrate that the suggested method is highly competitive, providing the best-known solutions to 70 test instances.     

Weighted multirecombination evolution strategies

Weighted recombination is a means for improving the local search performance of evolution strategies. It aims to make effective use of the information available, without significantly increasing computational costs per time step. In this paper, the potential speed-up resulting from using rank-based weighted multirecombination is investigated. Optimal weights are computed for the infinite-dimensional sphere model, and comparisons with the performance of strategies that do not make use of weighted recombination are presented. It is seen that unlike strategies that rely on unweighted recombination and truncation selection, weighted multirecombination evolution strategies are able to improve on the serial efficiency of the (1+1)$(1+1)$-ES on the sphere. The implications of the use of weighted recombination for noisy optimization are studied, and parallels to the use of rescaled mutations are drawn. The significance of the findings is investigated in finite-dimensional search spaces.     

Self-adaptation of evolution strategies under noisy fitness evaluations

This paper investigates the self-adaptation behavior of ()-evolution strategies (ES) on the noisy sphere model. To this end, the stochastic system dynamics is approximated on the level of the mean value dynamics. Being based on this “microscopic” analysis, the steady state behavior of the ES for the scaled noise scenario and the constant noise strength scenario will be theoretically analyzed and compared with real ES runs. An explanation will be given for the random walk like behavior of the mutation strength in the vicinity of the steady state. It will be shown that this is a peculiarity of the -ES and that intermediate recombination strategies do not suffer from such behavior.     

The optimisation of space structures using evolution strategies with functional networks

In this work, the input for large space structures is created using the Formex algebra of the Formian software. The different search and optimisation algorithm known as evolution strategies (ESs) has been applied to find the optimal design of the space trusses considering the areas of the members of the space structures as discrete variables. The objective function is obtained for first few generations by using a structural analysis package such as Feast and for other generations by functional networks (FNs). Initially, to obtain the data for a functional network, a structural package such as Feast is used. The use of a functional network is motivated by time consuming repeated analyses required by evolution strategies during the optimisation process. In addition, a multilevel optimisation approach is implemented by reducing the size of the search space for individual design variables in each successive level of the optimisation process for the first example; for the remaining three examples, a functional network has been combined with evolution strategies to get away with the use of a structural analysis package and a multilevel optimisation technique. The numerical tests presented demonstrate the computational advantage of the proposed approach of ESs combined with functional networks (FNs) which become pronounced for fairly large scale optimisation problems involving about 700 degrees of freedom.     

Human-competitive lens system design with evolution strategies

Lens system design provides ideal problems for evolutionary algorithms: a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic Self-Adaptive evolution strategy (SA-ES) and Covariance Matrix Adaptation evolution strategy (CMA-ES), as well as multiobjective Non-Dominated Sort Genetic Algorithm 2 (NSGA-II) optimization, the human competitiveness of an approach where an evolutionary algorithm is hybridized with a local search algorithm to solve both a classic benchmark problem, and a real-world problem.     

A differential evolution algorithm to solve redundancy allocation problems

To improve system reliability without changing its nature, three methods are proposed. The first method uses more reliable components and the second method provides redundant components within the system. The third method is a combination of these two methods. The redundancy allocation problem (RAP) finds the appropriate mix of components and redundancies within a system to maximize its reliability or minimize its cost due to several constraints, such as cost, weight, and volume. This paper presents a methodology to solve the RAP, which is an NP‐hard problem, modeled with discrete variables. In this paper, we use a metaheuristic to solve the RAP of a series–parallel system with a mix of components. Our metaheuristic offers a practical method with specific solution encoding, and combines a penalty function to solve large instances of the relaxed RAP, where different types of components can be used in parallel. The efficiency of the algorithm was tested through a set of well‐known benchmark problems from the literature. Testing of the algorithm achieved satisfactory results in reasonable computing time.     

Using risk-oriented approaches to solve information security problems

This paper analyzes risk-management methods that are used in practice and described in the technical literature, standards, and guidelines. The analysis is aimed at classifying the methods and identifying their strengths and weaknesses in terms of practical use to solve information security problems. Based on the data obtained, various ways of implementing risk-management methods are proposed and the range of information security problems for each class of the methods is defined.     

An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making

Credit risk analysis has long attracted a great deal of attention from both academic researchers and practitioners. However, because of the recent financial crisis, this field continues to draw ever increasingly attention. A multiple kernels multi-criteria programming approach based on evolution strategy (ES-MK-MCP) is proposed for credit decision making in this study. We introduce a linear combination of kernel functions to enhance the interpretability of credit classification models, and propose an alternative to optimize the parameters based on the evolution strategy. For illustration purpose, two UCI credit card data sets are used to verify the effectiveness and feasibility of the proposed model. As the experimental results reveal, the proposed ES-MK-MCP model is an efficient tool for credit risk analysis, especially for decision makers to identify the most relevant features.     

A simple multimembered evolution strategy to solve constrained optimization problems

This work presents a simple multimembered evolution strategy to solve global nonlinear optimization problems. The approach does not require the use of a penalty function. Instead, it uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population. This technique helps the algorithm to find the global optimum despite reaching reasonably fast the feasible region of the search space. A simple feasibility-based comparison mechanism is used to guide the process toward the feasible region of the search space. Also, the initial stepsize of the evolution strategy is reduced in order to perform a finer search and a combined (discrete/intermediate) panmictic recombination technique improves its exploitation capabilities. The approach was tested with a well-known benchmark. The results obtained are very competitive when comparing the proposed approach against other state-of-the art techniques and its computational cost (measured by the number of fitness function evaluations) is lower than the cost required by the other techniques compared.     

Structural optimization of as-built parts using reverse engineering and evolution strategies

In industry, some parts are prone to failures or their design is simply sub-optimal. In those critical situations, one would like to be able to make changes to the part, making it lighter or improving its mechanical resistance. The problem of as-built parts is that the original computer-aided design (CAD) model is not available or is lost. To optimize them, a reverse engineering process is necessary to capture the shape and topology of the original design. This paper describes how to capture the original design geometry using a semi-automated reverse engineering process based on measurement provided by an optical 3D sensor. Following this reverse engineering process, a Fixed Grid Finite Element method and evolutionary algorithms are used to find the optimum shape that will minimize stress and weight. Several examples of industrial parts are presented. These examples show the advantages and disadvantages of the proposed method in an industrial scenario. Presented at the 7th World Congress on Computational Mechanics, Los Angeles 2006.     

Evaluating differential evolution with penalty function to solve constrained engineering problems

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results with some recent metaheuristics. Results show that the classical DE with a very simple penalty function to handle constraints is still very competitive in the tested problems.     

Using distance transform to solve real-time machine vision inspection problems

This paper describes novel solutions to two challenging real-time inspection tasks in machine vision. The first is fast surface approximation for volume and surface area measurements of irregularly shaped objects; the second is fast intensity gradient correction for surface inspection and evaluation of spherical objects. Both solutions apply a distance transform (DT) based on the distance of each image pixel from the object boundary. We describe both real-time machine vision inspection tasks and discuss their complexity. We show that the new solutions result in significant improvements in both accuracy and efficiency—despite the relative simplicity of the DT approach.     

Quantum learning control using differential evolution with equally-mixed strategies

Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution (DE) to design optimal control for various quantum systems. Various DE methods are introduced and analyzed, and EMSDE featuring in equally mixed strategies is employed for quantum control. Two classes of quantum control problems, including control of four-level open quantum ensembles and quantum superconducting systems, are investigated to demonstrate the performance of EMSDE for learning control of quantum systems. Numerical results verify the effectiveness of the EMSDE method for various quantum systems and show the potential for complex quantum control problems.     

使用不同的博弈树搜索算法解决计算机围棋的吃子问题

使用Alpha Beta搜索和proof number (pn) 搜索解决计算机围棋的吃子问题.对吃子问题形式化并给出了简单有效的评估函数.Alpha Beta搜索使用了包括置换表在内的各种扩展技术.Pn搜索使用了包括df pn在内的4种变体.研究结果显示,对于解决吃子问题pn搜索优于Alpha Beta搜索.并且搜索过程中所产生的数据的一些模式可以帮助在结果未知的情况下对结果进行预测.所设计的算法可以用于解决单独的吃子问题或者计算机围棋比赛中的吃子计算.     

Using desktop computers to solve large-scale dense linear algebra problems

We provide experimental evidence that current desktop computers feature enough computational power to solve large-scale dense linear algebra problems. While the high computational cost of the numerical methods for solving these problems can be tackled by the multiple cores of current processors, we propose to use the disk to store the large data structures associated with these applications. Our results also show that the limited amount of RAM and the comparatively slow disk of the system pose no problem for the solution of very large dense linear systems and linear least-squares problems. Thus, current desktop computers are revealed as an appealing, cost-effective platform for research groups that have to deal with large dense linear algebra problems but have no direct access to large computing facilities.     

A fuzzy-logic approach to industrial control problems

Increasing demands for improved profitability and product quality, together with a growing awareness of the effects of industrial wastage on the environment, is forcing manufacturers to closely examine their process operations. As a consequence there is currently significant research and development activity aimed at improving control system strategies in a variety of industrial sectors. Recent years have witnessed renewed interest in fuzzy logic and rule-based control strategies and, by considering two illustrative industrial case studies, this paper highlights some of the potential advantages. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Managing emergencies and abnormal situations in air traffic control (part II): Teamwork strategies

Team performance has been studied in many safety-critical organizations including aviation, nuclear power plant, offshore oil platforms and health organizations. This study looks into teamwork strategies that air traffic controllers employ to manage emergencies and abnormal situations. Two field studies were carried out in the form of observations of simulator training in emergency and unusual scenarios of novices and experienced controllers. Teamwork strategies covered aspects of team orientation and coordination, information exchange, change management and error handling. Several performance metrics were used to rate the efficiency of teamwork and test the construct validity of a prototype model of teamwork. This is a companion study to an earlier investigation of taskwork strategies in the same field (part I) and contributes to the development of a generic model for Taskwork and Teamwork strategies in Emergencies in Air traffic Management (T²EAM). Suggestions are made on how to use T²EAM to develop training programs, assess team performance and improve mishap investigations.     

Managing emergencies and abnormal situations in air traffic control (part I): Taskwork strategies

A lot of research in Air Traffic Control (ATC) has focused on human errors in decision making whilst little attention has been paid to the cognitive strategies employed by controllers in managing abnormal situations. This study looks into cognitive strategies in taskwork that enable controllers to become resilient decision-makers. Two field studies were carried out where novice and experienced controllers were observed in simulator training in emergency and unusual scenarios. A prototype model of taskwork strategies in air traffic management was developed and its construct validity was tested in the context of the field studies. A companion study (part II), follows that investigates aspects of teamwork in the same field and contributes to the development of a generic model of Taskwork & Teamwork strategies in Emergencies in Air traffic Management (T²EAM). The final section addresses the difficulties experienced by novice controllers and explains taskwork strategies employed by experts to manage uncertainty and balance workload in simulator emergencies.     

微分进化求解无线传感器网络中的覆盖问题

研究使用微分进化算法求解无线传感器网络中的覆盖问题,介绍一种通用的无线传感器网络覆盖问题数据集生成算法,利用该算法生成覆盖问题数据集.提出利用微分进化算法求解覆盖问题的方法,方法的重组操作保证至少一个关键点的传感器分配到不同的子集,提高解的质量.其适应度函数考虑了完全覆盖子集的个数和非完全覆盖子集的覆盖率.与此类经典算法进行对比实验,实验结果表明了该方法的有效性.