Search

A novel iterative combinatorial nanoscale search based on the application of cross-sectional synchrotron X-ray nanodiffraction and cross-sectional nanoindentation is used to refine the relationship between deposition conditions, microstructure and properties of nanostructured TiAlN thin films. Using three iterative steps, a nanolamellar TiAlN thin film with a maximal hardness of ∼36 GPa is developed., Because of the tremendous variability of crystallite sizes and shapes in nano­materials, it is challenging to assess the corresponding size–property relationships and to identify microstructures with particular physical properties or even optimized functions. This task is especially difficult for nanomaterials formed by self-organization, where the spontaneous evolution of microstructure and properties is coupled. In this work, two compositionally graded TiAlN films were (i) grown using chemical vapour deposition by applying a varying ratio of reacting gases and (ii) subsequently analysed using cross-sectional synchrotron X-ray nanodiffraction, electron microscopy and nanoindentation in order to evaluate the microstructure and hardness depth gradients. The results indicate the formation of self-organized hexagonal–cubic and cubic–cubic nanolamellae with varying compositions and thicknesses in the range of ∼3–15 nm across the film thicknesses, depending on the actual composition of the reactive gas mixtures. On the basis of the occurrence of the nanolamellae and their correlation with the local film hardness, progressively narrower ranges of the composition and hardness were refined in three steps. The third film was produced using an AlCl3/TiCl4 precursor ratio of ∼1.9, resulting in the formation of an optimized lamellar microstructure with ∼1.3 nm thick cubic Ti(Al)N and ∼12 nm thick cubic Al(Ti)N nanolamellae which exhibits a maximal hardness of ∼36 GPa and an indentation modulus of ∼522 GPa. The presented approach of an iterative nanoscale search based on the application of cross-sectional synchrotron X-ray nanodiffraction and cross-sectional nanoindentation allows one to refine the relationship between (i) varying deposition conditions, (ii) gradients of microstructure and (iii) gradients of mechanical properties in nanostructured materials prepared as thin films. This is done in a combinatorial way in order to screen a wide range of deposition conditions, while identifying those that result in the formation of a particular microstructure with optimized functional attributes.

Motivation: The Backrub is a small but kinematically efficient side-chain-coupled local backbone motion frequently observed in atomic-resolution crystal structures of proteins. A backrub shifts the Cα–Cβ orientation of a given side-chain by rigid-body dipeptide rotation plus smaller individual rotations of the two peptides, with virtually no change in the rest of the protein. Backrubs can therefore provide a biophysically realistic model of local backbone flexibility for structure-based protein design. Previously, however, backrub motions were applied via manual interactive model-building, so their incorporation into a protein design algorithm (a simultaneous search over mutation and backbone/side-chain conformation space) was infeasible. Results: We present a combinatorial search algorithm for protein design that incorporates an automated procedure for local backbone flexibility via backrub motions. We further derive a dead-end elimination (DEE)-based criterion for pruning candidate rotamers that, in contrast to previous DEE algorithms, is provably accurate with backrub motions. Our backrub-based algorithm successfully predicts alternate side-chain conformations from ≤0.9 Å resolution structures, confirming the suitability of the automated backrub procedure. Finally, the application of our algorithm to redesign two different proteins is shown to identify a large number of lower-energy conformations and mutation sequences that would have been ignored by a rigid-backbone model. Availability: Contact authors for source code. Contact: brd+ismb08@cs.duke.edu

BackgroundHealth promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. ObjectiveThis study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. MethodsWe developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher’s tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). ResultsAn illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. ConclusionsResults suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.

Planning intensity modulated radiation therapy (IMRT) treatment involves selection of several angle parameters as well as specification of structures and constraints employed in the optimization process. Including these parameters in the combinatorial search space vastly increases the computational burden, and therefore the parameter selection is normally performed manually by a clinician, based on clinical experience. We have investigated the use of a genetic algorithm (GA) and distributed-computing platform to optimize the gantry angle parameters and provide insight into additional structures, which may be necessary, in the dose optimization process to produce optimal IMRT treatment plans. For an IMRT prostate patient, we produced the first generation of 40 samples, each of five gantry angles, by selecting from a uniform random distribution, subject to certain adjacency and opposition constraints. Dose optimization was performed by distributing the 40-plan workload over several machines running a commercial treatment planning system. A score was assigned to each resulting plan, based on how well it satisfied clinically-relevant constraints. The second generation of 40 samples was produced by combining the highest-scoring samples using techniques of crossover and mutation. The process was repeated until the sixth generation, and the results compared with a clinical (equally-spaced) gantry angle configuration. In the sixth generation, 34 of the 40 GA samples achieved better scores than the clinical plan, with the best plan showing an improvement of 84%. Moreover, the resulting configuration of beam angles tended to cluster toward the patient's sides, indicating where the inclusion of additional structures in the dose optimization process may avoid dose hot spots. Additional parameter selection in IMRT leads to a large-scale computational problem. We have demonstrated that the GA combined with a distributed-computing platform can be applied to optimize gantry angle selection within a reasonable amount of time.

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, community detection in bipartite networks has not been extensively researched. In this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect the communities in two-mode graphs (i.e. bipartite graphs) with two kinds of vertices in the cluster (i.e. community). Firstly, this paper proposed a novel population initialization process of artificial bee colony (ABC) method for two-mode graph cluster identification. This initialization method can improve the diversity of initial population of ABC and speed up its convergence rate. Secondly, in the employed bee search step of the algorithm, a new combinatorial search equation is proposed. This equation is guided by the global optimal solution and the better neighbour solution of the current solution. By using this combination equation and the increased parameter perturbation frequency, the exploitation ability of the algorithm is further enhanced. Thirdly, in the onlooker bees step, another new combination search equation is also proposed. This equation improves the exploitation level of the algorithm, and an opposition based studying method is employed to promote the exploitation ability of the algorithm. Lastly, in scout bee stage, a probability threshold $\beta $ is introduced to enhance the exploration ability of the algorithm and improve the population diversity of the algorithm. To our knowledge, the IABC-BN method presented in this paper is the first ABC method used to cluster identification in two-mode graphs with two kinds of vertices in the cluster. For verifying the accuracy of the results of the proposed method, a large number of experiments are carried out making use of synthetic bipartite graphs and real bipartite graphs. The test outcomes show that this algorithm is an excellent algorithm for cluster discovery in two-mode graph.

This paper addresses the distributed limited-buffer flowshop scheduling problem with makespan minimization criterion for the first time. The manufacturing layout consists of multiple homogeneous factories and each one is set as a permutation flowshop with limited buffers between any two adjacent machines. Compared with traditional single shop scheduling, this problem is more realistic because of the distribution and limited buffer constraints. Given that the NP-hardness, this paper focuses on the approximate approaches, and successively proposes two constructive heuristics for generating fast schedule and providing good initialization of meta-heuristics, a differential evolution algorithm run in continuous search space to further better the so-obtained schedules, and a discrete differential evolution algorithm implemented directly in combinatorial search space. Extensive experimental results validate that the performance of discrete differential evolution perform the best and other algorithms proposed in this paper are all with satisfactory evaluations.

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming. This paper is under consideration for acceptance in TPLP., Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 16 pages, 6 figures

Aim. This paper is the continuation of [1] that proposes using the R programming language for fault tree analysis (FTA). In [1], three examples are examined: fault tree (FT) calculation per known probabilities, dynamic FT calculation per known distributions of times to failure for a system’selements. In the latter example, FTA is performed for systems with elements that are described by different functional and service models. Fault tree analysis (FTA) is one of the primary methods of dependability analysis of complex technical systems. This process often utilizes commercial software tools like Saphire, Risk Spectrum, PTC Windchill Quality, Arbitr, etc. Practically each software tool allows calculating the dependability of complex systems subject to possible common cause failures (CCF). CCF are the associated failures of a group of several elements that occur simultaneously or within a short time interval (i.e. almost simultaneously) due to one common cause (e.g. a sudden change in the climatic service conditions, flooding of the premises, etc.). An associated failure is a multiple failure of several system elements, of which the probability cannot be expressed simply as the product of the probabilities of unconditional failures of individual elements. There are several generally accepted models used in CCF probability calculation: the Greek letters model, the alpha, beta factor models, as well as their variations. The beta factor model is the most simple in terms of associated failures simulation and further dependability calculation. The other models involve combinatorial search associated events in a group of n events, that becomes labor-consuming if the number n is large. Therefore, in the above software tools there are some restrictions on the n, beyond which the probability of CCF is calculated approximately. In the current R FaultTree package version there are no above CCF models, therefore all associated failures have to be simulated manually, which is not complicated if the number of associated events is small, as well as useful in terms of understanding the various CCF models. In this paper, for the selected diagram a detailed analysis of the procedure of associated failures simulation is performed for alpha and beta factor models. The Purposeof this paper consists in the detailed analysis of the alpha and beta factor methods for a certain diagram, in the demonstration of fault tree creation procedure taking account of ССF using R’s FaultTree package. Methods. R’s FaultTree scripts were used for the calculations and FTA capabilities demonstration.Conclusions. Two examples are examined in detail. In the first example, for the selected block diagram that contains two groups of elements subject to associated failures, the alpha factor model is applied. In the second example, the beta factor model is applied. The deficiencies of the current version of FaultTree package are identified. Among the main drawbacks we should indicate the absence of some basic logical gates.

This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.

International audience; Most studies related to parallel and portfolio search for solving combinatorial problems, such as those found in Boolean satisfiability or constraint programming, evaluate search cost in terms of runtime. However, given the complex computing architectures available today and the focus on the environmental impact of computing, there is growing interest in also considering the energy cost associated with solving these problems. In the context of combinatorial problem-solving, a simple approximation of energy cost is the product of the number of machines multiplied by the runtime spent to solve a problem instance. However, the picture is much more complex due to the impact that the distribution of runtimes, even for solving a single specific instance, can have on search cost. In this paper we present an initial, but comprehensive, study on the impact of runtime distribution on the amount of energy required for combinatorial problem solving characterized by two common continuous runtime distributions, namely the Weibull and Pareto distributions. The primary contribution of this paper is to demonstrate that there is an interesting and non-trivial relationship between runtime, parallelisation, and energy cost in combinatorial solving that is worthy of further study.

A new approach is used to generate combinatorial test suites.Cuckoo Search application is investigated for a new type of application and case study.The strategy opens a new approach in Search Based Software Testing (SBST).The strategy is evaluated through different benchmarks and it is able to get comparative results.Application of combinatorial optimization is also investigated in the current paper. ContextSoftware has become an innovative solution nowadays for many applications and methods in science and engineering. Ensuring the quality and correctness of software is challenging because each program has different configurations and input domains. To ensure the quality of software, all possible configurations and input combinations need to be evaluated against their expected outputs. However, this exhaustive test is impractical because of time and resource constraints due to the large domain of input and configurations. Thus, different sampling techniques have been used to sample these input domains and configurations. ObjectiveCombinatorial testing can be used to effectively detect faults in software-under-test. This technique uses combinatorial optimization concepts to systematically minimize the number of test cases by considering the combinations of inputs. This paper proposes a new strategy to generate combinatorial test suite by using Cuckoo Search concepts. MethodCuckoo Search is used in the design and implementation of a strategy to construct optimized combinatorial sets. The strategy consists of different algorithms for construction. These algorithms are combined to serve the Cuckoo Search. ResultsThe efficiency and performance of the new technique were proven through different experiment sets. The effectiveness of the strategy is assessed by applying the generated test suites on a real-world case study for the purpose of functional testing. ConclusionResults show that the generated test suites can detect faults effectively. In addition, the strategy also opens a new direction for the application of Cuckoo Search in the context of software engineering.

retrieval is the most fundamental requirement for any kind of computing application and which requires search operation to be performed from massive databases implemented by various data structures. Searching an element from the list is the fundamental aspects in computing world. Numbers of algorithms are developed for searching an element among which linear search and binary search are the most popular algorithms. In this paper researcher has made efforts to compare these both algorithms to implement on various data structures and to find out the solution to implement binary search on linked linear list. This paper also analyzes both the algorithms at some extent for the applicability and execution efficiency. This paper also analyzes the few data structures to implement these algorithms. At last based on the linear search and binary search algorithms, one algorithm is designed to function on linked linear list.

In this paper we consider the problem of selecting a carrier of the plurality of outdoor advertising offers available in the formulation of the knapsack problem, namely, when restricted to the amount of funding. The paper proposes a variant of the heuristic based on the modeling method of artificial immune system of the human body. The basis algorithm immune operators comprise cloning, mutation, selective compression of the memory and repeating the natural processes in lymphocytes. The proposed simulation method of artificial immune systems can be used to solve a wide range of combinatorial search and constrained optimization problems.

This paper describes the design of a system to monitor floor vibrations in an office building and an analysis of several months worth of collected data. Floors of modern office buildings are prone to occupant-induced vibrations. The contributing factors include long spans, slender and flexible designs, use of lightweight materials and low damping. As a result, resonant frequencies often fall in the range easily excited by normal footfall loading, creating potential serviceability problems due to undesirable levels of vibrations. This study investigates in-situ performance of a non-composite timber-concrete floor located in a recently constructed innovative multi-storey office building. The floor monitoring system consists of several displacement transducers to measure long-term deformations due to timber and concrete creep and three accelerometers to measure responses to walking forces, the latter being the focus of this paper. Floor response is typically complex and multimodal and the optimal accelerometer locations were decided with the help of the effective independence-driving point residue (EfI-DPR) technique. A novel approach to the EfI-DPR method proposed here uses a combinatorial search algorithm that increases the chances of obtaining the globally optimal solution. Several months worth of data collected by the monitoring system were analyzed using available industry guidelines, including ISO2631-1:1997(E), ISO10137:2007(E) and SCI Publication P354. This enabled the evaluation of the floor performance under real operating conditions.

This is the first of two papers presenting and evaluating the power of a new framework for combinatorial optimization in graphical models, based on AND/OR search spaces. We introduce a new generation of depth-first Branch-and-Bound algorithms that explore the AND/OR search tree using static and dynamic variable orderings. The virtue of the AND/OR representation of the search space is that its size may be far smaller than that of a traditional OR representation, which can translate into significant time savings for search algorithms. The focus of this paper is on linear space search which explores the AND/OR search tree. In the second paper we explore memory intensive AND/OR search algorithms. In conjunction with the AND/OR search space we investigate the power of the mini-bucket heuristics in both static and dynamic setups. We focus on two most common optimization problems in graphical models: finding the Most Probable Explanation in Bayesian networks and solving Weighted CSPs. In extensive empirical evaluations we demonstrate that the new AND/OR Branch-and-Bound approach improves considerably over the traditional OR search strategy and show how various variable ordering schemes impact the performance of the AND/OR search scheme.

This paper investigates cooperative search strategies for agents engaged in costly search in a complex environment. Searching cooperatively, several search goals can be satisfied within a single search effort. Given the searchers' preferences, the goal is to conduct a search in a way that the expected overall utility out of the set of opportunities found (e.g., products when operating in a market) minus the costs associated with finding that set is maximized. This search scheme, given in the context of a group search, applies also to scenarios where a single agent has to search for a set of items for satisfying several different goals. The uniqueness of the proposed mechanism is in the ability to partition the group of agents/goals into sub-groups where the search continues for each group autonomously. As we show throughout the paper, this strategy is favorable as it weakly dominates (i.e., can improve but never worsen) cooperative and autonomous search techniques. The paper presents a comprehensive analysis of the new search method and highlights the specific characteristics of the optimal search strategy. Furthermore, we introduce innovative algorithms for extracting the optimal search strategy in a range of common environments, that eliminates the computational overhead associated with the use of the partitioning technique.

In recent years, much attention has been devoted to the development of metaheuristics and local search algorithms for tackling stochastic combinatorial optimization problems. This paper focuses on local search algorithms; their effectiveness is greatly determined by the evaluation procedure that is used to select the best of several solutions in the presence of uncertainty. In this paper, we propose an effective evaluation procedure that makes use of empirical estimation techniques. We illustrate this approach and we assess its performance on the probabilistic traveling salesman problem. Experimental results on a large set of instances show that the proposed approach can lead to a very fast and highly effective local search algorithm.

Combinatorial testing is an important testing method. It requires the test cases to cover various combinations of parameters of the system under test. The test generation problem for combinatorial testing can be modeled as constructing a matrix which has certain properties. This paper first discusses two combinatorial testing criteria: covering array and orthogonal array, and then proposes a backtracking search algorithm to construct matrices satisfying them. Several search heuristics and symmetry breaking techniques are used to reduce the search time. This paper also introduces some techniques to generate large covering array instances from smaller ones. All the techniques have been implemented in a tool called EXACT (EXhaustive seArch of Combinatorial Test suites). A new optimal covering array is found by this tool.

Determining the most appropriate search method or artificial intelligence technique to solve a problem is not always evident and usually requires implementation of the different approaches to ascertain this. In some instances a single approach may not be sufficient and hybridization of methods may be needed to find a solution. This process can be time consuming. The paper proposes the use of hyper-heuristics as a means of identifying which method or combination of approaches is needed to solve a problem. The research presented forms part of a larger initiative aimed at using hyper-heuristics to develop intelligent hybrid systems. As an initial step in this direction, this paper investigates this for classical artificial intelligence uninformed and informed search methods, namely depth first search, breadth first search, best first search, hill-climbing and the A* algorithm. The hyper-heuristic determines the search or combination of searches to use to solve the problem. An evolutionary algorithm hyper-heuristic is implemented for this purpose and its performance is evaluated in solving the 8-Puzzle, Towers of Hanoi and Blocks World problems. The hyper-heuristic employs a generational evolutionary algorithm which iteratively refines an initial population using tournament selection to select parents, which the mutation and crossover operators are applied to for regeneration. The hyper-heuristic was able to identify a search or combination of searches to produce solutions for the twenty 8-Puzzle, five Towers of Hanoi and five Blocks World problems. Furthermore, admissible solutions were produced for all problem instances.

The particle swarm optimization (PSO) approach has been successfully applied in continuous problems in practice. However, its application on the combinatorial search space is relatively new. The component assignment/sequencing problem in printed circuit board (PCB) has been verified as NP-hard (non-deterministic polynomial time). This paper presents an adaptive particle swarm optimization (APSO) approach to optimize the sequence of component placements on a PCB and the assignment of component types to feeders simultaneously for a pick-and-place machine with multiple heads. The objective of the problem is to minimize the total traveling distance (the traveling time) and the total change time of head nozzle. The APSO proposed in the paper incorporates three heuristics, namely, head assignment algorithm, reel grouping optimization and adaptive particle swarm optimization. Compared with the results obtained by other research, the performance of APSO is not worse than the performance of genetic algorithms (GA) in terms of the distance traveled by the placement head.

PurposeThe purpose of this paper is to: develop an effective cellular manufacturing system (CMS) design methodology by simultaneously considering system costs and individual machine reliabilities; and propose a combinatorial search‐based solution procedure to solve large‐sized problems.Design/methodology/approachThis paper presents a multi‐objective mixed integer‐programming model for the design of CMS with the objective of minimizing costs and maximizing system reliability. The approach optimizes inter‐cell material handling costs, the variable cost of machining operations, and the machine under‐utilization costs. It also maximizes the system reliability by selecting process routes for the part types with the highest system reliability for the machines along the routes. To solve the multi‐objective, multiple process plan model, a simulated annealing (SA)‐based algorithm is developed. The algorithm follows the basic steps of SA, but also incorporates the genetic algorithm (GA) operations of crossover and mutations to generate better neighboring solutions from the current good solutions.FindingsThe algorithm in the paper solves the multi‐objective CMS design model and generates near optimal solutions for medium to large‐sized problems within reasonable limits of CPU time.Practical implicationsIn the paper the CMS design approach can be implemented to improve reliability performance of the CMS.Originality/valueA new CMS design methodology in this paper, which minimizes system costs and maximizes machine‐related system reliability, is developed. The proposed algorithm, which combines the basic steps of SA and crossover and mutation operations of GA, will enable CMS designers and users to obtain near optimal solutions for practical‐sized problems within reasonable time limits.

The 18th EURO Summer/Winter Institute (ESWI XVIII) took place during the spring 2000 in Switzerland. The topic of ESWI XVIII, “Meta-heuristics in Combinatorial Optimization”, was selected due to its great current scientific interest: indeed, in recent years, several meta-heuristics have proved to be highly efficient for the solution of difficult combinatorial optimization problems. The Institute was focused more particularly on the development and the use of local search and population search algorithms. Applications of these meta-heuristics on academic or real life problems were also discussed. This special issue of EJOR contains papers written by the participants to ESWI XVIII. These papers have benefited from fruitful discussions among the participants, the organizers and the invited speakers. We have tried to summarize here below some guidelines that should help in the design of successful adaptations of meta-heuristics to difficult combinatorial optimization problems.

In this paper, the timetabling problem for a typical high school environment was modeled and solved using a constraint programming (CP) approach. In addition, operations research (OR) models and local search techniques were also used in order to assist the CP search process by effectively reducing the solution search space. Relaxed models that can be solved using minimum cost matching algorithms were used in order to calculate problem lower bounds at various instances of the solution process. These bounds were in turn used to prioritize the search options of the CP process. The use of minimum cost matching model in the search process is an economical and efficient mechanism for the creation of effective search strategies and it is a competitive manner of introducing problem domain information in the CP environment. By including in the solution process a sequence of local search steps, the solution quality was further improved. Several large problems were solved and actual computational results for specific problem instances are presented. Scope and purpose There exist various school timetabling problems depending on the environment and the characteristics of the particular school level [1] . In this paper the high school situation in which the teachers teach in several different class sections during the day and the students remain in their classrooms is modeled and solved. The objective function attempts to minimize the idle hours between the daily teaching responsibilities of all the teachers while also attempting to satisfy their requests for early or late shift assignments. The school timetabling problem is combinatorial and there are several strict organizational and sequence-related rules that must be respected. The problem specifications used in this paper, although they closely describe the situation of a typical Greek high school, are quite general and abstract, which makes the findings of this paper applicable to wider set of school timetabling problems. The specifications mainly focus on the fact that each teacher is scheduled to lecture for a given number of hours at a fixed subset of class sections and the requirement that all the class sections must be always in session without any empty periods in their daily schedules. The integration of constraint programming and operations research techniques for the solution of this problem is one of the main contributions of this paper. The solutions obtained fully utilized the data management and organizational capabilities of the constraint programming approach while being assisted in the search path selection process by techniques and algorithms form the operations research pool of knowledge. The additional information provided by the calculation of efficient lower bounds and the subproblem domain definition and solution strategy presented in this paper, further assists the CP process in selecting promising search paths.

The paper addresses a class of continuous-time, optimal control problems whose solutions are typically characterized by both bang-bang and "singular" control regimes. Analytical study and numerical computation of such solutions are very difficult and far from complete when only techniques from control theory are used. This paper solves optimal control problems by reducing them to the combinatorial search for the shortest path in a specially constructed graph. Since the nodes of the graph are weighted in a sequence-dependent manner, we extend the classical, shortest-path algorithm to our case. The proposed solution method is currently limited to single-state problems with multiple control functions. A production planning problem and a train operation problem are optimally solved to illustrate the method.

Local search is a traditional technique to solve combinatorial search problems and has raised much interest in recent years. The design and implementation of local search algorithms is not an easy task in general and may require considerable experimentation and programming effort. However, contrary to global search, little support is available to assist the design and implementation of local search algorithms. This paper is an attempt to support the implementation of local search. It presents the preliminary design of LOCALIZER, a modeling language which makes it possible to express local search algorithms in a notation close to their informal descriptions in scientific papers. Experimental results on our first implementation show the feasibility of the approach.

In this paper we consider high-dimensional multiclass classification by sparse multinomial logistic regression. We propose first a feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the nonasymptotic bounds for misclassification excess risk of the resulting classifier. We establish also their tightness by deriving the corresponding minimax lower bounds. In particular, we show that there exist two regimes corresponding to small and large number of classes. The bounds can be reduced under the additional low noise condition. To find a penalized maximum likelihood solution with a complexity penalty requires, however, a combinatorial search over all possible models. To design a feature selection procedure computationally feasible for high-dimensional data, we propose multinomial logistic group Lasso and Slope classifiers and show that they also achieve the minimax order.

Although there is a proliferation of techniques for resource-allocation problems in construction, ranging from mathematical programming to knowledge-based expert systems, there still exists a need for the use of more efficient approaches in practice. This paper presents a research study that investigates the application of genetic algorithms (GAs) to the multidimensional problem. The objective is to investigate the use of GAs for both a numerical function optimisation and a combinatorial search problem within the framework of a decision-support system (DSS). A hybrid GA system was designed for construction-resource selection, and a genetic model that represents the problem and solution space was built into the system. A genetic state-space search (GSSS) technique for multimodal functions was used to evaluate the cost profiles that resulted from different combinations of tasks and resources. The study indicates that GA systems have huge potential applications as DSS component(s) in construction-resource assignment. The results also highlighted that GAs exhibit the chaotic characteristics that are often observed in other complex non-linear dynamic systems. The paper discusses the genetic model for the problem, and the empirical results obtained. Recommendations are given on how to achieve improved results in adapting GAs for construction-resource optimisation problems.

Combinatorial optimization problems are those problems that have a finite set of possible solutions. The best way to solve a combinatorial optimization problem is to check all the feasible solutions in the search space. However, checking all the feasible solutions is not always possible, especially when the search space is large. Thus, many meta-heuristic algorithms have been devised and modified to solve these problems. The meta-heuristic approaches are not guaranteed to find the optimal solution since they evaluate only a subset of the feasible solutions, but they try to explore different areas in the search space in a smart way to get a near-optimal solution in less cost and time. In this paper, we propose a new meta-heuristic algorithm that can be used for solving combinatorial optimization problems. The method introduced in this paper is named the Global Neighborhood Algorithm (GNA). The algorithm is principally based on a balance between both the global and local search. A set of random solutions are first generated from the global search space, and then the best solution will give the optimal value. After that, the algorithm will iterate, and in each iteration there will be two sets of generated solutions; one from the global search space and the other set of solutions will be generated from the neighborhood of the best solution. Throughout the paper, the algorithm will be delineated with examples. In the final phase of the research, the results of GNA will be discussed and compared with the results of Genetic Algorithm (GA) as an example of another optimization method.

The following model is considered. There is exactly one unknown element in the n-element set. A question is a partition of S into three classes: (A,L,B). If x∈A then the answer is "yes" (or 1), if x∈B then the answer is "no" (or 0), finally if x∈L then the answer can be either "yes" or "no". In other words, if the answer "yes" is obtained then we know that x∈A∪L while in the case of "no" answer the conclusion is x∈B∪L. The mathematical problem is to minimize the minimum number of questions under certain assumptions on the sizes of A,B and L. This problem has been solved under the condition |L|≥k by the author and Krisztian Tichler in previous papers for both the adaptive and non-adaptive cases. In this paper we suggest to solve the problem under the conditions |A|≤a, |B|≤b. We exhibit some partial results for both the adaptive and non-adaptive cases. We also show that the problem is closely related to some known combinatorial problems. Let us mention that the case b=n−a has been more or less solved in earlier papers.

The fundamental inverse problem in distance geometry is the one of finding positions from inter-point distances. The Discretizable Molecular Distance Geometry Problem (DMDGP) is a subclass of the Distance Geometry Problem (DGP) whose search space can be discretized and represented by a binary tree, which can be explored by a Branch-and-Prune (BP) algorithm. It turns out that this combinatorial search space possesses many interesting symmetry properties that were studied in the last decade. In this paper, we present a new algorithm for this subclass of the DGP, which exploits DMDGP symmetries more effectively than its predecessors. Computational results show that the speedup, with respect to the classic BP algorithm, is considerable for sparse DMDGP instances related to protein conformation.

The work presented in this paper has addressed the issue of resource sharing in dynamic heterogeneous Multi Agent Systems as a search problem. When performing a random search, this might lead to traverse the whole network and increase the failure ratio. This paper has introduced heuristic directed search based on the usage of an approximate matching mechanism to overcome this problem. Our implementation of search algorithms differs from traditional algorithms by using semantically guided technique for resource search as well as a dynamically re-organisable network of agents. The experimental results have shown that using directed search techniques is better than random search in terms of number of hops to find the match. Furthermore, network re-organisation has improved the system performance by directing the search based on resources information, especially when high accuracy is required.

International audience; This paper presents a new numerical algorithm for the integration of the constitutive equations of a single crystal for finite rate-independent elastoplastic strains. The algorithm addressed in this paper is dedicated to face-centered-cubic (FCC) crystal structures. Its first feature is a much more efficient and more accurate integration scheme of the constitutive equations compared to previous attempts. This scheme is based on a fully implicit integration procedure, yet it may be transformed easily into an explicit scheme. Determining the set of active slip systems is performed by the use of a combinatorial search procedure, and the determination of the slip rates of the different active slip systems is based on the fixed point method. The second feature of this algorithm stems from the original method used to solve the ambiguity of the possible non-uniqueness of the set of active slip systems. A robust method, based on a small positive perturbation of the critical shear stresses, is proposed to overcome this difficulty. It is worth mentioning that the algorithm developed in this paper is not limited to one particular hardening law or to FCC crystal structures. Rather, it can be used and extended to various hardening laws and crystal structures (e.g. BCC or HCP...) in a straightforward manner. The authors demonstrate the performance of the proposed algorithm and illustrate its accuracy and efficiency through various numerical simulations at the single crystal and polycrystal scales. The predicted results obtained from those simulations were compared with those obtained using other numerical techniques and algorithms (i.e., a pseudo-inversion technique and an explicit algorithm). Our numerical predictions are also compared with some numerical and experimental results from other papers. The response of the polycrystal was computed by using the proposed algorithm combined with Taylor's homogenization scheme, which is used to compute the overall polycrystalline behavior. The paper ends with a statistical study of the influence of the perturbation technique on the response prediction for a single crystal and a polycrystal.

In this paper, we study the performance of various IDA*-style searches and investigate methods to improve their performance by predicting in each stage the threshold to be used for pruning. Without loss of generality, we consider minimization problems in this paper. We first present three models to approximate the distribution of the number of search nodes by lower bounds: exponential, geometric, and linear, and illustrate these distributions based on some well-known combinatorial search problems. Based on these distributions, we show the performance of an ideal IDA* algorithm and identify reasons why existing IDA*-style algorithms perform well. In practice, we will be able to know from experience the type of distribution for a given problem instance, but will not be able to know the parameters of this distribution until the instance is solved. Hence, we develop RIDA*, a method that estimates dynamically the parameters of the distribution, and predicts the best threshold to be used in each stage. Finally, we compare the performance of several IDA*-style algorithms—Korf’s IDA* and RBFS, RIDA*, IDA*_CR and DFS*—on several application problems, and identify conditions under which each of these algorithms will perform well.