Efficient Search Method Research Articles

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem, which is a generalization of classical permutation flow shop scheduling problem. The DPFSP is NP-hard in general. It is in the early stages of studies on algorithms for solving this problem. In this paper, we propose a GA-based algorithm, denoted by GA_LS, for solving this problem with objective to minimize the maximum completion time. In the proposed GA_LS, crossover and mutation operators are designed to make it suitable for the representation of DPFSP solutions, where the set of partial job sequences is employed. Furthermore, GA_LS utilizes an efficient local search method to explore neighboring solutions. The local search method uses three proposed rules that move jobs within a factory or between two factories. Intensive experiments on the benchmark instances, extended from Taillard instances, are carried out. The results indicate that the proposed hybrid genetic algorithm can obtain better solutions th...

Sifting a Massive Virtual Library of Peptide Ligands for an Optimal Binder to a Given Receptor

The identification of an optimal protein ligand that binds to a target is a difficult problem because the library contains more than a mole of ligands if only 18 residue sites are allowed to vary across all the naturally occurring amino acids. Such a library is far too large to specify explicitly. Instead, the so-called massive virtual library (MVL) is specified indirectly through a set of class rules. An efficient search method through the MVL, which incorporates the principles of sequence design, protein docking and statistical mechanics, has been recently introduced:∗ (i) a random sub-library is created according to the user defined pruning criteria, (ii) each member in the sub-library is docked to the target using AutoDock and ranked according to the binding free energies that are calculated with both AutoDock's scoring function and CHARMM non-bonded interaction energies, (iii) using the calculated free energies, Boltzmann weighted probabilities are assigned to each sequence, (iv) the weights are then used to select the next-generation MVL, and (v) iterate back to step (i) using the current MVL. The algorithm concludes when convergence occurs between the results from two subsequent rounds. For an 8 residue peptide design that binds to Deoxyribonuclease I, the convergence is achieved in as few as 16 iterations. The generated sequence was found experimentally to have high binding affinity selectively towards the desired target.∗S. Quirk, S. Zhong, and R. Hernandez, Proteins: Struct. Func. Bioinfo. 76, 693-705 (2009).

Efficient Search of Anisimkin's (Quasilongitudinal) Modes in Rotated Y-cut of Various Piezoelectric Material

Quasilongitudinal modes in quartz ST cut discovered by Anisimkin gave a new possibility of selecting material for sensing devices. An efficient search method is presented based on analyses previously reported.

Efficient Search Methods for Statistical Dependency Rules

Dependency analysis is one of the central problems in bioinformatics and all empirical science. In genetics, for example, an important problem is to find which gene alleles are mutually dependent or which alleles and diseases are dependent. In ecology, a similar problem is to find dependencies between different species or groups of species. In both cases a classical solution is to consider all pairwise dependencies between single attributes and evaluate the relationships with some statistical measure like the χ 2-measure. It is known that the actual dependency structures can involve more attributes, but the existing computational methods are too inefficient for such an exhaustive search. In this paper, we introduce efficient search methods for positive dependencies of the form X → A with typical statistical measures. The efficiency is achieved by a special kind of a branch-and-bound search which also prunes out redundant rules. Redundant attributes are especially harmful in dependency analysis, because they can blur the actual dependencies and even lead to erroneous conclusions. We consider two alternative definitions of redundancy: the classical one and a stricter one. We improve our previous algorithm for searching for the best strictly non-redundant dependency rules and introduce a totally new algorithm for searching for the best classically non-redundant rules. According to our experiments, both algorithms can prune the search space very efficiently, and in practice no minimum frequency thresholds are needed. This is an important benefit, because biological data sets are typically dense, and the alternative search methods would require too large minimum frequency thresholds for any practical purpose.

Self-Sensing Composites and Optimization of Composite Structures in Japan

I review research on self-sensing and structural optimizations of laminated carbon/epoxy composites in Japan. Self-sensing is one of the multiple functions of composites; i.e., carbon fiber is used as a sensor as well as reinforcement. I present a controversial issue in self-sensing and detail research results. Structural optimization of laminated CFRP composites is indispensable in reducing the weights of modern aerospace structural components. I present a modified efficient global search method using the multi-objective genetic algorithm and fractal branch and bound method. My group has focused its research on these subjects and our research results are presented here.

A distribution network expansion planning model considering distributed generation options and techo-economical issues

This paper presents a dynamic multi-objective model for distribution network expansion, considering the distributed generations as non-wire solutions. The proposed model simultaneously optimizes two objectives namely, total costs and technical constraint satisfaction by finding the optimal schemes of sizing, placement and specially the dynamics (i.e., timing) of investments on DG units and/or network reinforcements over the planning period. An efficient heuristic search method is proposed to find non-dominated solutions of the formulated problem and a fuzzy satisfying method is used to choose the final solution. The effectiveness of the proposed model and search method are assessed and demonstrated by various studies on an actual distribution network.

ORBIT DETERMINATION OF DOUBLE-LINED SPECTROSCOPIC BINARIES BY FITTING THE REVISEDHIPPARCOSINTERMEDIATE ASTROMETRIC DATA

Untill now, the Hipparcos intermediate astrometric data (HIAD) have contributed little to the full orbit determination of double-lined spectroscopic binaries (SB2s). This is because the photocenter of such a binary system is usually not far from the system mass center, and its orbital wobble is generally weak with respect to the accuracy of the HIAD. However, the HIAD have been recently revised and the accuracy is increased by a factor of 2.2 in the total weight. Therefore, it is interesting to see if the revised HIAD can be used in the orbit determination at least for some SB2s. In this paper, we first search the 9th Catalogue of Orbits of Spectroscopic Binaries (SB9) for SB2s with reliable spectroscopic orbital solutions and with periods between 50 days and 3.2 years. This leaves us with 56 systems. The full orbital solutions of these systems are then determined from the HIAD by a highly efficient grid search method developed in this paper. The high efficiency is achieved by reducing the number of nonlinear model parameters to one, and by allowing all parameters to be adjustable within a region centered at each grid point. After a variety of tests, we finally accept orbital solutions of 13 systems. Among these systems, six (HIP 677, 20894, 87895, 95995, 101382, and 111170) are well resolved with reliable interferometric data. Orbital solutions from these data are consistent with our results. The full orbital solutions of the other seven systems (HIP 9121, 17732, 32040, 57029, 76006, 102431, and 116360) are determined for the first time.

Structure-Specified H∞ Loop Shaping Control for Balancing of Bicycle Robots: A Particle Swarm Optimization Approach

The Particle swarm optimization (PSO) approach is an efficient meta-heuristic search method that can be used to solve the multi-objective and non-convex optimization problems that are normally generated when designing structure-specified H∞ loop shaping controllers. In this study, PSO is used to design the controllers able to balance a bicycle robot, with a model-based systematic procedure for the controller design being proposed. Simulation and experimental results are presented that show that superior robustness and efficiency properties were obtained using the structure-specified H∞ loop shaping controllers compared to those obtained using the proportional plus derivative as well as conventional H∞ loop shaping ones. In comparison with the solutions obtained based on genetic algorithms, the use of PSO has a better efficiency in term of the computational time.

Accelerated similarity searching and clustering of large compound sets by geometric embedding and locality sensitive hashing

Motivation: Similarity searching and clustering of chemical compounds by structural similarities are important computational approaches for identifying drug-like small molecules. Most algorithms available for these tasks are limited by their speed and scalability, and cannot handle today's large compound databases with several million entries.Results: In this article, we introduce a new algorithm for accelerated similarity searching and clustering of very large compound sets using embedding and indexing (EI) techniques. First, we present EI-Search as a general purpose similarity search method for finding objects with similar features in large databases and apply it here to searching and clustering of large compound sets. The method embeds the compounds in a high-dimensional Euclidean space and searches this space using an efficient index-aware nearest neighbor search method based on locality sensitive hashing (LSH). Second, to cluster large compound sets, we introduce the EI-Clustering algorithm that combines the EI-Search method with Jarvis–Patrick clustering. Both methods were tested on three large datasets with sizes ranging from about 260 000 to over 19 million compounds. In comparison to sequential search methods, the EI-Search method was 40–200 times faster, while maintaining comparable recall rates. The EI-Clustering method allowed us to significantly reduce the CPU time required to cluster these large compound libraries from several months to only a few days.Availability: Software implementations and online services have been developed based on the methods introduced in this study. The online services provide access to the generated clustering results and ultra-fast similarity searching of the PubChem Compound database with subsecond response time.Contact: thomas.girke@ucr.eduSupplementary information: Supplementary data are available at Bioinformatics online.

A Simple and Efficient Method to Extract Keyposes from Mocap Data

Researchers have been constantly developing new methods for generating realistic human motion. A common and popular approach is to use motion capture data or mocap data. However, this approach produces a mountain of data and there is an increasing necessity for quick and efficient mocap data search methods. For text documents, search results are usually returned in the form of a list of documents' titles. This allows users to quickly browse the search results and easily find the documents that they are looking for. Applying this method to mocap data requires the extraction of keyposes since displaying every frame will only present excessive and unnecessary information. The authors of this paper propose a simple and efficient method to automatically extract keyposes of mocap data. The proposed method requires only O(kn) operations. Thus, users can vary the search parameters and results will be presented interactively. Our results show that the proposed approach is applicable to mocap data of complex motions and motions composing of many motion segments.

Genome-Wide Conditional Search for Epistatic Disease-Predisposing Variants in Human Association Studies

Genome-wide search for new disease variants, based on well-established variants, has a long history in linkage analysis but is less well-known in genetic case-control association studies. We developed a simple yet highly efficient conditional search method that can find new variants, which are associated with a disease only through epistatic interaction with another variant and do not necessarily have a direct association effect. Our approach is analogous to partitioning of χ² in a hierarchical design, which is a well-established statistical technique. Applied to previously published data on age-related macular degeneration, our method found two single-nucleotide polymorphisms with genome-wide significant epistatic interaction that could not be found based only on direct main effects.

Power Allocation for Maximizing the Minimum Rate With QoS Constraints

Max-min fair power allocation brings higher average throughput and better utilization of the resources than a work-conserving equal-sharing policy. In this paper, instead of achieving a common maximum sum-rate objective, an optimal power-allocation design for maximizing the minimum rate of the reverse link of code-division multiple-access (CDMA) systems with quality-of-service (QoS) constraints is investigated. To avoid the difficulty of directly solving a nonconvex optimization problem, we split the original problem into two successive steps. First, we try to derive the structure of the optimum solution via majorization theory. Then, based on this result, we propose a very efficient search method to find the max-min fair solution, which reduces the search space from a 1-D space into a finite set of points. Compared with existing methods, a much lighter computational complexity is required by our method.

RFID 시스템에서 Hash-Chain기반 Tag-Grouping을 이용한 안전하고 효율적인 데이터베이스 검색

RFID(Radion Frequency Identification)는 바코드를 대체할 차세대 기술이다. RFID는 무선 주파수를 사용하여 RFID 태그 내부에 있는 ID를 읽어 물체를 식별할 수 있다. 하지만 RFID 태그는 어떤 리더의 요청에도 자신의 고유 ID를 무선 통신으로 응답하기 때문에 도청이나 불법 리더로부터 보안이나 프라이버시에 대한 공격에 취약하다. RFID 인증 프로토콜은 보안과 프라이버시 문제를 해결하기 위해 활발히 연구되고 있으며 태그 검색에도 사용되고 있다. 최근에는 RFID 시스템에서 태그의 수가 증가하고 데이터 수집 비용도 늘어나면서 효과적인 태그 검색에 대한 비중이 더 많아졌다. 본 논문에서는 보안과 프라이버시 보호를 위한 필요조건을 보장하는 Miyako Ohkubo의 hash-chain 메커니즘에서 데이터베이스 연산량이 많은 문제점을 Tag-Grouping을 통해 보완한 효율적인 검색 방법을 제안한다. 실험 결과 데이터베이스에서 10만개의 레코드 수를 기준으로 접근비율이 5이상인 태그 그룹의 우선 검색시 약 30%의 검색시간이 감소 하였다. RFID (Radio Frequency Identification) is a next generation technology that will replace barcode. RFID can identify an object by reading ID inside a RFID tag using radio frequency. However, because a RFID tag replies its unique ID to the request of any reader through wireless communication, it is vulnerable to attacks on security or privacy through wiretapping or an illegal reader's request. The RFID authentication protocol has been studied actively in order to solve security and privacy problems, and is used also in tag search. Recently, as the number of tags is increasing in RFTD systems and the cost of data collection is also rising, the importance of effective tag search is increasing. This study proposed an efficient search method that solved through ta9 group the problem of large volume of database computation in Miyako Ohkubo's hash chain mechanism, which meets requirements for security and privacy protection. When we searched first the group of tags with access rate of 5 or higher in a database with 100,000 records, search time decreased by around 30%.

MicroarrayDesigner: an online search tool and repository for near-optimal microarray experimental designs

BackgroundDual-channel microarray experiments are commonly employed for inference of differential gene expressions across varying organisms and experimental conditions. The design of dual-channel microarray experiments that can help minimize the errors in the resulting inferences has recently received increasing attention. However, a general and scalable search tool and a corresponding database of optimal designs were still missing.DescriptionAn efficient and scalable search method for finding near-optimal dual-channel microarray designs, based on a greedy hill-climbing optimization strategy, has been developed. It is empirically shown that this method can successfully and efficiently find near-optimal designs. Additionally, an improved interwoven loop design construction algorithm has been developed to provide an easily computable general class of near-optimal designs. Finally, in order to make the best results readily available to biologists, a continuously evolving catalog of near-optimal designs is provided.ConclusionA new search algorithm and database for near-optimal microarray designs have been developed. The search tool and the database are accessible via the World Wide Web at . Source code and binary distributions are available for academic use upon request.

A Study of Parallel Efficiency of Modified Direct Algorithm Applied to Thermohydrodynamic Lubrication

AbstractThis study examines the parallel computing as a means to minimize the execution time in the optimization applied to thermohydrodynamic (THD) lubrication. The objective of the optimization is to maximize the load capacity of a slider bearing with two design variables. A global optimization method, DIviding RECTangle (DIRECT) algorithm, is used. The first approach was to apply the parallel computing within the THD model in a shared-memory processing (SMP) environment to examine the parallel efficiency of fine-grain computation. Next, a distributed parallel computing in the search level was conducted by use of the standard DIRECT algorithm. Then, the algorithm is modified to provide a version suitable for effective parallel computing. In the latter coarse-grain computation the speedups obtained by the DIRECT algorithms are compared with some previous studies using other parallel optimization methods. In the fine-grain computation of the SMP machine, the communication and overhead time costs prohibit high speedup in the cases of four or more simultaneous threads. It is found that the standard DIRECT algorithm is an efficient sequential but less parallel-computing-friendly method. When the modified algorithm is used in the slider bearing optimization, a parallel efficiency of 96.3% is obtained in the 16-computing-node cluster. This study presents the modified DIRECT algorithm, an efficient parallel search method, for general engineering optimization problems.

How many hidden layers and nodes?

The question of how many hidden layers and how many hidden nodes should there be always comes up in any classification task of remotely sensed data using neural networks. Until today there has been no exact solution. A method of shedding some light to this question is presented in this paper. A near‐optimal solution is discovered after searching with a genetic algorithm. A novel fitness function is introduced that concurrently seeks for the most accurate and compact solution. The proposed method is thoroughly compared to many other methods currently in use, including several heuristics and pruning algorithms. The results are encouraging, indicating that it is time to shift our focus from suboptimal practices to efficient search methods, to tune the parameters of neural networks.

Optimal Periodic Memory Allocation for Image Processing With Multiple Windows

One major issue in designing image processors is to design a memory system that supports parallel access with a simple interconnection network. This paper presents an efficient memory allocation to minimize the number of memory modules and processing elements with a parallel access capability when multiple windows with arbitrary shapes are specified. This paper also presents an efficient search method based on regularity of window-type image processing. We give some practical examples including a stereo-matching processor for acquiring 3-D information, and an optical-flow processor for motion estimation. These examples show that the numbers of memory modules are reduced to 2.7% and 10%, respectively, in comparison with a basic approach. It is also shown that the search time is less than 1 ms for practical image sizes and window sizes.

Generalized ensemble methods for de novo structure prediction

Current methods for predicting protein structure depend on two interrelated components: (i) an energy function that should have a low value near the correct structure and (ii) a method for searching through different conformations of the polypeptide chain. Identification of the most efficient search methods is essential if we are to be able to apply such methods broadly and with confidence. In addition, efficient search methods provide a rigorous test of existing energy functions, which are generally knowledge-based and contain different terms added together with arbitrary weights. Here, we test different search methods with one of the most accurate and predictive energy functions, namely Rosetta the knowledge-based force-field from Baker's group [Simons K, Kooperberg C, Huang E, Baker D (1997) J Mol Biol 268:209-225]. We use an implementation of a generalized ensemble search method to scale relevant parts of the energy function. This method, known as Hamiltonian Replica Exchange Monte Carlo, outperforms the original Monte Carlo Simulated Annealing used in the Rosetta package in terms of sampling low-energy states. It also outperforms another widely used generalized ensemble search method known as Temperature Replica Exchange Monte Carlo. Our results reveal clear deficiencies in the low-resolution Rosetta energy function in that the lowest energy structures are not necessarily the most native-like. By using a set of nonnative low-energy structures found by our extensive sampling, we discovered that the long-range and short-range backbone hydrogen-bonding energy terms of the Rosetta energy discriminate between the nonnative and native-like structures significantly better than the low-resolution score used in Rosetta.

An efficient search method for multi-objective flexible job shop scheduling problems

Flexible job shop scheduling is very important in both fields of production management and combinatorial optimization. Owing to the high computational complexity, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches. Motivated by some empirical knowledge, we propose an efficient search method for the multi-objective flexible job shop scheduling problems in this paper. Through the work presented in this work, we hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The final experimental results have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.

Improving Distributed Resource Search through a Statistical Methodology of Topological Feature Selection

The Internet is considered a complex network for its size, interconnectivity and rules that govern are dynamic, because of constantly evolve. For this reason the search of distributed resources shared by users and online communities is a complex task that needs efficient search method. The goal of this work is to improve the performance of distributed search of information, through analysis of the topological features. In this paper we described a statistical methodology to select a set of topologic metrics that allow to locally distinguish the type of complex network. In this way we use the metrics to guide the search towards nodes with better connectivity. In addition we present an algorithm for distributed search of information, enriched with the selected topological metric. The results show that including the topological metric in the Neighboring-Ant Search algorithm improves its performance 50% in terms of the number of hops needed to locate a set of resources. The methodology described provides a better understanding of why the features were selected and aids to explain how this metric impacts in the search process.