Smaller Search Space Research Articles

Reduced Search Space Algorithm for Simultaneous Localization and Mapping in Mobile Robots

In this paper, we propose a new algorithm for simultaneous localization and mapping in mobile robots which uses evolutionary algorithm and particle swarm optimization. The proposed method is based on both local and global heuristic search methods. In each step of robot movements, the local search is applied in the small search space of odometry errors to improve the map accuracy. A global search method is applied for loop closing. The proposed algorithm detects loops and closes them, detects and solves correspondence and avoids local extremums. With a proper representation of problem parameters in chromosome, the dimensionality of search space is reduced. The proposed algorithm utilizes occupancy grid and does not require land marks which are not available in most natural environments. A new fitness function is proposed that is computationally efficient and eliminates the need for complex statistical calculations as used in current approaches. Results of experiments on real datasets exhibit the superior performance of the proposed method compared to the current methods. DOI: http://dx.doi.org/10.11591/ijra.v1i1.274 Full Text: PDF

Rebirthing genetic algorithm for storm sewer network design

Application of standard binary coded genetic algorithms for the solution of problems with continuous design variables requires discretization of the continuous decision variables. Coarse discretization of the design variables could adversely affect the final solution, while finer discretization would increasingly enlarge the scale of the problem, leading to higher computation cost. A rebirthing procedure is used in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is refined and a completely new search is conducted to find a better solution. The procedure is continued until no refinement is necessary or no improvement could be made by further refinement. The method is applied to a benchmark problem of a storm water network design, and the results are compared with those of the existing method. The method is shown to be very effective, efficient and insensitive to the population size of the genetic search and the search space size of the optimization problem.

Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method

In this paper, wind power generators are being incorporated in the multiobjective economic emission dispatch problem which minimizes wind-thermal electrical energy cost and emissions produced by fossil-fueled power plants, simultaneously. Large integration of wind energy sources necessitates an efficient model to cope with uncertainty arising from random wind variation. Hence, a multiobjective stochastic search algorithm based on 2 m point estimated method is implemented to analyze the probabilistic wind-thermal economic emission dispatch problem considering both overestimation and underestimation of available wind power. 2 m point estimated method handles the system uncertainties and renders the probability density function of desired variables efficiently. Moreover, a new population-based optimization algorithm called modified teaching-learning algorithm is proposed to determine the set of non-dominated optimal solutions. During the simulation, the set of non-dominated solutions are kept in an external memory (repository). Also, a fuzzy-based clustering technique is implemented to control the size of the repository. In order to select the best compromise solution from the repository, a niching mechanism is utilized such that the population will move toward a smaller search space in the Pareto-optimal front. In order to show the efficiency and feasibility of the proposed framework, three different test systems are represented as case studies.

CrossWork: Software-assisted identification of cross-linked peptides

The increased interest in chemical cross-linking for probing protein structure and interaction has led to a large increase in literature describing new cross-linkers and search programs. However, this has not led to a corresponding increase in the analysis of large and complex proteins. A major obstacle is that the new cross-linkers are either not readily available and/or have a low reactivity. In combination with aging search programs that are slow and have low sensitivity, or new search programs that are described but not released, these efforts do little to advance the field of cross-linking. Here we present a method pipeline for chemical cross-linking, using two standard cross-linkers, BS3 and BS2G, combined with our freely available CrossWork search program. By this approach we generate cross-link data sufficient to derive structural information for large and complex proteins. CrossWork searches batches of tandem mass-spectrometric data, and identifies cross-linked and non-cross-linked peptides using a standard PC. We tested CrossWork by searching mass-spectrometric datasets of cross-linked complement factor C3 against small (1 protein) and large (1000 proteins) search spaces, and show that the resulting distance constraints agree with the established structures. We further investigated the structure of the multi-domain ERp72, and combined the individual domains of ERp72 into a single structure.

Line detection with adaptive random samples

This paper examines the detection of parameterized shapes in multidimensional noisy grayscale images. A novel shape detection algorithm utilizing random sample theory is presented. Although the method can be generalized, line detection is detailed. Each line in the image corresponds to a point in the line parameter space. The method creates hypothesis lines by randomly selecting parameter space points and tests the surrounding regions for acceptable linear features. The information obtained from each randomly selected line is used to update the parameter distribution, which reducesi the required number of random trials. The selected lines are re-estimated within a smaller search space with a more accurate algorithm like the Hough transform (HT). Faster results are obtained compared to HT, without losing performance as in other faster HT variants. The method is robust and suitable for binary or grayscale images. Results are given from both simulated and experimental subsurface seismic and ground penetrating radar (GPR) images when searching for features like pipes or tunnels.

An Implementation of Frequent Pattern Mining Algorithm using Dynamic Function

ABSTRACT An Important Problem in Data Mining in Various Fields like Medicine, Telecommunications and World Wide Web is Discovering Patterns. Frequent patterns mining is the focused research topic in association rule analysis. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori Algorithm. Most of the previous studies adopt Apriori-like algorithms which generate-and-test candidates and improving algorithm strategy and structure but no one concentrate on the structure of database. A simple approach is if we implement in Transposed database then result is very fast. Recently, different works proposed a new way to mine patterns in transposed databases where a database with thousands of attributes but only tens of objects. In this case, mining the transposed database runs through a smaller search space. In this paper, we systematically explore the search space of frequent patterns mining and represent database in transposed form. We developed an algorithm (termed DFPMT—A Dynamic Approach for Frequent Patterns Mining Using Transposition of Database) for mining frequent patterns which are based on Apriori algorithm and used Dynamic function for Longest Common Subsequence [1]. The main distinguishing factors among the proposed schemes is the database stores in transposed form and in each iteration database is filter /reduce by generating LCS of transaction id for each pattern. Our solutions provide faster result. A quantitative exploration of these tradeoffs is conducted through an extensive experimental study on synthetic and real-life data sets.

Hybrid enhanced continuous tabu search and genetic algorithm for parameter estimation in colored noise environments

Parameter estimation is an important concept in engineering where a mathematical model of a system is identified with the help of input and output signals. Classical parameter estimation algorithms such as Least Squares (LS), Recursive Least Squares (RLS), Least Mean Squares (LMS) and Generalized Least Squares (GLS) give an unbiased estimate of the parameters when the system noise is white. This property is lost when the system noise is colored which is generally the case for many practical situations. In order to overcome the bias problem associated with the colored noise environment, one can use a whitening filter. The cost function of the estimation problem in the case of a colored noise environment becomes multimodal when the signal to noise ratio is high. Hence the motivation to use some intelligent optimization technique for the purpose of finding the global minimum of the parameter estimation problem. A new hybrid algorithm combining intelligent optimization techniques, i.e. enhanced continuous tabu search (ECTS) and elitism based genetic algorithm (GA) is proposed and is applied to the parameter estimation problem. In this work, the ECTS is used to define smaller search spaces, which are investigated in a second stage by a GA to find the respective local minima. Simulation results show that the parameters estimated using the proposed algorithm is unbiased in the presence colored noise. In addition, the hybrid algorithm is also tested with known multimodal benchmark problems.

A Comparison Study of Transcription Factor – DNA Binding Models

The comparison study is drawn between two widely used motif representations i.e. Positional Weight Matrices (PWM) and Consensus Sequences. In the case of motif finding, where the binding sites are not known a priori but the algorithm must search a large space of possible binding sites, the PWM model may be difficult to learn as the search space is very large even for the PWM of short length (RN for a PWM of length N, where R is the space of real numbers between 0 to 1). Optimization methods used to search for the best PWM may converge to a local minimum. On the other hand the consensus sequence has a smaller search space (15N for a motif of length N) which is easier to search for the global optimum.

An Optimized Algorithm for Diagnosability of Component-based Systems

Diagnosability is a crucial system property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. The existence of two indistinguishable behaviors, i.e. holding the same observations, with exactly one of them containing the fault violates the diagnosability property. A classical approach for diagnosability verification consists in constructing a finite state machine called twin plant to search for a path representing such indistinguishable behaviors, called a critical path. To avoid the unrealistic hypothesis about the monolithic model of a complex system, recent work constructs local twin plants and then incrementally synchronizes some of them until diagnosability is decided without computing the impractical global twin plant. In this paper, we optimize the distributed approach by abstracting necessary and sufficient diagnosability information from local twin plants to check the existence of critical paths. Thus diagnosability can be analyzed with as small search space as possible. Furthermore, our approach describes how to improve the diagnosis algorithm by using our diagnosability results in a formal way when the system is verified to be diagnosable. Finally, when the system is not diagnosable, the algorithm returns some useful information about its indistinguishable behaviors, which can help in upgrading system diagnosable level.

Quantum-behaved particle swarm optimization algorithm with random selection of optimal individual

The particles are easy to mass into a small search space in late stage and thus the diversity of swarms decline.Based on the analysis of quantum-behaved Particle Swarm Optimization(PSO) algorithm,a method of random selection of optimal individual was proposed to improve the diversity in searching progress.The of the improved QPSO was compared with the original PSO and the original QPSO using the testing function of the benchmark.Experimental results demonstrate that the improved QPSO is more suitable for resolving the multi-peak optimization problem.

Synthesis of distillation configurations: I. Characteristics of a good search space

The exhaustive enumeration of all possible distillation configurations for zeotropic n-component feed mixtures into n nearly pure product streams leads to an exponentially large number of configurations, making the search for an optimal configuration intractable for even a modest number of components. To reduce the search space to a manageable level, we divide the column configurations into two categories: (i) Basic, configurations having ( n − 1) distillation columns; and (ii) Non-basic, configurations having more columns. On exhaustively simulating multiple four-component feed conditions using Underwood's method for pinched columns, we find that for none of the simulated feed conditions does a non-basic configuration have a lower heat duty than the lowest heat duty basic configuration. Based on this observation, we assert that a much smaller search space containing only basic configurations should be sufficient to find an optimal n-component configuration. We also find basic configurations allowing non-sharp splits (ABC → AB/BC) have lower heat duty more often than those with only sharp splits (ABC → A/BC or ABC → AB/C). Finally, the heat duty reduction due to thermal coupling is found to be less when compared to non-sharp basic configurations as against sharp-split basic configurations.

Baseband implementation of OTR-UWB receiver using FPGA

This paper presents a new transmitted reference ultra-wideband receiver, which utilizes the orthogonal property of even and odd order derivatives of Gaussian pulses in neighboring chips for synchronization. This system, referred to as orthogonal TR-UWB (OTR-UWB), employs only a single spreading code, which results in much lower mean detection time compared to DS-UWB systems since smaller search space is involved for coarse acquisition. The hardware complexity for OTR-UWB receiver is significantly reduced against conventional TR-UWB systems. In addition, simulation results show that bit error rate (BER) performance is improved, while the new system is capable of supporting higher data rates. Also, this paper presents a technique for implementing low rate OTR-UWB system in FPGA by combining pipeline scheduling, parallel component, and module selection. The system has been implemented on two FPGA platforms from ALTERA and XILINX, CycloneII (EP2C35F672C6) and Spartan 3 (3s4000fg676-5)with a bit-rate of 25Mb/s without using equalizer. It incorporates 4-bit of resolution with 8GHz frequency sampling rate for analog to digital conversion realized by parallel low frequencyADC blocks. Gate estimation and power analysis are performed by Quartus II 7.1 (ALTERA) and ISE 8.1 (XILINX) softwares.

Tabu Search Method for Solving the Traveling salesman Problem

This research presents the fundamental concepts of tabu search for optimization problem as symmetric traveling salesman problem. The purpose of this research is to solve the traveling salesman problem with tabu search method, to find an optimal solution of small search space and computational requirements.

Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition

A clustering method, called HACO (Hyperbox clustering with Ant Colony Optimization), is proposed for classifying unlabeled data using hyperboxes and an ant colony meta-heuristic. It acknowledges the topological information (inherently associated to classification) of the data while looking in a small search space, providing results with high precision in a short time. It is validated using artificial 2D data sets and then applied to a real medical data set, automatically extracting medical risk profiles, a laborious operation for doctors. Clustering results show an improvement of 36% in accuracy and 7 times faster processing time when compared to the usual ant colony optimization approach. It can be further extended to hyperbox shape optimization (fine tune accuracy), automatic parameter setting (improve usability), and applied to diagnosis decision support systems.

Automatic semantic Web services composition based on complete backward tree`

Compared to the traditional ways of composing Web services,semantic-based ways of composing Web services automatically are more effective and automatic.An approach based on complete backward tree to compose semantic Web services automatically was proposed.It not only added the semantics to Web services,but also had a smaller search space limited to the complete backward tree.Experimental results show that this method improves both the efficiency and the successful ratio of Web services composition.

Reducing the search space of a linear fractional programming problem under fuzzy relational equations with max-Archimedean t-norm composition

Prior studies have demonstrated that one of the minimal solutions of a fuzzy relational equation with the max-Archimedean t-norm composition is an optimal solution of a linear objective function with positive coefficients. However, this property cannot be adopted to optimize the problem of a linear fractional objective function. This study presents an efficient method to optimize such a linear fractional programming problem. First, some theoretical results are developed based on the properties of max-Archimedean t-norm composition. The result is used to reduce the feasible domain. The problem can thus be simplified and converted into a traditional linear fractional programming problem, and eventually optimized in a small search space. A numerical example is provided to illustrate the procedure.

Layout and size optimization of tree-like pipe networks by incremental solution building ants

Application of an ant colony optimization algorithm (ACOA) for simultaneous layout and size optimization of tree-like pipe networks is described in this paper using two different formulations. In the first formulation, each link of the base graph is considered as the decision point of the problem. Each decision point is considered in turn and the ants are then required to choose any of the available options at the current decision point. The list of available pipe diameters with the null option included for each link constitutes the available options in this formulation. In the second approach, the network nodes are considered as the decision points of the problem. The available options in this formulation are represented by the list of allowable pipe diameters for all plausible links such that the resulting network is a tree network. The plausible links at each decision point are provided by a tree-growing algorithm. This formulation leads to a very small search space compared with the first algorithm, as each ant is now forced to create a feasible solution regarding the layout geometry of the network. This approach fully exploits the sequential nature of the ACOA in building solutions, which is believed to be one of the main advantages of these algorithms compared with other general metaheuristics. The proposed methods are applied to find the optimal layout of two benchmark examples in the published literature and the results are presented and compared with the existing results.

The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra

The Paragon Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user interface that removes the user expertise requirement, presenting control settings in the language of the laboratory that are translated to optimal algorithmic settings. To validate this new algorithm, a comparison with Mascot is presented for a series of analogous searches to explore the relative impact of increasing search space probed with Mascot by relaxing the tryptic digestion conformance requirements from trypsin to semitrypsin to no enzyme and with the Paragon Algorithm using its Rapid mode and Thorough mode with and without tryptic specificity. Although they performed similarly for small search space, dramatic differences were observed in large search space. With the Paragon Algorithm, hundreds of biological and artifact modifications, all possible substitutions, and all levels of conformance to the expected digestion pattern can be searched in a single search step, yet the typical cost in search time is only 2-5 times that of conventional small search space. Despite this large increase in effective search space, there is no drastic loss of discrimination that typically accompanies the exploration of large search space.

SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in Solving Difference Logic

Existing difference logic (DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel difference logic solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in difference predicates. This phase is similar to previous small domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and smaller ranges for variables. Furthermore, the difference logic formula is not transformed into an equi-satisfiable Boolean formula in a single step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it cannot be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state of-the art solvers using similar eager or lazy techniques.

Automatic Web Service Composition Based on Backward Tree

An approach based on backward tree to compose services automatically is proposed. It composes services for a user through three steps: 1) Builds a complete backward trees on-line; 2) Searches for optimal valid generation sources (generation-paths); 3) Composes generation paths. Compared to traditional graph-based methods, it has a smaller search space and avoids the repetition search. Experimental results show that this method has a good performance even the repository has a large number of services.