Optimality of Spatially Inhomogeneous Search Strategies.
Schwarz, Karsten; Schröder, Yannick; Qu, Bin; Hoth, Markus; Rieger, Heiko
2016-08-05
We consider random search processes alternating stochastically between diffusion and ballistic motion, in which the distribution function of ballistic motion directions varies from point to point in space. The specific space dependence of the directional distribution together with the switching rates between the two modes of motion establishes a spatially inhomogeneous search strategy. We show that the mean first passage times for several standard search problems-narrow escape, reaction partner finding, reaction escape-can be minimized with a directional distribution that is reminiscent of the spatial organization of the cytoskeleton filaments of cells with a centrosome: radial ballistic transport from the center to the periphery and back, and ballistic transport in random directions within a concentric shell of thickness Δ_{opt} along the domain boundary. The results suggest that living cells realize efficient search strategies for various intracellular transport problems economically through a spatial cytoskeleton organization that involves radial microtubules in the central region and only a narrow actin cortex rather than a cell body filled with randomly oriented actin filaments.
Optimality of Spatially Inhomogeneous Search Strategies
Schwarz, Karsten; Schröder, Yannick; Qu, Bin; Hoth, Markus; Rieger, Heiko
2016-08-01
We consider random search processes alternating stochastically between diffusion and ballistic motion, in which the distribution function of ballistic motion directions varies from point to point in space. The specific space dependence of the directional distribution together with the switching rates between the two modes of motion establishes a spatially inhomogeneous search strategy. We show that the mean first passage times for several standard search problems—narrow escape, reaction partner finding, reaction escape—can be minimized with a directional distribution that is reminiscent of the spatial organization of the cytoskeleton filaments of cells with a centrosome: radial ballistic transport from the center to the periphery and back, and ballistic transport in random directions within a concentric shell of thickness Δopt along the domain boundary. The results suggest that living cells realize efficient search strategies for various intracellular transport problems economically through a spatial cytoskeleton organization that involves radial microtubules in the central region and only a narrow actin cortex rather than a cell body filled with randomly oriented actin filaments.
Optimal search strategies on complex networks
Di Patti, Francesca; Piazza, Francesco
2014-01-01
Complex networks are ubiquitous in nature and play a role of paramount importance in many contexts. Internet and the cyberworld, which permeate our everyday life, are self-organized hierarchical graphs. Urban traffic flows on intricate road networks, which impact both transportation design and epidemic control. In the brain, neurons are cabled through heterogeneous connections, which support the propagation of electric signals. In all these cases, the true challenge is to unveil the mechanisms through which specific dynamical features are modulated by the underlying topology of the network. Here, we consider agents randomly hopping along the links of a graph, with the additional possibility of performing long-range hops to randomly chosen disconnected nodes with a given probability. We show that an optimal combination of the two jump rules exists that maximises the efficiency of target search, the optimum reflecting the topology of the network.
A Competitive and Experiential Assignment in Search Engine Optimization Strategy
Clarke, Theresa B.; Clarke, Irvine, III
2014-01-01
Despite an increase in ad spending and demand for employees with expertise in search engine optimization (SEO), methods for teaching this important marketing strategy have received little coverage in the literature. Using Bloom's cognitive goals hierarchy as a framework, this experiential assignment provides a process for educators who may be…
A Competitive and Experiential Assignment in Search Engine Optimization Strategy
Clarke, Theresa B.; Clarke, Irvine, III
2014-01-01
Despite an increase in ad spending and demand for employees with expertise in search engine optimization (SEO), methods for teaching this important marketing strategy have received little coverage in the literature. Using Bloom's cognitive goals hierarchy as a framework, this experiential assignment provides a process for educators who may be new…
A Competitive and Experiential Assignment in Search Engine Optimization Strategy
Clarke, Theresa B.; Clarke, Irvine, III
2014-01-01
Despite an increase in ad spending and demand for employees with expertise in search engine optimization (SEO), methods for teaching this important marketing strategy have received little coverage in the literature. Using Bloom's cognitive goals hierarchy as a framework, this experiential assignment provides a process for educators who may be new…
Optimal search strategies on complex multi-linked networks
Di Patti, Francesca; Fanelli, Duccio; Piazza, Francesco
2015-01-01
In this paper we consider the problem of optimal search strategies on multi-linked networks, i.e. graphs whose nodes are endowed with several independent sets of links. We focus preliminarily on agents randomly hopping along the links of a graph, with the additional possibility of performing non-local hops to randomly chosen nodes with a given probability. We show that an optimal combination of the two jump rules exists that maximises the efficiency of target search, the optimum reflecting the topology of the network. We then generalize our results to multi-linked networks with an arbitrary number of mutually interfering link sets. PMID:25950716
Wiegmann, Daniel D; Seubert, Steven M; Wade, Gordon A
2010-02-21
The behavior of a female in search of a mate determines the likelihood that she encounters a high-quality male in the search process. The fixed sample (best-of-n) search strategy and the sequential search (fixed threshold) strategy are two prominent models of search behavior. The sequential search strategy dominates the former strategy--yields an equal or higher expected net fitness return to searchers--when search costs are nontrivial and the distribution of quality among prospective mates is uniform or truncated normal. In this paper our objective is to determine whether there are any search costs or distributions of male quality for which the sequential search strategy is inferior to the fixed sample search strategy. The two search strategies are derived under general conditions in which females evaluate encountered males by inspection of an indicator character that has some functional relationship to male quality. The solutions are identical to the original models when the inspected male attribute is itself male quality. The sequential search strategy is shown to dominate the fixed sample search strategy for all search costs and distributions of male quality. Low search costs have been implicated to explain empirical observations that are consistent with the use of a fixed sample search strategy, but under conditions in which the original models were derived there is no search cost or distribution of male quality that favors the fixed sample search strategy. Plausible alternative explanations for the apparent use of this search strategy are discussed.
Evolutionary and principled search strategies for sensornet protocol optimization.
Tate, Jonathan; Woolford-Lim, Benjamin; Bate, Iain; Yao, Xin
2012-02-01
Interactions between multiple tunable protocol parameters and multiple performance metrics are generally complex and unknown; finding optimal solutions is generally difficult. However, protocol tuning can yield significant gains in energy efficiency and resource requirements, which is of particular importance for sensornet systems in which resource availability is severely restricted. We address this multi-objective optimization problem for two dissimilar routing protocols and by two distinct approaches. First, we apply factorial design and statistical model fitting methods to reject insignificant factors and locate regions of the problem space containing near-optimal solutions by principled search. Second, we apply the Strength Pareto Evolutionary Algorithm 2 and Two-Archive evolutionary algorithms to explore the problem space, with each iteration potentially yielding solutions of higher quality and diversity than the preceding iteration. Whereas a principled search methodology yields a generally applicable survey of the problem space and enables performance prediction, the evolutionary approach yields viable solutions of higher quality and at lower experimental cost. This is the first study in which sensornet protocol optimization has been explicitly formulated as a multi-objective problem and solved with state-of-the-art multi-objective evolutionary algorithms.
Walters, Leslie A; Wilczynski, Nancy L; Haynes, R Brian
2006-01-01
Qualitative researchers address many issues relevant to patient health care. Their studies appear in an array of journals, making literature searching difficult. Large databases such as EMBASE provide a means of retrieving qualitative research, but these studies represent only a minuscule fraction of published articles, making electronic retrieval problematic. Little work has been done on developing search strategies for the detection of qualitative studies. The objective of this study was to develop optimal search strategies to retrieve qualitative studies in EMBASE for the 2000 publishing year. The authors conducted an analytic survey, comparing hand searches of journals with retrievals from EMBASE for candidate search terms and combinations. Search strategies reached peak sensitivities at 94.2% and peak specificities of 99.7%. Combining search terms to optimize the combination of sensitivity and specificity resulted in values over 89% for both. The authors identified search strategies with high performance for retrieving qualitative studies in EMBASE.
Optimal swimming strategies in mate searching pelagic copepods
DEFF Research Database (Denmark)
Kiørboe, Thomas
2008-01-01
Male copepods must swim to find females, but swimming increases the risk of meeting predators and is expensive in terms of energy expenditure. Here I address the trade-offs between gains and risks and the question of how much and how fast to swim using simple models that optimise the number...... of lifetime mate encounters. Radically different swimming strategies are predicted for different feeding behaviours, and these predictions are tested experimentally using representative species. In general, male swimming speeds and the difference in swimming speeds between the genders are predicted...... and observed to increase with increasing conflict between mate searching and feeding. It is high in ambush feeders, where searching (swimming) and feeding are mutually exclusive and low in species, where the matured males do not feed at all. Ambush feeding males alternate between stationary ambush feeding...
Improved quantum-behaved particle swarm optimization with local search strategy
Directory of Open Access Journals (Sweden)
Maolong Xi
2017-03-01
Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.
Altomare, Cristina; Guglielmann, Raffaella; Riboldi, Marco; Bellazzi, Riccardo; Baroni, Guido
2015-02-01
In high precision photon radiotherapy and in hadrontherapy, it is crucial to minimize the occurrence of geometrical deviations with respect to the treatment plan in each treatment session. To this end, point-based infrared (IR) optical tracking for patient set-up quality assessment is performed. Such tracking depends on external fiducial points placement. The main purpose of our work is to propose a new algorithm based on simulated annealing and augmented Lagrangian pattern search (SAPS), which is able to take into account prior knowledge, such as spatial constraints, during the optimization process. The SAPS algorithm was tested on data related to head and neck and pelvic cancer patients, and that were fitted with external surface markers for IR optical tracking applied for patient set-up preliminary correction. The integrated algorithm was tested considering optimality measures obtained with Computed Tomography (CT) images (i.e. the ratio between the so-called target registration error and fiducial registration error, TRE/FRE) and assessing the marker spatial distribution. Comparison has been performed with randomly selected marker configuration and with the GETS algorithm (Genetic Evolutionary Taboo Search), also taking into account the presence of organs at risk. The results obtained with SAPS highlight improvements with respect to the other approaches: (i) TRE/FRE ratio decreases; (ii) marker distribution satisfies both marker visibility and spatial constraints. We have also investigated how the TRE/FRE ratio is influenced by the number of markers, obtaining significant TRE/FRE reduction with respect to the random configurations, when a high number of markers is used. The SAPS algorithm is a valuable strategy for fiducial configuration optimization in IR optical tracking applied for patient set-up error detection and correction in radiation therapy, showing that taking into account prior knowledge is valuable in this optimization process. Further work will be
Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning
Directory of Open Access Journals (Sweden)
Lejla Banjanovic-Mehmedovic
2011-01-01
Full Text Available This paper presents implementation of optimal search strategy (OSS in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.
Developing optimal search strategies for detecting clinically sound treatment studies in EMBASE*
Wong, Sharon S.-L.; Wilczynski, Nancy L; Haynes, R Brian
2006-01-01
Objective: The ability to accurately identify articles about therapy in large bibliographic databases such as EMBASE is important for researchers and clinicians. Our study aimed to develop optimal search strategies for detecting sound treatment studies in EMBASE in the year 2000.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-07-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
Directory of Open Access Journals (Sweden)
Shouheng Tuo
2013-01-01
Full Text Available Harmony search (HS algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL, is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.
Optimal search strategies for identifying sound clinical prediction studies in EMBASE
Directory of Open Access Journals (Sweden)
Haynes R Brian
2005-04-01
Full Text Available Abstract Background Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges" for retrieval of empirically tested clinical prediction guides from EMBASE. Methods An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. Results 163 clinical prediction studies were identified, of which 69 (42.3% passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1% was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8% was found in a 2-term strategy, but with a
Directory of Open Access Journals (Sweden)
Haynes R Brian
2004-06-01
Full Text Available Abstract Background Clinical end users of MEDLINE have a difficult time retrieving articles that are both scientifically sound and directly relevant to clinical practice. Search filters have been developed to assist end users in increasing the success of their searches. Many filters have been developed for the literature on therapy and reviews but little has been done in the area of prognosis. The objective of this study is to determine how well various methodologic textwords, Medical Subject Headings, and their Boolean combinations retrieve methodologically sound literature on the prognosis of health disorders in MEDLINE. Methods An analytic survey was conducted, comparing hand searches of journals with retrievals from MEDLINE for candidate search terms and combinations. Six research assistants read all issues of 161 journals for the publishing year 2000. All articles were rated using purpose and quality indicators and categorized into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized as 'pass' or 'fail' for methodologic rigor in the areas of prognosis and other clinical topics. Candidate search strategies were developed for prognosis and run in MEDLINE – the retrievals being compared with the hand search data. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. Results 12% of studies classified as prognosis met basic criteria for scientific merit for testing clinical applications. Combinations of terms reached peak sensitivities of 90%. Compared with the best single term, multiple terms increased sensitivity for sound studies by 25.2% (absolute increase, and increased specificity, but by a much smaller amount (1.1% when sensitivity was maximized. Combining terms to optimize both sensitivity and specificity achieved sensitivities and specificities of approximately 83% for each. Conclusion Empirically derived
Evolution of optimal L\\'evy-flight strategies in human mental searches
Radicchi, Filippo
2012-01-01
Recent analysis of empirical data [F. Radicchi, A. Baronchelli & L.A.N. Amaral. PloS ONE {\\bf 7}, e029910 (2012)] showed that humans adopt L\\'evy flight strategies when exploring the bid space in on-line auctions. A game theoretical model proved that the observed L\\'evy exponents are nearly optimal, being close to the exponent value that guarantees the maximal economical return to players. Here, we rationalize these findings by adopting an evolutionary perspective. We show that a simple evolutionary process is able to account for the empirical measurements with the only assumption that the reproductive fitness of a player is proportional to her search ability. Contrarily to previous modeling, our approach describes the emergence of the observed exponent without resorting to any strong assumptions on the initial searching strategies. Our results generalize earlier research, and open novel questions in cognitive, behavioral and evolutionary sciences.
Evolution of optimal Lévy-flight strategies in human mental searches
Radicchi, Filippo; Baronchelli, Andrea
2012-06-01
Recent analysis of empirical data [Radicchi, Baronchelli, and Amaral, PloS ONE1932-620310.1371/journal.pone.0029910 7, e029910 (2012)] showed that humans adopt Lévy-flight strategies when exploring the bid space in online auctions. A game theoretical model proved that the observed Lévy exponents are nearly optimal, being close to the exponent value that guarantees the maximal economical return to players. Here, we rationalize these findings by adopting an evolutionary perspective. We show that a simple evolutionary process is able to account for the empirical measurements with the only assumption that the reproductive fitness of the players is proportional to their search ability. Contrary to previous modeling, our approach describes the emergence of the observed exponent without resorting to any strong assumptions on the initial searching strategies. Our results generalize earlier research, and open novel questions in cognitive, behavioral, and evolutionary sciences.
Directory of Open Access Journals (Sweden)
Chun-Liang Lu
2014-12-01
Full Text Available Differential evolution (DE is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP. Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.
Optimal search strategies for detecting cost and economic studies in EMBASE
Directory of Open Access Journals (Sweden)
Haynes R Brian
2006-06-01
Full Text Available Abstract Background Economic evaluations in the medical literature compare competing diagnosis or treatment methods for their use of resources and their expected outcomes. The best evidence currently available from research regarding both cost and economic comparisons will continue to expand as this type of information becomes more important in today's clinical practice. Researchers and clinicians need quick, reliable ways to access this information. A key source of this type of information is large bibliographic databases such as EMBASE. The objective of this study was to develop search strategies that optimize the retrieval of health costs and economics studies from EMBASE. Methods We conducted an analytic survey, comparing hand searches of journals with retrievals from EMBASE for candidate search terms and combinations. 6 research assistants read all issues of 55 journals indexed by EMBASE for the publishing year 2000. We rated all articles using purpose and quality indicators and categorized them into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized for purpose (i.e., cost and economics and other clinical topics and depending on the purpose as 'pass' or 'fail' for methodologic rigor. Candidate search strategies were developed for economic and cost studies, then run in the 55 EMBASE journals, the retrievals being compared with the hand search data. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. Results Combinations of search terms for detecting both cost and economic studies attained levels of 100% sensitivity with specificity levels of 92.9% and 92.3% respectively. When maximizing for both sensitivity and specificity, the combination of terms for detecting cost studies (sensitivity increased 2.2% over the single term but at a slight decrease in specificity of 0.9%. The maximized combination of terms
National Research Council Canada - National Science Library
Altomare, Cristina; Guglielmann, Raffaella; Riboldi, Marco; Bellazzi, Riccardo; Baroni, Guido
2015-01-01
.... Such tracking depends on external fiducial points placement. The main purpose of our work is to propose a new algorithm based on simulated annealing and augmented Lagrangian pattern search (SAPS...
Robustness of optimal intermittent search strategies in one, two, and three dimensions.
Loverdo, C; Bénichou, O; Moreau, M; Voituriez, R
2009-09-01
Search problems at various scales involve a searcher, be it a molecule before reaction or a foraging animal, which performs an intermittent motion. Here we analyze a generic model based on such type of intermittent motion, in which the searcher alternates phases of slow motion allowing detection and phases of fast motion without detection. We present full and systematic results for different modeling hypotheses of the detection mechanism in space in one, two, and three dimensions. Our study completes and extends the results of our recent letter [Loverdo, Nat. Phys. 4, 134 (2008)] and gives the necessary calculation details. In addition, another modeling of the detection case is presented. We show that the mean target detection time can be minimized as a function of the mean duration of each phase in one, two, and three dimensions. Importantly, this optimal strategy does not depend on the details of the modeling of the slow detection phase, which shows the robustness of our results. We believe that this systematic analysis can be used as a basis to study quantitatively various real search problems involving intermittent behaviors.
Directory of Open Access Journals (Sweden)
Rony Baskoro Lukito
2014-12-01
Full Text Available The purpose of this research is how to optimize a web design that can increase the number of visitors. The number of Internet users in the world continues to grow in line with advances in information technology. Products and services marketing media do not just use the printed and electronic media. Moreover, the cost of using the Internet as a medium of marketing is relatively inexpensive when compared to the use of television as a marketing medium. The penetration of the internet as a marketing medium lasted for 24 hours in different parts of the world. But to make an internet site into a site that is visited by many internet users, the site is not only good from the outside view only. Web sites that serve as a medium for marketing must be built with the correct rules, so that the Web site be optimal marketing media. One of the good rules in building the internet site as a marketing medium is how the content of such web sites indexed well in search engines like google. Search engine optimization in the index will be focused on the search engine Google for 83% of internet users across the world using Google as a search engine. Search engine optimization commonly known as SEO (Search Engine Optimization is an important rule that the internet site is easier to find a user with the desired keywords.
Directory of Open Access Journals (Sweden)
Rony Baskoro Lukito
2014-12-01
Full Text Available The purpose of this research is how to optimize a web design that can increase the number of visitors. The number of Internet users in the world continues to grow in line with advances in information technology. Products and services marketing media do not just use the printed and electronic media. Moreover, the cost of using the Internet as a medium of marketing is relatively inexpensive when compared to the use of television as a marketing medium. The penetration of the internet as a marketing medium lasted for 24 hours in different parts of the world. But to make an internet site into a site that is visited by many internet users, the site is not only good from the outside view only. Web sites that serve as a medium for marketing must be built with the correct rules, so that the Web site be optimal marketing media. One of the good rules in building the internet site as a marketing medium is how the content of such web sites indexed well in search engines like google. Search engine optimization in the index will be focused on the search engine Google for 83% of internet users across the world using Google as a search engine. Search engine optimization commonly known as SEO (Search Engine Optimization is an important rule that the internet site is easier to find a user with the desired keywords.
Wang, Yan; Huang, Song; Ji, Zhicheng
2017-07-01
This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.
Strategy of changing cracking furnace feedstock based on improved group search optimization
Institute of Scientific and Technical Information of China (English)
Xiaoyu Nian; Zhenlei Wang; Feng Qian
2015-01-01
The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is pro-posed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the“excel-lent”infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Final y, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.
Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization
Modiri, Arezoo; Gu, Xuejun; Hagan, Aaron M.; Sawant, Amit
2016-01-01
Objective Evolutionary stochastic global optimization algorithms are widely used in large-scale, non-convex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. Methods We use particle swarm optimization (PSO) algorithm to solve a 4-dimensional radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally-used unconstrained, hard-constrained and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm- a popular RT optimization technique is also implemented and used. Results The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. Conclusion The proposed virtual search approach boosts the swarm search efficiency and, consequently, improves the optimization convergence rate and robustness for PSO. Significance RT planning is a large-scale, non-convex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems. PMID:27362755
Directory of Open Access Journals (Sweden)
Aldemar Araujo Castro
Full Text Available OBJECTIVE: To define and disseminate the optimal search strategy for clinical trials in the Latin American and Caribbean Health Science Literature (LILACS. This strategy was elaborated based on the optimal search strategy for MEDLINE recommended by Cochrane Collaboration for the identification of clinical trials in electronic databases. DESIGN: Technical information. SETTING: Clinical Trials and Meta-Analysis Unit, Federal University of São Paulo, in conjunction with the Brazilian Cochrane Center, São Paulo, Brazil. (http://www.epm.br/cochrane. DATA: LILACS/CD-ROM (Latin American and Caribbean Health Science Information Database, 27th edition, January 1997, edited by BIREME (Latin American and Caribbean Health Science Information Center. LILACS Indexes 670 journals in the region, with abstracts in English, Portuguese or Spanish; only 41 overlap in the MEDLINE-EMBASE. Of the 168.902 citations since 1982, 104,016 are in human trials, and 38,261 citations are potentiality clinical trials. Search strategy was elaborated combining headings with text word in three languages, adapting the interface of the LILACS. We will be working by locating clinical trials in LILACS for Cochrane Controlled Trials Database. This effort is being coordinated by the Brazilian Cochrane Center.
Cheng, Longjiu; Cai, Wensheng; Shao, Xueguang
2005-03-01
An energy-based perturbation and a new idea of taboo strategy are proposed for structural optimization and applied in a benchmark problem, i.e., the optimization of Lennard-Jones (LJ) clusters. It is proved that the energy-based perturbation is much better than the traditional random perturbation both in convergence speed and searching ability when it is combined with a simple greedy method. By tabooing the most wide-spread funnel instead of the visited solutions, the hit rate of other funnels can be significantly improved. Global minima of (LJ) clusters up to 200 atoms are found with high efficiency.
Guaraldi, Federica; Parasiliti-Caprino, Mirko; Goggi, Riccardo; Beccuti, Guglielmo; Grottoli, Silvia; Arvat, Emanuela; Ghizzoni, Lucia; Ghigo, Ezio; Giordano, Roberta; Gori, Davide
2014-12-01
The exponential growth of scientific literature available through electronic databases (namely PubMed) has increased the chance of finding interesting articles. At the same time, search has become more complicated, time consuming, and at risk of missing important information. Therefore, optimized strategies have to be adopted to maximize searching impact. The aim of this study was to formulate efficient strings to search PubMed for etiologic associations between adrenal disorders (ADs) and other conditions. A comprehensive list of terms identifying endogenous conditions primarily affecting adrenals was compiled. An ad hoc analysis was performed to find the best way to express each term in order to find the highest number of potentially pertinent articles in PubMed. A predefined number of retrieved abstracts were read to assess their association with ADs' etiology. A more sensitive (providing the largest literature coverage) and a more specific (including only those terms retrieving >40 % of potentially pertinent articles) string were formulated. Various researches were performed to assess strings' ability to identify articles of interest in comparison with non-optimized literature searches. We formulated optimized, ready applicable tools for the identification of the literature assessing etiologic associations in the field of ADs using PubMed, and demonstrated the advantages deriving from their application. Detailed description of the methodological process is also provided, so that this work can easily be translated to other fields of practice.
Directory of Open Access Journals (Sweden)
Tinggui Chen
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.
Sonnemans, J.H.
1998-01-01
Two experiments are designed to examine the strategies people use in search behavior. In the first experiment, an electronic information board is used to register on which aspects of the situation subjects focus their attention and after that subjects also submit a formal strategy. Although efficien
Evolution Strategies in Optimization Problems
Cruz, Pedro A F
2007-01-01
Evolution Strategies are inspired in biology and part of a larger research field known as Evolutionary Algorithms. Those strategies perform a random search in the space of admissible functions, aiming to optimize some given objective function. We show that simple evolution strategies are a useful tool in optimal control, permitting to obtain, in an efficient way, good approximations to the solutions of some recent and challenging optimal control problems.
Davis, Harold
2006-01-01
SEO--short for Search Engine Optimization--is the art, craft, and science of driving web traffic to web sites. Web traffic is food, drink, and oxygen--in short, life itself--to any web-based business. Whether your web site depends on broad, general traffic, or high-quality, targeted traffic, this PDF has the tools and information you need to draw more traffic to your site. You'll learn how to effectively use PageRank (and Google itself); how to get listed, get links, and get syndicated; and much more. The field of SEO is expanding into all the possible ways of promoting web traffic. This
Coward, D M; Sutton, P J; Howell, E J; Regimbau, T; Laas-Bourez, M; Klotz, A; Boer, M; Branchesi, M
2011-01-01
Observations of an optical source coincident with gravitational wave emission detected from a binary neutron star coalescence will improve the confidence of detection, provide host galaxy localisation, and test models for the progenitors of short gamma ray bursts. We employ optical observations of three short gamma ray bursts, 050724, 050709, 051221, to estimate the detection rate of a coordinated optical and gravitational wave search of neutron star mergers. Model R-band optical afterglow light curves of these bursts that include a jet-break are extrapolated for these sources at the sensitivity horizon of an Advanced LIGO/Virgo network. Using optical sensitivity limits of three telescopes, namely TAROT (m=18), Zadko (m=21) and an (8-10) meter class telescope (m=26), we approximate detection rates and cadence times for imaging. We find a median coincident detection rate of 4 yr^{-1} for the three bursts. GRB 050724 like bursts, with wide opening jet angles, offer the most optimistic rate of 13 coincident dete...
Optimal GENCO bidding strategy
Gao, Feng
Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed
Optimization Strategy of Search Engine in E-commerce Website%电子商务网站的搜索引擎优化策略
Institute of Scientific and Technical Information of China (English)
李亮
2011-01-01
搜索引擎优化,以其突出的优势正在渗透到网络的各个方面,成为网络营销的核心.本文分析了企业网站在搜索引擎优化上存在的问题,在此基础上提出电子商务网站的优化策略.%Search engine optimization with its outstanding advantages is penetrating into all aspects of the network, and become the core of network marketing. This paper analyzes the problems in the search engine optimization of corporate website, and proposes the optimization strategy of e-commerce website on this basis.
Directory of Open Access Journals (Sweden)
Chengfen Zhang
2015-01-01
Full Text Available Dry-type air-core reactor is now widely applied in electrical power distribution systems, for which the optimization design is a crucial issue. In the optimization design problem of dry-type air-core reactor, the objectives of minimizing the production cost and minimizing the operation cost are both important. In this paper, a multiobjective optimal model is established considering simultaneously the two objectives of minimizing the production cost and minimizing the operation cost. To solve the multi-objective optimization problem, a memetic evolutionary algorithm is proposed, which combines elitist nondominated sorting genetic algorithm version II (NSGA-II with a local search strategy based on the covariance matrix adaptation evolution strategy (CMA-ES. NSGA-II can provide decision maker with flexible choices among the different trade-off solutions, while the local-search strategy, which is applied to nondominated individuals randomly selected from the current population in a given generation and quantity, can accelerate the convergence speed. Furthermore, another modification is that an external archive is set in the proposed algorithm for increasing the evolutionary efficiency. The proposed algorithm is tested on a dry-type air-core reactor made of rectangular cross-section litz-wire. Simulation results show that the proposed algorithm has high efficiency and it converges to a better Pareto front.
An improved group search optimizer for mechanical design optimization problems
Institute of Scientific and Technical Information of China (English)
Hai Shen; Yunlong Zhu; Ben Niu; Q.H. Wu
2009-01-01
This paper presents an improved group search optimizer (iGSO) for solving mechanical design optimization problems.In the pro-posed algorithm,subpopulations and a co-operation evolutionary strategy were adopted to improve the global search capability and convergence performance.The iGSO is evaluated on two optimization problems of classical mechanical design:spring and pressure vessel.The experimental results are analyzed in comparison with those reported in the literatures.The results show that iGSO has much better convergence performance and is easier to implement in comparison with other existing evolutionary algorithms.
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Tales from the Field: Search Strategies Applied in Web Searching
Directory of Open Access Journals (Sweden)
Soohyung Joo
2010-08-01
Full Text Available In their web search processes users apply multiple types of search strategies, which consist of different search tactics. This paper identifies eight types of information search strategies with associated cases based on sequences of search tactics during the information search process. Thirty-one participants representing the general public were recruited for this study. Search logs and verbal protocols offered rich data for the identification of different types of search strategies. Based on the findings, the authors further discuss how to enhance web-based information retrieval (IR systems to support each type of search strategy.
Optimal Labour Taxation and Search
Boone, J.; Bovenberg, A.L.
2000-01-01
This paper explores the optimal role of the tax system in alleviating labour-market imperfections, raising revenue, and correcting the income distribution. For this purpose, the standard search model of the labour market is extended by introducing non-linear vacancy costs due to scarce entrepreneuri
Mullin, Gerard E
2010-12-01
Since the beginning of time, we have been searching for diets that satisfy our palates while simultaneously optimizing health and well-being. Every year, there are hundreds of new diet books on the market that make a wide range of promises but rarely deliver. Unfortunately, consumers are gullible and believe much of the marketing hype because they are desperately seeking ways to maximize their health. As a result, they continue to purchase these diet books, sending many of them all the way to the bestseller list. Because many of these meal plans are not sustainable and are questionable in their approaches, the consumer is ultimately left to continue searching, only able to choose from the newest "fad" promoted by publicists rather than being grounded in science. Thus, the search for the optimal diet continues to be the "holy grail" for many of us today, presenting a challenge for nutritionists and practitioners to provide sound advice to consumers.
A cuckoo search algorithm for multimodal optimization.
Cuevas, Erik; Reyna-Orta, Adolfo
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.
University Students' Online Information Searching Strategies in Different Search Contexts
Tsai, Meng-Jung; Liang, Jyh-Chong; Hou, Huei-Tse; Tsai, Chin-Chung
2012-01-01
This study investigates the role of search context played in university students' online information searching strategies. A total of 304 university students in Taiwan were surveyed with questionnaires in which two search contexts were defined as searching for learning, and searching for daily life information. Students' online search strategies…
Search engine optimization an hour a day
Grappone, Jennifer
2011-01-01
The third edition of the bestselling guide to do-it-yourself SEO. Getting seen on the first page of search engine result pages is crucial for businesses and online marketers. Search engine optimization helps improve Web site rankings, and it is often complex and confusing. This task-based, hands-on guide covers the concepts and trends and then lays out a day-by-day strategy for developing, managing, and measuring a successful SEO plan. With tools you can download and case histories to illustrate key points, it's the perfect solution for busy marketers, business owners, and others whose jobs in
Optimal Strategy in Basketball
Skinner, Brian
2015-01-01
This book chapter reviews some of the major principles associated with optimal strategy in basketball. In particular, we consider the principles of allocative efficiency (optimal allocation of shots between offensive options), dynamic efficiency (optimal shot selection in the face of pressure from the shot clock), and the risk/reward tradeoff (strategic manipulation of outcome variance). For each principle, we provide a simple example of a strategic problem and show how it can be described analytically. We then review general analytical results and provide an overview of existing statistical studies. A number of open challenges in basketball analysis are highlighted.
Optimal Fungal Space Searching Algorithms.
Asenova, Elitsa; Lin, Hsin-Yu; Fu, Eileen; Nicolau, Dan V; Nicolau, Dan V
2016-10-01
Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.
Research on the Optimization Strategy of Search Engine Ads%搜索引擎广告优化策略研究
Institute of Scientific and Technical Information of China (English)
马玉红
2015-01-01
互联网搜索引擎是最广泛使用的工具，它是互联网用户寻找信息和资源的主要途径。搜索引擎广告从产生之初，由于其低投资，高回报而受到关注，与传统媒体相比，他们的投资回报率非常有吸引力。许多公司都在从事搜索引擎广告的投放，但并不是所有的公司都取得了令人满意的结果，有些取得了很高的回报率，但一些收益却不高。其部分原因是一些企业虽然是有投放策略，但是缺少合理的计划。文章研究搜索引擎广告优化的目的是希望能够提出适合企业的搜索引擎广告投放的策略。%Internet search engine is the most widely used tool, it is the Internet users search for information and resources of the main way. Search engine ads from the beginning, due to their low investment, high returns and attention, compared with the traditional media, their return on investment is very attractive. Many companies are engaged in search engine ads, but not all companies have achieved satisfactory results, some achieved a high rate of return, but some of the proceeds are not high. The reason is that some of the enterprise although there is a strategy, but the lack of reasonable plan. The purpose of this paper is to study the advertising optimization of search engine, and it is hoped that it can be put forward for the search engine advertising strategy.
Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization
Directory of Open Access Journals (Sweden)
Qingyang Xu
2014-01-01
Full Text Available The vessel collision accidents cause a great loss of lives and property. In order to reduce the human fault and greatly improve the safety of marine traffic, collision avoidance strategy optimization is proposed to achieve this. In the paper, a multiobjective optimization algorithm NSGA-II is adopted to search for the optimal collision avoidance strategy considering the safety as well as economy elements of collision avoidance. Ship domain and Arena are used to evaluate the collision risk in the simulation. Based on the optimization, an optimal rudder angle is recommended to navigator for collision avoidance. In the simulation example, a crossing encounter situation is simulated, and the NSGA-II searches for the optimal collision avoidance operation under the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS. The simulation studies exhibit the validity of the method.
Cuckoo search for business optimization applications
Yang, Xin-She; Deb, Suash; Karamanoglu, Mehmet; He, Xingshi
2012-01-01
Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support ve...
Optimality criteria solution strategies in multiple constraint design optimization
Levy, R.; Parzynski, W.
1981-01-01
Procedures and solution strategies are described to solve the conventional structural optimization problem using the Lagrange multiplier technique. The multipliers, obtained through solution of an auxiliary nonlinear optimization problem, lead to optimality criteria to determine the design variables. It is shown that this procedure is essentially equivalent to an alternative formulation using a dual method Lagrangian function objective. Although mathematical formulations are straight-forward, successful applications and computational efficiency depend upon execution procedure strategies. Strategies examined, with application examples, include selection of active constraints, move limits, line search procedures, and side constraint boundaries.
Adaptive backtracking search optimization algorithm with pattern search for numerical optimization
Institute of Scientific and Technical Information of China (English)
Shu Wang; Xinyu Da; Mudong Li; Tong Han
2016-01-01
Thebacktracking search optimization algorithm (BSA) is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capa-bility to find global optimal solutions. However, the algorithm is stil insufficient in balancing the exploration and the exploita-tion. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts: the BSA is used for the exploration phase and the modified pattern search method completes the ex-ploitation phase. In particular, a simple but effective strategy of adapting one of BSA’s important control parameters is intro-duced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Com-putation 2014 (IEEE CEC2014) over six widely-used bench-marks and 22 real-parameter single objective numerical opti-mization benchmarks in IEEE CEC2014. The results of ex-periment and statistical analysis demonstrate the effective-ness and efficiency of the proposed algorithm.
Optimization of web pages for search engines
Harej, Anže
2011-01-01
The thesis describes the most important elements of a Web Page and outside factors that affect Search Engine Optimization. The basic structure of a Web page, structure and functionality of a modern Search Engine is described at the beginning. The first section deals with the start of Search Engine Optimization, including planning, analysis of web space and the selection of the most important keywords for which the site will be optimized. The next section Web Page Optimization describes...
SEARCH ENGINE OPTIMIZATION: A CASE STUDY OF BENEFITS OF IT’S APPLICATION IN WEBSITES
National Research Council Canada - National Science Library
Christian Luís Ramos; Camilla Zanchin Caramigo1; Vinícius Camargo Andrade; Gustavo Kimura Montanha; Fernando Henrique Campos
2016-01-01
.... The Search Engine Optimization (SEO) is a set of strategies and techniques that are aimed at improving the position where a website is displayed in the results list of search engines in the search of a particular subject...
Search strategies of Wikipedia readers
2017-01-01
The quest for information is one of the most common activity of human beings. Despite the the impressive progress of search engines, not to miss the needed piece of information could be still very tough, as well as to acquire specific competences and knowledge by shaping and following the proper learning paths. Indeed, the need to find sensible paths in information networks is one of the biggest challenges of our societies and, to effectively address it, it is important to investigate the strategies adopted by human users to cope with the cognitive bottleneck of finding their way in a growing sea of information. Here we focus on the case of Wikipedia and investigate a recently released dataset about users’ click on the English Wikipedia, namely the English Wikipedia Clickstream. We perform a semantically charged analysis to uncover the general patterns followed by information seekers in the multi-dimensional space of Wikipedia topics/categories. We discover the existence of well defined strategies in which users tend to start from very general, i.e., semantically broad, pages and progressively narrow down the scope of their navigation, while keeping a growing semantic coherence. This is unlike strategies associated to tasks with predefined search goals, namely the case of the Wikispeedia game. In this case users first move from the ‘particular’ to the ‘universal’ before focusing down again to the required target. The clear picture offered here represents a very important stepping stone towards a better design of information networks and recommendation strategies, as well as the construction of radically new learning paths. PMID:28152030
Armentum: a hybrid direct search optimization methodology
Briones, Francisco Zorrilla
2016-07-01
Design of experiments (DOE) offers a great deal of benefits to any manufacturing organization, such as characterization of variables and sets the path for the optimization of the levels of these variables (settings) trough the Response surface methodology, leading to process capability improvement, efficiency increase, cost reduction. Unfortunately, the use of these methodologies is very limited due to various situations. Some of these situations involve the investment on production time, materials, personnel, equipment; most of organizations are not willing to invest in these resources or are not capable because of production demands, besides the fact that they will produce non-conformant product (scrap) during the process of experimentation. Other methodologies, in the form of algorithms, may be used to optimize a process. Known as direct search methods, these algorithms search for an optimum on an unknown function, trough the search of the best combination of the levels on the variables considered in the analysis. These methods have a very different application strategy, they search on the best combination of parameters, during the normal production run, calculating the change in the input variables and evaluating the results in small steps until an optimum is reached. These algorithms are very sensible to internal noise (variation of the input variables), among other disadvantages. In this paper it is made a comparison between the classical experimental design and one of these direct search methods, developed by Nelder and Mead (1965), known as the Nelder Mead simplex (NMS), trying to overcome the disadvantages and maximize the advantages of both approaches, trough a proposed combination of the two methodologies.
Automatic Planning of External Search Engine Optimization
Directory of Open Access Journals (Sweden)
Vita Jasevičiūtė
2015-07-01
Full Text Available This paper describes an investigation of the external search engine optimization (SEO action planning tool, dedicated to automatically extract a small set of most important keywords for each month during whole year period. The keywords in the set are extracted accordingly to external measured parameters, such as average number of searches during the year and for every month individually. Additionally the position of the optimized web site for each keyword is taken into account. The generated optimization plan is similar to the optimization plans prepared manually by the SEO professionals and can be successfully used as a support tool for web site search engine optimization.
Solving the constrained shortest path problem using random search strategy
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
In this paper, we propose an improved walk search strategy to solve the constrained shortest path problem. The proposed search strategy is a local search algorithm which explores a network by walker navigating through the network. In order to analyze and evaluate the proposed search strategy, we present the results of three computational studies in which the proposed search algorithm is tested. Moreover, we compare the proposed algorithm with the ant colony algorithm and k shortest paths algorithm. The analysis and comparison results demonstrate that the proposed algorithm is an effective tool for solving the constrained shortest path problem. It can not only be used to solve the optimization problem on a larger network, but also is superior to the ant colony algorithm in terms of the solution time and optimal paths.
Perron vector optimization applied to search engines
Fercoq, Olivier
2011-01-01
In the last years, Google's PageRank optimization problems have been extensively studied. In that case, the ranking is given by the invariant measure of a stochastic matrix. In this paper, we consider the more general situation in which the ranking is determined by the Perron eigenvector of a nonnegative, but not necessarily stochastic, matrix, in order to cover Kleinberg's HITS algorithm. We also give some results for Tomlin's HOTS algorithm. The problem consists then in finding an optimal outlink strategy subject to design constraints and for a given search engine. We study the relaxed versions of these problems, which means that we should accept weighted hyperlinks. We provide an efficient algorithm for the computation of the matrix of partial derivatives of the criterion, that uses the low rank property of this matrix. We give a scalable algorithm that couples gradient and power iterations and gives a local minimum of the Perron vector optimization problem. We prove convergence by considering it as an app...
Quantitative analysis of saccadic search strategy
Over, E.A.B.
2007-01-01
This thesis deals with the quantitative analysis of saccadic search strategy. The goal of the research presented was twofold: 1) to quantify overall characteristics of fixation location and saccade direction, and 2) to identify search strategies, with the use of a quantitative description of eye mov
Quantitative analysis of saccadic search strategy
Over, E.A.B.
2007-01-01
This thesis deals with the quantitative analysis of saccadic search strategy. The goal of the research presented was twofold: 1) to quantify overall characteristics of fixation location and saccade direction, and 2) to identify search strategies, with the use of a quantitative description of eye
Collaborative Search Strategies for Green Innovation
DEFF Research Database (Denmark)
Ørding Olsen, Anders; Sofka, Wolfgang; Grimpe, Christoph
Recent innovation and strategy research emphasizes the importance of firm’s search for external knowledge to improve innovation performance. We focus on such search strategies within the domain of sustainable innovation in which problems are inherently complex and the relevant knowledge is widely...
An efficient cuckoo search algorithm for numerical function optimization
Ong, Pauline; Zainuddin, Zarita
2013-04-01
Cuckoo search algorithm which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos has demonstrated its superiority in obtaining the global solution for numerical optimization problems. However, the involvement of fixed step approach in its exploration and exploitation behavior might slow down the search process considerably. In this regards, an improved cuckoo search algorithm with adaptive step size adjustment is introduced and its feasibility on a variety of benchmarks is validated. The obtained results show that the proposed scheme outperforms the standard cuckoo search algorithm in terms of convergence characteristic while preserving the fascinating features of the original method.
HOPSPACK: Hybrid Optimization Parallel Search Package.
Energy Technology Data Exchange (ETDEWEB)
Gray, Genetha Anne.; Kolda, Tamara G.; Griffin, Joshua; Taddy, Matt; Martinez-Canales, Monica L.
2008-12-01
In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel SearchPackage), a new software platform which facilitates combining multiple optimization routines into asingle, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The frameworkis designed such that existing optimization source code can be easily incorporated with minimalcode modification. By maintaining the integrity of each individual solver, the strengths and codesophistication of the original optimization package are retained and exploited.4
Tetris Agent Optimization Using Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
Victor II M. Romero
2011-01-01
Full Text Available Harmony Search (HS algorithm, a relatively recent meta-heuristic optimization algorithm based on the music improvisation process of musicians, is applied to one of today's most appealing problems in the field of Computer Science, Tetris. Harmony Search algorithm was used as the underlying optimization algorithm to facilitate the learning process of an intelligent agent whose objective is to play the game of Tetris in the most optimal way possible, that is, to clear as many rows as possible. The application of Harmony Search algorithm to Tetris is a good illustration of the involvement of optimization process to decision-making problems. Experiment results show that Harmony Search algorithm found the best possible solution for the problem at hand given a random sequence of Tetrominos.
An introduction to harmony search optimization method
Wang, Xiaolei; Zenger, Kai
2014-01-01
This brief provides a detailed introduction, discussion and bibliographic review of the nature1-inspired optimization algorithm called Harmony Search. It uses a large number of simulation results to demonstrate the advantages of Harmony Search and its variants and also their drawbacks. The authors show how weaknesses can be amended by hybridization with other optimization methods. The Harmony Search Method with Applications will be of value to researchers in computational intelligence in demonstrating the state of the art of research on an algorithm of current interest. It also helps researche
Directory of Open Access Journals (Sweden)
Marcy L. Brown
2006-06-01
Full Text Available Objective – To develop and test search strategies for retrieving clinically sound studies from the MEDLINE database on the prevention or treatment of health disorders. Design – Analytical survey. Subjects – The data sources were articles about treatment studies selected from 161 journal titles indexed for MEDLINE in the year 2000. Setting – MEDLINE database searches performed at the Health Information Research Unit, McMaster University, in Ontario, Canada. Methods – Researchers hand searched each issue of 161 journal titles indexed in MEDLINE in the year 2000 to find treatment studies. Journal content included internal medicine, family practice, nursing, and mental health titles. Selected studies met the following criteria: randomisation of subjects, outcome assessment for at least 80% of who entered the study, and an analysis consistent with study design. Of 49,028 potential articles, 6,568 were identified as being treatment or prevention related, and 1,587 met the evaluation criteria. The study authors then created search strategies designed to retrieve articles in MEDLINE that met the same criteria, while excluding articles that did not. They compiled a list of 4,862 unique terms related to study criteria, and tested them using the Ovid Technologies search platform. Overall, 18,404 multiple‐term search strategies were tested. Single terms with specificity greater than 75% and sensitivity greater than 25% were combined into strategies with two or more terms. These multiple term strategies were tested if they yielded sensitivity or accuracy greater than 75% and specificity of at least 50%. Main Results – Of the 4,862 unique terms, 3,807 retrieved citations from MEDLINE that researchers used to assess sensitivity, specificity, precision, and accuracy. The single term that yielded the best accuracy while keeping sensitivity greater than 50% was ‘randomized controlled trial.pt.’. The single term that yielded the best precision
Collaborative Search Strategies for Green Innovation
DEFF Research Database (Denmark)
Ørding Olsen, Anders; Sofka, Wolfgang; Grimpe, Christoph
Recent innovation and strategy research emphasizes the importance of firm’s search for external knowledge to improve innovation performance. We focus on such search strategies within the domain of sustainable innovation in which problems are inherently complex and the relevant knowledge is widely...... dispersed. Hence, firms need to collaborate. We shed new light on collaborative search strategies led by firms in general and for solving environmental problems in particular. Both topics are largely absent in the extant open innovation literature. Using data from the European Seventh Framework Program...... for Research and Technological Development (FP7), our results indicate that the problem-solving potential of a search strategy increases with the diversity of existing knowledge of the partners in a consortium and with the experience of the partners involved. Moreover, we identify a substantial negative effect...
"Compressing liquid": an efficient global minima search strategy for clusters.
Zhou, R L; Zhao, L Y; Pan, B C
2009-07-21
In this paper we present a new global search strategy named as "compressing liquid" for atomic clusters. In this strategy, a random fragment of liquid structure is adopted as a starting geometry, followed by iterative operations of "compressing" and Monte Carlo adjustment of the atom positions plus structural optimization. It exhibits fair efficiency when it is applied to seeking the global minima of Lennard-Jones clusters. We also employed it to search the low-lying candidates of medium silicon clusters Si(n)(n=40-60), where the global search is absent. We found the best candidates for most sizes. More importantly, we obtained non-fullerene-based structures for some sized clusters, which were not found from the endohedral-fullerene strategy. These results indicate that the "compressing-liquid" method is highly efficient for global minima search of clusters.
Movement Strategies in a Haptic Search Task
van Polanen, V.; Bergmann Tiest, W.M.; Kappers, A.M.L.
2011-01-01
Movement strategies were investigated in a haptic search task where participants indicated whether a target was present among a varying number of items. Hand movements were classified according to two criteria into three movement types. Results indicated that an easy search was performed with a para
Cuckoo search optimization for linear antenna arrays synthesis
Directory of Open Access Journals (Sweden)
Ahmed Haffane
2013-01-01
Full Text Available A recently developed metaheuristic optimization algorithm, the Cuckoo search algorithm, is used in this paper for the synthesis of symmetric uniformly spaced linear microstrip antennas array. Cuckoo search is based on the breeding strategy of Cuckoos augmented by a Levy flight behaviour found in the foraging habits of other species. This metaheuristic is tested on amplitude only pattern synthesis and amplitude and phase pattern synthesis. In both case, the objective, is to determinate the optimal excitations element that produce a synthesized radiation pattern within given bounds specified by a pattern mask.
Optimal Search Mechanism Analysis of Light Ray Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Jihong SHEN; Jialian LI; Bin WEI
2012-01-01
Based on Fermat's principle and the automatic optimization mechanism in the propagation process of light,an optimal searching algorithm named light ray optimization is presented,where the laws of refraction and reflection of light rays are integrated into searching process of optimization.In this algorithm,coordinate space is assumed to be the space that is full of media with different refractivities,then the space is divided by grids,and finally the searching path is assumed to be the propagation path of light rays.With the law of refraction,the search direction is deflected to the direction that makes the value of objective function decrease.With the law of reflection,the search direction is changed,which makes the search continue when it cannot keep going with refraction.Only the function values of objective problems are used and there is no artificial rule in light ray optimization,so it is simple and easy to realize.Theoretical analysis and the results of numerical experiments show that the algorithm is feasible and effective.
Competing intelligent search agents in global optimization
Energy Technology Data Exchange (ETDEWEB)
Streltsov, S.; Vakili, P. [Boston Univ., MA (United States); Muchnik, I. [Rutgers Univ., Piscataway, NJ (United States)
1996-12-31
In this paper we present a new search methodology that we view as a development of intelligent agent approach to the analysis of complex system. The main idea is to consider search process as a competition mechanism between concurrent adaptive intelligent agents. Agents cooperate in achieving a common search goal and at the same time compete with each other for computational resources. We propose a statistical selection approach to resource allocation between agents that leads to simple and efficient on average index allocation policies. We use global optimization as the most general setting that encompasses many types of search problems, and show how proposed selection policies can be used to improve and combine various global optimization methods.
Optimal Cooperative Searching Using Purely Repulsive Interactions
Tani, Noriyuki P; Quint, David A; Gopinathan, Ajay
2013-01-01
Foraging, either solitarily or collectively, is a necessary behavior for survival that is demonstrated by many organisms. Foraging can be collectively optimized by utilizing communication between the organisms. Examples of such communication range from high level strategic foraging by animal groups to rudimentary signaling among unicellular organisms. Here we systematically study the simplest form of communication via long range repulsive interactions between two diffusing Brownian searchers on a one-dimensional lattice. We show that the mean first passage time for either of them to reach a fixed target depends non-monotonically on the range of the interaction and can be optimized for a repulsive range that is comparable to the average spacing between searchers. Our results suggest that even the most rudimentary form of collective searching does in fact lower the search time for the foragers suggesting robust mechanisms for search optimization in cellular communities
Near-Optimal Compressive Binary Search
Malloy, Matthew L.; Nowak, Robert D.
2012-01-01
We propose a simple modification to the recently proposed compressive binary search. The modification removes an unnecessary and suboptimal factor of log log n from the SNR requirement, making the procedure optimal (up to a small constant). Simulations show that the new procedure performs significantly better in practice as well. We also contrast this problem with the more well known problem of noisy binary search.
A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution
Directory of Open Access Journals (Sweden)
Lijin Wang
2015-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
Bobbert, Maarten F; Kistemaker, Dinant A; Vaz, Marco Aurélio; Ackermann, Marko
2016-08-01
The sit-to-stand task, which involves rising unassisted from sitting on a chair to standing, is important in daily life. Many people with muscle weakness, reduced range of motion or loading-related pain in a particular joint have difficulty performing the task. How should a person suffering from such impairment best perform the sit-to-stand task and, in the case of pain in a particular joint, with reduced loading of that joint? We developed a musculoskeletal model with reference parameter values based on properties of healthy strong subjects. The model's muscle stimulation-time input was optimized using direct collocation to find strategies that yielded successful sit-to-stand task performance with minimum 'control effort' for the reference set and modified sets of parameter values, and with constraints on tibiofemoral compression force. The sit-to-stand task could be performed successfully and realistically by the reference model, by a model with isometric knee extensor forces reduced to 40% of reference, by a model with isometric forces of all muscles reduced to 45% of reference, and by the reference model with the tibiofemoral compression force constrained during optimization to 65% of the peak value in the reference condition. The strategies found by the model in conditions other than reference could be interpreted well on the basis of cost function and task biomechanics. The question remains whether it is feasible to teach patients with musculoskeletal impairments or joint pain to perform the sit-to-stand task according to strategies that are optimal according to the simulation model. Copyright © 2016 Elsevier Ltd. All rights reserved.
Optimal Strategy and Business Models
DEFF Research Database (Denmark)
Johnson, Peter; Foss, Nicolai Juul
2016-01-01
, it is possible to formalize useful notions of a business model, resources, and competitive advantage. The business model that underpins strategy may be seen as a set of constraints on resources that can be interpreted as controls in optimal control theory. Strategy then might be considered to be the control......This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonian...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....
Directory of Open Access Journals (Sweden)
Clifford Tammy J
2006-02-01
Full Text Available Abstract Background Most electronic search efforts directed at identifying primary studies for inclusion in systematic reviews rely on the optimal Boolean search features of search interfaces such as DIALOG® and Ovid™. Our objective is to test the ability of an Ultraseek® search engine to rank MEDLINE® records of the included studies of Cochrane reviews within the top half of all the records retrieved by the Boolean MEDLINE search used by the reviewers. Methods Collections were created using the MEDLINE bibliographic records of included and excluded studies listed in the review and all records retrieved by the MEDLINE search. Records were converted to individual HTML files. Collections of records were indexed and searched through a statistical search engine, Ultraseek, using review-specific search terms. Our data sources, systematic reviews published in the Cochrane library, were included if they reported using at least one phase of the Cochrane Highly Sensitive Search Strategy (HSSS, provided citations for both included and excluded studies and conducted a meta-analysis using a binary outcome measure. Reviews were selected if they yielded between 1000–6000 records when the MEDLINE search strategy was replicated. Results Nine Cochrane reviews were included. Included studies within the Cochrane reviews were found within the first 500 retrieved studies more often than would be expected by chance. Across all reviews, recall of included studies into the top 500 was 0.70. There was no statistically significant difference in ranking when comparing included studies with just the subset of excluded studies listed as excluded in the published review. Conclusion The relevance ranking provided by the search engine was better than expected by chance and shows promise for the preliminary evaluation of large results from Boolean searches. A statistical search engine does not appear to be able to make fine discriminations concerning the relevance of
Optimization of semiconductor quantum devices by evolutionary search.
Goldoni, G; Rossi, F
2000-07-15
A novel simulation strategy is proposed for searching for semiconductor quantum devices that are optimized with respect to required performances. Based on evolutionary programming, a technique that implements the paradigm of genetic algorithms in more-complex data structures than strings of bits, the proposed algorithm is able to deal with quantum devices with preset nontrivial constraints (e.g., transition energies, geometric requirements). Therefore our approach allows for automatic design, thus avoiding costly by-hand optimizations. We demonstrate the advantages of the proposed algorithm through a relevant and nontrivial application, the optimization of a second-harmonic-generation device working in resonance conditions.
Search Engine Marketing (SEM: Financial & Competitive Advantages of an Effective Hotel SEM Strategy
Directory of Open Access Journals (Sweden)
Leora Halpern Lanz
2015-05-01
Full Text Available Search Engine Marketing and Optimization (SEO, SEM are keystones of a hotels marketing strategy, in fact research shows that 90% of travelers start their vacation planning with a Google search. Learn five strategies that can enhance a hotels SEO and SEM strategies to boost bookings.
Optimal directed searches for continuous gravitational waves
Ming, Jing; Papa, Maria Alessandra; Aulbert, Carsten; Fehrmann, Henning
2015-01-01
Wide parameter space searches for long lived continuous gravitational wave signals are computationally limited. It is therefore critically important that available computational resources are used rationally. In this paper we consider directed searches, i.e. targets for which the sky position is known accurately but the frequency and spindown parameters are completely unknown. Given a list of such potential astrophysical targets, we therefore need to prioritize. On which target(s) should we spend scarce computing resources? What parameter space region in frequency and spindown should we search? Finally, what is the optimal search set-up that we should use? In this paper we present a general framework that allows to solve all three of these problems. This framework is based on maximizing the probability of making a detection subject to a constraint on the maximum available computational cost. We illustrate the method for a simplified problem.
Optimal strategies for throwing accurately.
Venkadesan, M; Mahadevan, L
2017-04-01
The accuracy of throwing in games and sports is governed by how errors in planning and initial conditions are propagated by the dynamics of the projectile. In the simplest setting, the projectile path is typically described by a deterministic parabolic trajectory which has the potential to amplify noisy launch conditions. By analysing how parabolic trajectories propagate errors, we show how to devise optimal strategies for a throwing task demanding accuracy. Our calculations explain observed speed-accuracy trade-offs, preferred throwing style of overarm versus underarm, and strategies for games such as dart throwing, despite having left out most biological complexities. As our criteria for optimal performance depend on the target location, shape and the level of uncertainty in planning, they also naturally suggest an iterative scheme to learn throwing strategies by trial and error.
Ant colony search algorithm for optimal reactive power optimization
Directory of Open Access Journals (Sweden)
Lenin K.
2006-01-01
Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.
Strategy for early SUSY searches at ATLAS
Yamamoto, Shimpei
2008-01-01
The CERN Large Hadron Collider (LHC) is scheduled to commence operation in 2008 and inclusive searches for supersymmetry (SUSY) will be one of our primary tasks in the first days of LHC operation. It is certain that the final state of multijets plus missing transverse energy will provide a superior performance in SUSY searches. As yet, well-considered strategies for the understanding of instrumental effects of detectors and the realistic estimations of the Standard Model (SM) backgrounds would not be clear: they are urgent issues for the coming data. We describe the strategy for early SUSY searches at the ATLAS experiment using the fist data corresponding to the integrated luminosity up to 1fb^-1, which comprises many progresses in the data-driven technique for the SM background estimations.
Strategy for early SUSY searches at ATLAS
Yamamoto, S
2007-01-01
The CERN Large Hadron Collider (LHC) is scheduled to commence operation in 2008 and inclusive searches for supersymmetry (SUSY) will be one of our primary tasks in the first days of LHC operation. It is certain that the final state of âﾜmultijets + missing transverse energyâ will provide a superior performance in SUSY searches. As yet, well-considered strategies for the understanding of instrumental effects of detectors and the realistic estimations of the Standard Model (SM) backgrounds would not be clear: they are urgent issues for the coming data. We describe the strategy for early SUSY searches at the ATLAS experiment using the fist data corresponding to the integrated luminosity up to 1fbâ1, which comprises many progresses in the data-driven technique for the SM background estimations.
Strategy for early SUSY searches at ATLAS
Yamamoto, S
2007-01-01
The CERN Large Hadron Collider (LHC) is scheduled to commence operation in 2008 and inclusive searches for supersymmetry (SUSY) will be one of our primary tasks in the first days of LHC operation. It is certain that the final state of multijets plus missing transverse energy will provide a superior performance in SUSY searches. As yet, well-considered strategies for the understanding of instrumental effects of detectors and the realistic estimations of the Standard Model (SM) backgrounds would not be clear: they are urgent issues for the coming data. We describe the strategy for early SUSY searches at the ATLAS experiment using the fist data corresponding to the integrated luminosity up to 1fb^-1, which includes many progresses in the data-driven technique for the SM background estimations.
Asynchronous parallel pattern search for nonlinear optimization
Energy Technology Data Exchange (ETDEWEB)
P. D. Hough; T. G. Kolda; V. J. Torczon
2000-01-01
Parallel pattern search (PPS) can be quite useful for engineering optimization problems characterized by a small number of variables (say 10--50) and by expensive objective function evaluations such as complex simulations that take from minutes to hours to run. However, PPS, which was originally designed for execution on homogeneous and tightly-coupled parallel machine, is not well suited to the more heterogeneous, loosely-coupled, and even fault-prone parallel systems available today. Specifically, PPS is hindered by synchronization penalties and cannot recover in the event of a failure. The authors introduce a new asynchronous and fault tolerant parallel pattern search (AAPS) method and demonstrate its effectiveness on both simple test problems as well as some engineering optimization problems
Estimation of optimal feeding strategies for fed-batch bioprocesses.
Franco-Lara, Ezequiel; Weuster-Botz, Dirk
2005-07-01
A generic methodology for feeding strategy optimization is presented. This approach uses a genetic algorithm to search for optimal feeding profiles represented by means of artificial neural networks (ANN). Exemplified on a fed-batch hybridoma cell cultivation, the approach has proven to be able to cope with complex optimization tasks handling intricate constraints and objective functions. Furthermore, the performance of the method is compared with other previously reported standard techniques like: (1) optimal control theory, (2) first order conjugate gradient, (3) dynamical programming, (4) extended evolutionary strategies. The methodology presents no restrictions concerning the number or complexity of the state variables and therefore constitutes a remarkable alternative for process development and optimization.
Taking It to the Top: A Lesson in Search Engine Optimization
Frydenberg, Mark; Miko, John S.
2011-01-01
Search engine optimization (SEO), the promoting of a Web site so it achieves optimal position with a search engine's rankings, is an important strategy for organizations and individuals in order to promote their brands online. Techniques for achieving SEO are relevant to students of marketing, computing, media arts, and other disciplines, and many…
Mixed integer evolution strategies for parameter optimization.
Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C
2013-01-01
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.
Institute of Scientific and Technical Information of China (English)
景文博; 徐皓; 王晓曼; 姜会林
2012-01-01
针对海上低对比度低信噪比的条件下,能够精确快速搜索空域目标,提出了一种快速精确的海空目标多尺度波峰最佳阈值自动搜索策略:采用金字塔波门搜索和多尺度波峰阈值法,逐级减小搜索区域并对搜索域内图像进行分割,然后根据目标特征进行概率统计分析,确定最优目标,实现海空目标的自动搜索.实验结果表明,该算法在序列图像中的搜索定位平均误差为0.413像素并全部识别成功,较OTSU算法的2.61像素和最大熵阈值算法的3.1像素的误差,精确度大大提升.整幅图像搜索时间优于21.34 ms,满足海空目标自动搜索的精度和实时性的要求.%To search spatial domain target precisely and quickly in the condition of low contrast and low SNR, a new fast and accurate automatic search strategy for sea-sky target by multi-scale peak optimal threshold algorithm was proposed. Using the method of pyramid wave gate search and multi-scale peak threshold, the search area was gradually reduced and image segmentation was accomplished in this search area. Then, the probability and statistics according to the characteristics of the target was analysed to determine the optimum target and achieve automatic search for sea梥ky target. Experimental results show that the average error of searching and locating is 0.413 pixel and all identified with this algorithm in the image sequences, compared with 2.61 pixel of OTSU algorithm and 3.1 pixel of the maximum entropy threshold algorithm, the accuracy rises obviously. The whole image search time is less than 21.34 ms, which meets the requirements of the precision and real-time for automatically search of the sea-sky target.
Particle swarm optimization based optimal bidding strategy in an ...
African Journals Online (AJOL)
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Particle swarm optimization based optimal bidding strategy in an open ... relaxation-based approach for strategic bidding in England-Wales pool type electricity market has ... presents the mathematical formulation of optimal bidding problem.
Food Searching Strategy of Amoeboid Cells by Starvation Induced Run Length Extension
Van Haastert, Peter J. M.; Bosgraaf, Leonard
2009-01-01
Food searching strategies of animals are key to their success in heterogeneous environments. The optimal search strategy may include specialized random walks such as Levy walks with heavy power-law tail distributions, or persistent walks with preferred movement in a similar direction. We have invest
Food Searching Strategy of Amoeboid Cells by Starvation Induced Run Length Extension
Van Haastert, Peter J. M.; Bosgraaf, Leonard
2009-01-01
Food searching strategies of animals are key to their success in heterogeneous environments. The optimal search strategy may include specialized random walks such as Levy walks with heavy power-law tail distributions, or persistent walks with preferred movement in a similar direction. We have
Optimal intervention strategies for tuberculosis
Bowong, Samuel; Aziz Alaoui, A. M.
2013-06-01
This paper deals with the problem of optimal control of a deterministic model of tuberculosis (abbreviated as TB for tubercle bacillus). We first present and analyze an uncontrolled tuberculosis model which incorporates the essential biological and epidemiological features of the disease. The model is shown to exhibit the phenomenon of backward bifurcation, where a stable disease-free equilibrium co-exists with one or more stable endemic equilibria when the associated basic reproduction number is less than the unity. Based on this continuous model, the tuberculosis control is formulated and solved as an optimal control problem, indicating how control terms on the chemoprophylaxis and detection should be introduced in the population to reduce the number of individuals with active TB. Results provide a framework for designing the cost-effective strategies for TB with two intervention methods.
Hedging Strategies for Bayesian Optimization
Brochu, Eric; de Freitas, Nando
2010-01-01
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It is able to do this by sampling the objective using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We describe the method, which we call GP-Hedge, and show that this method almost always outperforms the best individual acquisition function.
How landscape heterogeneity frames optimal diffusivity in searching processes.
Directory of Open Access Journals (Sweden)
E P Raposo
2011-11-01
Full Text Available Theoretical and empirical investigations of search strategies typically have failed to distinguish the distinct roles played by density versus patchiness of resources. It is well known that motility and diffusivity of organisms often increase in environments with low density of resources, but thus far there has been little progress in understanding the specific role of landscape heterogeneity and disorder on random, non-oriented motility. Here we address the general question of how the landscape heterogeneity affects the efficiency of encounter interactions under global constant density of scarce resources. We unveil the key mechanism coupling the landscape structure with optimal search diffusivity. In particular, our main result leads to an empirically testable prediction: enhanced diffusivity (including superdiffusive searches, with shift in the diffusion exponent, favors the success of target encounters in heterogeneous landscapes.
Optimal growth strategies under divergent predation pressure.
Aikio, S; Herczeg, G; Kuparinen, A; Merilä, J
2013-01-01
The conditions leading to gigantism in nine-spined sticklebacks Pungitius pungitius were analysed by modelling fish growth with the von Bertalanffy model searching for the optimal strategy when the model's growth constant and asymptotic fish size parameters are negatively related to each other. Predator-related mortality was modelled through the increased risk of death during active foraging. The model was parameterized with empirical growth data of fish from four different populations and analysed for optimal growth strategy at different mortality levels. The growth constant and asymptotic fish size were negatively related in most populations. Optimal fish size, fitness and life span decreased with predator-induced mortality. At low mortality, the fitness of pond populations was higher than that of sea populations. The differences disappeared at intermediate mortalities, and sea populations had slightly higher fitness at extremely high mortalities. In the scenario where all populations mature at the same age, the pond populations perform better at low mortalities and the sea populations at high mortalities. It is concluded that a trade-off between growth constant and asymptotic fish size, together with different mortality rates, can explain a significant proportion of body size differentiation between populations. In the present case, it is a sufficient explanation of gigantism in pond P. pungitius. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.
Affordance Learning Based on Subtask's Optimal Strategy
Directory of Open Access Journals (Sweden)
Huaqing Min
2015-08-01
Full Text Available Affordances define the relationships between the robot and environment, in terms of actions that the robot is able to perform. Prior work is mainly about predicting the possibility of a reactive action, and the object's affordance is invariable. However, in the domain of dynamic programming, a robot’s task could often be decomposed into several subtasks, and each subtask could limit the search space. As a result, the robot only needs to replan its sub strategy when an unexpected situation happens, and an object’s affordance might change over time depending on the robot’s state and current subtask. In this paper, we propose a novel affordance model linking the subtask, object, robot state and optimal action. An affordance represents the first action of the optimal strategy under the current subtask when detecting an object, and its influence is promoted from a primitive action to the subtask strategy. Furthermore, hierarchical reinforcement learning and state abstraction mechanism are introduced to learn the task graph and reduce state space. In the navigation experiment, the robot equipped with a camera could learn the objects’ crucial characteristics, and gain their affordances in different subtasks.
A Strategy Tackling Local Minimum of Direct Search Method in Modeling a Hydraulic Actuator
Institute of Scientific and Technical Information of China (English)
刘云山; 陈晓辉
2013-01-01
A strategy for attacking the local minimum problem of direct search method is developed for modeling a hydraulic actuator. The Nelder-Mead direct search method is combined with Ordinary Least Squares which can used to optimize the parameters which the model function is in linear with. The model fitting results show that this strategy can reach a solution more close to the global minimum than the Nelder-Mead direct search method used alone.
The Effect of Teaching Search Strategies on Perceptual Performance
van der Gijp, Anouk; Vincken, Koen L|info:eu-repo/dai/nl/143101722; Boscardin, Christy K.; Webb, Emily M; Ten Cate, Olle Th J|info:eu-repo/dai/nl/068931204; Naeger, David M
2017-01-01
RATIONALE AND OBJECTIVES: Radiology expertise is dependent on the use of efficient search strategies. The aim of this study is to investigate the effect of teaching search strategies on trainee's accuracy in detecting lung nodules at computed tomography. MATERIALS AND METHODS: Two search strategies,
Iris Localization Based on Edge Searching Strategies
Institute of Scientific and Technical Information of China (English)
Wang Yong; Han Jiuqiang
2005-01-01
An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-ofGaussian (LoG) is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparently faster than Hough transform.
Optimization of BEV Charging Strategy
Ji, Wei
This paper presents different approaches to optimize fast charging and workplace charging strategy of battery electric vehicle (BEV) drivers. For the fast charging analysis, a rule-based model was built to simulate BEV charging behavior. Monte Carlo analysis was performed to explore to the potential range of congestion at fast charging stations which could be more than four hours at the most crowded stations. Genetic algorithm was performed to explore the theoretical minimum waiting time at fast charging stations, and it can decrease the waiting time at the most crowded stations to be shorter than one hour. A deterministic approach was proposed as a feasible suggestion that people should consider to take fast charging when the state of charge is approaching 40 miles. This suggestion is hoped to help to minimize potential congestion at fast charging stations. For the workplace charging analysis, scenario analysis was performed to simulate temporal distribution of charging demand under different workplace charging strategies. It was found that if BEV drivers charge as much as possible and as late as possible at workplace, it could increase the utility of solar-generated electricity while relieve grid stress of extra intensive electricity demand at night caused by charging electric vehicles at home.
Strategies for searching and managing evidence-based practice resources.
Robb, Meigan; Shellenbarger, Teresa
2014-10-01
Evidence-based nursing practice requires the use of effective search strategies to locate relevant resources to guide practice change. Continuing education and staff development professionals can assist nurses to conduct effective literature searches. This article provides suggestions for strategies to aid in identifying search terms. Strategies also are recommended for refining searches by using controlled vocabulary, truncation, Boolean operators, PICOT (Population/Patient Problem, Intervention, Comparison, Outcome, Time) searching, and search limits. Suggestions for methods of managing resources also are identified. Using these approaches will assist in more effective literature searches and may help evidence-based practice decisions.
Topological Optimization of Artificial Microstructure Strategies
2015-04-02
Topographic Optimization Through Artificial Microstructure Strategies During this project as part of DARPA MCMA we aimed to develop and demonstrate...Topographic Optimization Through Artificial Microstructure Strategies Report Title During this project as part of DARPA MCMA we aimed to develop and...Artificial Microstructure Strategies (Yale and Johns Hopkins) During DARPA MCMA we aimed to develop and demonstrate a 3D microstructural
Swarm Robots Search for Multiple Targets Based on an Improved Grouping Strategy.
Tang, Qirong; Ding, Lu; Yu, Fangchao; Zhang, Yuan; Li, Yinghao; Tu, Haibo
2017-03-14
Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally. Obstacle avoidance is considered during the search process. Simulation compared with previous method demonstrates the adaptability, accuracy and efficiency of the proposed strategy in multiple targets searching.
In Search of Search Engine Marketing Strategy Amongst SME's in Ireland
Barry, Chris; Charleton, Debbie
Researchers have identified the Web as a searchers first port of call for locating information. Search Engine Marketing (SEM) strategies have been noted as a key consideration when developing, maintaining and managing Websites. A study presented here of SEM practices of Irish small to medium enterprises (SMEs) reveals they plan to spend more resources on SEM in the future. Most firms utilize an informal SEM strategy, where Website optimization is perceived most effective in attracting traffic. Respondents cite the use of ‘keywords in title and description tags’ as the most used SEM technique, followed by the use of ‘keywords throughout the whole Website’; while ‘Pay for Placement’ was most widely used Paid Search technique. In concurrence with the literature, measuring SEM performance remains a significant challenge with many firms unsure if they measure it effectively. An encouraging finding is that Irish SMEs adopt a positive ethical posture when undertaking SEM.
Group Search Optimization for Fixed Head Hydrothermal Power System
Jena, Chitralekha; Basu, Mousumi
2017-02-01
This paper presents group search optimization for optimal scheduling of thermal plants in coordination with fixed head hydro units. Numerical results for two test systems have been presented to demonstrate the performance of the proposed method. Results obtained from the proposed group search optimization method have been compared with those obtained from differential evolution and evolutionary programming.
Jianwen Guo; Zhenzhong Sun; Hong Tang; Xuejun Jia; Song Wang; Xiaohui Yan; Guoliang Ye; Guohong Wu
2016-01-01
All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test fun...
Formalized Search Strategies for Human Risk Contributions
DEFF Research Database (Denmark)
Rasmussen, Jens; Pedersen, O. M.
For risk management, the results of a probabilistic risk analysis (PRA) as well as the underlying assumptions can be used as references in a closed-loop risk control; and the analyses of operational experiences as a means of feedback. In this context, the need for explicit definition and document......For risk management, the results of a probabilistic risk analysis (PRA) as well as the underlying assumptions can be used as references in a closed-loop risk control; and the analyses of operational experiences as a means of feedback. In this context, the need for explicit definition...... and documentation of the PRA coverage, including the search strategies applied, is discussed and aids are proposed such as plant description in terms of a formal abstraction hierarchy and use of cause-consequence-charts for the documentation of not only the results of PRA but also of its coverage. Typical human...... risk contributions are described on the basis of general plant design features relevant for risk and accident analysis. With this background, search strategies for human risk contributions are treated: Under the designation "work analysis", procedures for the analysis of familiar, well trained, planned...
Optimal Investment Strategy for Risky Assets
Sergei Maslov; Yi-Cheng Zhang
1998-01-01
We design an optimal strategy for investment in a portfolio of assets subject to a multiplicative Brownian motion. The strategy provides the maximal typical long-term growth rate of investor's capital. We determine the optimal fraction of capital that an investor should keep in risky assets as well as weights of different assets in an optimal portfolio. In this approach both average return and volatility of an asset are relevant indicators determining its optimal weight. Our results are parti...
Genetic-Algorithm Tool For Search And Optimization
Wang, Lui; Bayer, Steven
1995-01-01
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
Institute of Scientific and Technical Information of China (English)
刘乐
2016-01-01
为了改善标准果蝇优化（fruit fly optimization，FFO）算法易陷入局部极优，收敛精度不高的不足，提出了一种结合群体协同（swarm collaboration，SC）与和声搜索（harmony search，HS）策略的新型果蝇优化算法FFO-SC+HS。该算法基于随机确定的单一维度和动态搜索半径得到果蝇个体的食物源位置，并在种群中心位置的逐代更新环节新增了两个可供选择的备选位置。两备选位置均出自按群体协同策略重构后的位置集合，其一为重构后位置向量集合中的最佳位置，另一则为借助和声搜索策略得到的新位置向量。为验证所设计算法的有效性，在10种测试函数上进行了大量的计算实验与性能对比分析，结果表明FFO-SC+HS在求解质量、收敛能力上优于其他4种已报道的FFO算法，并发现3个主要参数的不同取值组合对其优化性能具有显著影响，所采取的SC与HS策略缺一不可。%To deal with the drawbacks of trapping in local optimal solutions easily and low convergence accuracy of the standard fruit fly optimization (FFO) algorithm, this paper proposes a novel fruit fly optimization algorithm by combining the swarm collaboration (SC) and harmony search (HS) strategies, named as FFO-SC+HS. In each iteration of FFO-SC+HS, the food source location of each fruit fly is generated based on a single dimension that is randomly determined and dynamic search radius, and two candidate location vectors derived from the reconstructed location set by SC strategy are considered during the update process of fruit fly swarm location. Further, one candidate location is the best one of the reconstructed location set, and the other one is obtained by means of HS strategy. Extensive compu-tational experiments and comparison analysis are conducted upon 10 benchmark functions to validate the effectiveness of FFO-SC+HS. As demonstrated in the results, FFO-SC+HS outperforms other 4 reported
Faccioli, Primetta; Ciceri, Gian Paolo; Provero, Paolo; Stanca, Antonio Michele; Morcia, Caterina; Terzi, Valeria
2007-03-01
Traditionally housekeeping genes have been employed as endogenous reference (internal control) genes for normalization in gene expression studies. Since the utilization of single housekeepers cannot assure an unbiased result, new normalization methods involving multiple housekeeping genes and normalizing using their mean expression have been recently proposed. Moreover, since a gold standard gene suitable for every experimental condition does not exist, it is also necessary to validate the expression stability of every putative control gene on the specific requirements of the planned experiment. As a consequence, finding a good set of reference genes is for sure a non-trivial problem requiring quite a lot of lab-based experimental testing. In this work we identified novel candidate barley reference genes suitable for normalization in gene expression studies. An advanced web search approach aimed to collect, from publicly available web resources, the most interesting information regarding the expression profiling of candidate housekeepers on a specific experimental basis has been set up and applied, as an example, on stress conditions. A complementary lab-based analysis has been carried out to verify the expression profile of the selected genes in different tissues and during heat shock response. This combined dry/wet approach can be applied to any species and physiological condition of interest and can be considered very helpful to identify putative reference genes to be shortlisted every time a new experimental design has to be set up.
Group Search Optimizer for the Mobile Location Management Problem
Directory of Open Access Journals (Sweden)
Dan Wang
2014-01-01
Full Text Available We propose a diversity-guided group search optimizer-based approach for solving the location management problem in mobile computing. The location management problem, which is to find the optimal network configurations of management under the mobile computing environment, is considered here as an optimization problem. The proposed diversity-guided group search optimizer algorithm is realized with the aid of diversity operator, which helps alleviate the premature convergence problem of group search optimizer algorithm, a successful optimization algorithm inspired by the animal behavior. To address the location management problem, diversity-guided group search optimizer algorithm is exploited to optimize network configurations of management by minimizing the sum of location update cost and location paging cost. Experimental results illustrate the effectiveness of the proposed approach.
Group Search Optimizer for the Mobile Location Management Problem
Wang, Dan; Xiong, Congcong; Huang, Wei
2014-01-01
We propose a diversity-guided group search optimizer-based approach for solving the location management problem in mobile computing. The location management problem, which is to find the optimal network configurations of management under the mobile computing environment, is considered here as an optimization problem. The proposed diversity-guided group search optimizer algorithm is realized with the aid of diversity operator, which helps alleviate the premature convergence problem of group search optimizer algorithm, a successful optimization algorithm inspired by the animal behavior. To address the location management problem, diversity-guided group search optimizer algorithm is exploited to optimize network configurations of management by minimizing the sum of location update cost and location paging cost. Experimental results illustrate the effectiveness of the proposed approach. PMID:25180199
Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization
Directory of Open Access Journals (Sweden)
Xiao Fu
2013-01-01
Full Text Available Quantum-behaved particle swarm optimization (QPSO algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.
Directory of Open Access Journals (Sweden)
Jianwen Guo
2016-01-01
Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.
Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization
Institute of Scientific and Technical Information of China (English)
Mudong Li; Hui Zhao; Xingwei Weng; Hanqiao Huang
2015-01-01
The artificial bee colony (ABC) algorithm is a sim-ple and effective global optimization algorithm which has been successful y applied in practical optimization problems of various fields. However, the algorithm is stil insufficient in balancing ex-ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which ful y utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self-adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en-hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition-based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func-tions show that the proposed algorithm, especial y for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.
Optimization of machining processes using pattern search algorithm
Miloš Madić; Miroslav Radovanović
2014-01-01
Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers a...
Enhanced Ocean Predictability Through Optimal Observing Strategies
2016-06-14
Enhanced Ocean Predictability Through Optimal Observing Strategies A. D. Kirwan, Jr. College of Marine Studies University of Delaware Robinson Hall...observation strategies that will maximize the capacity to predict mesoscale and submesoscale conditions so as to provide the best possible nowcasts and...systems approaches on developing optimal observing strategies . The common thread linking both approaches is Lagrangian data, so this phase of the work
Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution
Directory of Open Access Journals (Sweden)
Hongxue Yang
2014-04-01
Full Text Available Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distribution is proposed in this paper. Taboo search is a metaheuristic search method to perform local search used for logistic optimization. The taboo search is employed to accelerate convergence and the aspiration criterion is combined with the heuristics algorithm to solve routing optimization. Simulation experimental results demonstrate that the optimal routing in the logistic distribution can be quickly obtained by the taboo search algorithm
When is it time to move to the next map? Optimal foraging in guided visual search.
Ehinger, Krista A; Wolfe, Jeremy M
2016-10-01
Suppose that you are looking for visual targets in a set of images, each containing an unknown number of targets. How do you perform that search, and how do you decide when to move from the current image to the next? Optimal foraging theory predicts that foragers should leave the current image when the expected value from staying falls below the expected value from leaving. Here, we describe how to apply these models to more complex tasks, like search for objects in natural scenes where people have prior beliefs about the number and locations of targets in each image, and search is guided by target features and scene context. We model these factors in a guided search task and predict the optimal time to quit search. The data come from a satellite image search task. Participants searched for small gas stations in large satellite images. We model quitting times with a Bayesian model that incorporates prior beliefs about the number of targets in each map, average search efficiency (guidance), and actual search history in the image. Clicks deploying local magnification were used as surrogates for deployments of attention and, thus, for time. Leaving times (measured in mouse clicks) were well-predicted by the model. People terminated search when their expected rate of target collection fell to the average rate for the task. Apparently, people follow a rate-optimizing strategy in this task and use both their prior knowledge and search history in the image to decide when to quit searching.
Determining an optimal supply chain strategy
Directory of Open Access Journals (Sweden)
Intaher M. Ambe
2012-11-01
Full Text Available In today’s business environment, many companies want to become efficient and flexible, but have struggled, in part, because they have not been able to formulate optimal supply chain strategies. Often this is as a result of insufficient knowledge about the costs involved in maintaining supply chains and the impact of the supply chain on their operations. Hence, these companies find it difficult to manufacture at a competitive cost and respond quickly and reliably to market demand. Mismatched strategies are the root cause of the problems that plague supply chains, and supply-chain strategies based on a one-size-fits-all strategy often fail. The purpose of this article is to suggest instruments to determine an optimal supply chain strategy. This article, which is conceptual in nature, provides a review of current supply chain strategies and suggests a framework for determining an optimal strategy.
Linear Tabling Strategies and Optimizations
Zhou, Neng-Fa; Shen, Yi-Dong
2007-01-01
Recently, the iterative approach named linear tabling has received considerable attention because of its simplicity, ease of implementation, and good space efficiency. Linear tabling is a framework from which different methods can be derived based on the strategies used in handling looping subgoals. One decision concerns when answers are consumed and returned. This paper describes two strategies, namely, {\\it lazy} and {\\it eager} strategies, and compares them both qualitatively and quantitatively. The results indicate that, while the lazy strategy has good locality and is well suited for finding all solutions, the eager strategy is comparable in speed with the lazy strategy and is well suited for programs with cuts. Linear tabling relies on depth-first iterative deepening rather than suspension to compute fixpoints. Each cluster of inter-dependent subgoals as represented by a top-most looping subgoal is iteratively evaluated until no subgoal in it can produce any new answers. Naive re-evaluation of all loopi...
Optimization of machining processes using pattern search algorithm
Directory of Open Access Journals (Sweden)
Miloš Madić
2014-04-01
Full Text Available Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.
Piehowski, Paul D; Petyuk, Vladislav A; Sandoval, John D; Burnum, Kristin E; Kiebel, Gary R; Monroe, Matthew E; Anderson, Gordon A; Camp, David G; Smith, Richard D
2013-03-01
For bottom-up proteomics, there are wide variety of database-searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid-search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection--referred to as STEPS--utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true-positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.
Optimizing literature search in systematic reviews
DEFF Research Database (Denmark)
Aagaard, Thomas; Lund, Hans; Juhl, Carsten Bogh
2016-01-01
BACKGROUND: When conducting systematic reviews, it is essential to perform a comprehensive literature search to identify all published studies relevant to the specific research question. The Cochrane Collaborations Methodological Expectations of Cochrane Intervention Reviews (MECIR) guidelines...... of musculoskeletal disorders. METHODS: Data sources were systematic reviews published by the Cochrane Musculoskeletal Review Group, including at least five RCTs, reporting a search history, searching MEDLINE, EMBASE, CENTRAL, and adding reference- and hand-searching. Additional databases were deemed eligible...... if they indexed RCTs, were in English and used in more than three of the systematic reviews. Relative recall was calculated as the number of studies identified by the literature search divided by the number of eligible studies i.e. included studies in the individual systematic reviews. Finally, cumulative median...
Developing & Optimizing a Logical Sourcing Strategy
National Research Council Canada - National Science Library
Lee S Scheible; Chris Bodurow; Karin Daun
2015-01-01
In order to optimize the benefit of the sourcing strategy and ensure delivery of the portfolio, a logical operational process flow must be developed and implemented consistently across all study...
Optimal strategies for flood prevention
Eijgenraam, Carel; Brekelmans, Ruud; den Hertog, Dick; Roos, C.
2016-01-01
Flood prevention policy is of major importance to the Netherlands since a large part of the country is below sea level and high water levels in rivers may also cause floods. In this paper we propose a dike height optimization model to determine economically efficient flood protection standards. We i
Uncovering Web search strategies in South African higher education
Directory of Open Access Journals (Sweden)
Surika Civilcharran
2016-04-01
Full Text Available Background: In spite of the enormous amount of information available on the Web and the fact that search engines are continuously evolving to enhance the search experience, students are nevertheless faced with the difficulty of effectively retrieving information. It is, therefore, imperative for the interaction between students and search tools to be understood and search strategies to be identified, in order to promote successful information retrieval.Objectives: This study identifies the Web search strategies used by postgraduate students and forms part of a wider study into information retrieval strategies used by postgraduate students at the University of KwaZulu-Natal (UKZN, Pietermaritzburg campus, South Africa. Method: Largely underpinned by Thatcher’s cognitive search strategies, the mixed-methods approach was utilised for this study, in which questionnaires were employed in Phase 1 and structured interviews in Phase 2. This article reports and reflects on the findings of Phase 2, which focus on identifying the Web search strategies employed by postgraduate students. The Phase 1 results were reported in Civilcharran, Hughes and Maharaj (2015.Results: Findings reveal the Web search strategies used for academic information retrieval. In spite of easy access to the invisible Web and the advent of meta-search engines, the use of Web search engines still remains the preferred search tool. The UKZN online library databases and especially the UKZN online library, Online Public Access Catalogue system, are being underutilised.Conclusion: Being ranked in the top three percent of the world’s universities, UKZN is investing in search tools that are not being used to their full potential. This evidence suggests an urgent need for students to be trained in Web searching and to have a greater exposure to a variety of search tools. This article is intended to further contribute to the design of undergraduate training programmes in order to deal
Directory of Open Access Journals (Sweden)
John Loy
2007-06-01
Full Text Available Objective – To develop and test the sensitivity and specificity, precision andaccuracy of search strategies to retrieve clinically sound treatment studies in the EMBASE database.Design – Analytical study.Setting – Methodologically sound studies of treatment from 55 journals indexed in EMBASE for the year 2000.Subjects – EMBASE and hand searches performed at the Health Information Research Unit of McMaster University, Ontario, Canada.Methods – The authors compare the results of EMBASE searches using their search strategies with the “gold standard” of articles retrieved by hand search. Research assistants initially hand searched each issue of 55 selected journals published in 2000 to identify articles detailing studies on healthcare treatment of humans. Subject coverage of the journals was wide ranging and included obstetrics and gynaecology, psychiatry, oncology, neurology, surgery and general practice. Studies were then assessed to ensure they met the qualifying criteria: random allocation of participants to groups, outcome assessment of at least 80% of participants who began the study, and analysis consistent with study design. Initially, 3850 articles on treatment were identified, of which 1256 (32.6% were methodologically sound. To construct a comprehensive set of search terms, input was sought from librarians and researchers in the US and Canada. This initially produced a list of 5385 terms, of which 4843 were unique and 3524 produced hits. Individual search terms with sensitivity greater then 25% and specificity greater then 75% were incorporated into search strategies for use within the OVID interface for the EMBASE database to retrieve articles meeting the same criteria. These strategies were developed using all 27,769 articles published in the 55 journals in 2000. This all inclusive approach was used to test the search strategies’ ability to identify high quality treatment articles from a larger pool of material
Adaptive nonmonotone line search method for unconstrained optimization
Institute of Scientific and Technical Information of China (English)
Qunyan ZHOU; Wenyu SUN
2008-01-01
In this paper, an adaptive nonmonotone line search method for unconstrained minimization problems is proposed. At every iteration, the new algorithm selects only one of the two directions: a Newton-type direc-tion and a negative curvature direction, to perform the line search. The nonmonotone technique is included in the backtracking line search when the Newton-type direction is the search direction. Furthermore, if the negative curvature direction is the search direction, we increase the steplength un-der certain conditions. The global convergence to a stationary point with second-order optimality conditions is established. Some numerical results which show the efficiency of the new algorithm are reported.
Hierarchical random walks in trace fossils and the origin of optimal search behavior.
Sims, David W; Reynolds, Andrew M; Humphries, Nicolas E; Southall, Emily J; Wearmouth, Victoria J; Metcalfe, Brett; Twitchett, Richard J
2014-07-29
Efficient searching is crucial for timely location of food and other resources. Recent studies show that diverse living animals use a theoretically optimal scale-free random search for sparse resources known as a Lévy walk, but little is known of the origins and evolution of foraging behavior and the search strategies of extinct organisms. Here, using simulations of self-avoiding trace fossil trails, we show that randomly introduced strophotaxis (U-turns)--initiated by obstructions such as self-trail avoidance or innate cueing--leads to random looping patterns with clustering across increasing scales that is consistent with the presence of Lévy walks. This predicts that optimal Lévy searches may emerge from simple behaviors observed in fossil trails. We then analyzed fossilized trails of benthic marine organisms by using a novel path analysis technique and find the first evidence, to our knowledge, of Lévy-like search strategies in extinct animals. Our results show that simple search behaviors of extinct animals in heterogeneous environments give rise to hierarchically nested Brownian walk clusters that converge to optimal Lévy patterns. Primary productivity collapse and large-scale food scarcity characterizing mass extinctions evident in the fossil record may have triggered adaptation of optimal Lévy-like searches. The findings suggest that Lévy-like behavior has been used by foragers since at least the Eocene but may have a more ancient origin, which might explain recent widespread observations of such patterns among modern taxa.
Developing a Comprehensive Search Strategy for Evidence Based Systematic Reviews
Directory of Open Access Journals (Sweden)
Sekhar Thadiparthi
2008-03-01
Full Text Available Objective ‐ Within the health care field it becomes ever more critical to conduct systematic reviews of the research literature to guide programmatic activities, policy‐making decisions, and future research. Conducting systematic reviews requires a comprehensive search of behavioural, social, and policy research to identify relevant literature. As a result, the validity of the systematic review findings and recommendations is partly a function of the quality of the systematic search of the literature. Therefore, a carefully thought out and organized plan for developing and testing a comprehensive search strategy should be followed. This paper uses the HIV/AIDS prevention literature to provide a framework for developing, testing, and conducting a comprehensive search strategy looking beyond RCTs.Methods ‐ Comprehensive search strategies, including automated and manual search techniques, were developed, tested, and implemented to locate published and unpublished citations in order to build a database of HIV/AIDS and sexually transmitted diseases (STD literature. The search incorporated various automated and manual search methods to decrease the chance of missing pertinent information. The automated search was implemented in MEDLINE, EMBASE,PsycINFO, Sociological Abstracts and AIDSLINE. These searches utilized both index terms as well as keywords including truncation, proximity, and phrases. The manual search method includes physically examining journals (hand searching, reference list checks, and researching key authors.Results ‐ Using automated and manual search components, the search strategy retrieved 17,493 articles about prevention of HIV/AIDS and STDs for the years 1988‐2005. The automated search found 91%, and the manual search contributed 9% of the articles reporting on HIV/AIDS or STD interventions with behavioural/biologic outcomes. Among the citations located with automated searches, 48% were found in only one database (20
Differentially Private Search Log Sanitization with Optimal Output Utility
Hong, Yuan; Lu, Haibing; Wu, Mingrui
2011-01-01
Web search logs contain extremely sensitive data, as evidenced by the recent AOL incident. However, storing and analyzing search logs can be very useful for many purposes (i.e. investigating human behavior). Thus, an important research question is how to privately sanitize search logs. Although several search log anonymization techniques have been proposed with concrete privacy models, the output utility of most techniques is merely evaluated but not necessarily maximized. Indeed, when applying any privacy standard to the search log anonymization, the optimal (maximum utility) output can be derived according to the inter-relation between privacy and utility. In this paper, we take a first step towards tackling this problem by formulating utility-maximizing optimization problems based on the rigorous privacy standard of differential privacy. Specifically, we utilize optimization models to maximize the output utility of the sanitization for different applications, while ensuring that the production process sati...
Optimization of Shallow Foundation Using Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Mohammad Khajehzadeh
2012-01-01
Full Text Available In this study an effective method for nonlinear constrained optimization of shallow foundation is presented. A newly developed heuristic global optimization algorithm called Gravitational Search Algorithm (GSA is introduced and applied for the optimization of foundation. The algorithm is classified as random search algorithm and does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the foundation. To verify the efficiency of the proposed method, two design examples of spread footing are illustrated. To further validate the effectiveness and robustness of the GSA, these examples are solved using genetic algorithm. The results indicate that the proposed method could provide solutions of high quality, accuracy and efficiency for optimum design of foundation.
Optimizing Event Selection with the Random Grid Search
Energy Technology Data Exchange (ETDEWEB)
Bhat, Pushpalatha C. [Fermilab; Prosper, Harrison B. [Florida State U.; Sekmen, Sezen [Kyungpook Natl. U.; Stewart, Chip [Broad Inst., Cambridge
2017-06-29
The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.
Efficient Computation of Optimal Trading Strategies
Boyarshinov, Victor
2010-01-01
Given the return series for a set of instruments, a \\emph{trading strategy} is a switching function that transfers wealth from one instrument to another at specified times. We present efficient algorithms for constructing (ex-post) trading strategies that are optimal with respect to the total return, the Sterling ratio and the Sharpe ratio. Such ex-post optimal strategies are useful analysis tools. They can be used to analyze the "profitability of a market" in terms of optimal trading; to develop benchmarks against which real trading can be compared; and, within an inductive framework, the optimal trades can be used to to teach learning systems (predictors) which are then used to identify future trading opportunities.
Optimized quantum random-walk search algorithm for multi-solution search
Institute of Scientific and Technical Information of China (English)
张宇超; 鲍皖苏; 汪翔; 付向群
2015-01-01
This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.
Exploring Search Engine Optimization (SEO) Techniques for Dynamic Websites
Kanwal, Wasfa
2011-01-01
ABSTRACT Context: With growing number of online businesses, Search Engine Optimization (SEO) has become vital to capitalize a business because SEO is key factor for marketing an online business. SEO is the process to optimize a website so that it ranks well on Search Engine Result Pages (SERPs). Dynamic websites are commonly used for e-commerce because they are easier to update and expand; however they are subjected to indexing related problems. Objectives: This research aims to examine and a...
COMPARISON OF EXPLORATION STRATEGIES FOR MULTI-ROBOT SEARCH
Directory of Open Access Journals (Sweden)
Miroslav Kulich
2015-06-01
Full Text Available Searching for a stationary object in an unknown environment can be formulated as an iterative procedure consisting of map updating, selection of a next goal and navigation to this goal. It finishes when the object of interest is found. This formulation and a general search structure is similar to the related exploration problem. The only difference is in goal-selection, as search and exploration objectives are not the same. Although search is a key task in many search and rescue scenarios, the robotics community has paid little attention to the problem. There is no goal-selection strategy that has been designed specifically for search. In this paper, we study four state-of-the-art strategies for multi-robot exploration, and we evaluate their performance in various environments with respect to the expected time needed to find an object, i.e. to achieve the objective of the search.
Optimal control of anthracnose using mixed strategies.
Fotsa Mbogne, David Jaures; Thron, Christopher
2015-11-01
In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [15]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). For comparative purposes, we perform our analyses for a spatially-averaged model as well as the space-dependent diffusion model. Under weak smoothness conditions on parameters we demonstrate the well-posedness of both models by verifying existence and uniqueness of the solution for the growth inhibition rate for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by impulsive strategies, then analyze the simultaneous use of mixed continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies. In the case of pulse-only strategies, we provide explicit algorithms for finding the optimal control strategies for both the spatially-averaged model and the space-dependent model. We verify the algorithms for both models via simulation, and discuss properties of the optimal solutions. Copyright © 2015 Elsevier Inc. All rights reserved.
Variable Neighborhood Simplex Search Methods for Global Optimization Models
Directory of Open Access Journals (Sweden)
Pongchanun Luangpaiboon
2012-01-01
Full Text Available Problem statement: Many optimization problems of practical interest are encountered in various fields of chemical, engineering and management sciences. They are computationally intractable. Therefore, a practical algorithm for solving such problems is to employ approximation algorithms that can find nearly optimums within a reasonable amount of computational time. Approach: In this study the hybrid methods combining the Variable Neighborhood Search (VNS and simplexs family methods are proposed to deal with the global optimization problems of noisy continuous functions including constrained models. Basically, the simplex methods offer a search scheme without the gradient information whereas the VNS has the better searching ability with a systematic change of neighborhood of the current solution within a local search. Results: The VNS modified simplex method has a better searching ability for optimization problems with noise. The VNS modified simplex method also outperforms in average on the characteristics of intensity and diversity during the evolution of design point moving stage for the constrained optimization. Conclusion: The adaptive hybrid versions have proved to obtain significantly better results than the conventional methods. The amount of computation effort required for successful optimization is very sensitive to the rate of noise decrease of the process yields. Under circumstances of constrained optimization and gradually increasing the noise during an optimization the most preferred approach is the VNS modified simplex method.
Social media networking: YouTube and search engine optimization.
Jackson, Rem; Schneider, Andrew; Baum, Neil
2011-01-01
This is the third part of a three-part article on social media networking. This installment will focus on YouTube and search engine optimization. This article will explore the application of YouTube to the medical practice and how YouTube can help a practice retain its existing patients and attract new patients to the practice. The article will also describe the importance of search engine optimization and how to make your content appear on the first page of the search engines such as Google, Yahoo, and YouTube.
A hybrid genetic algorithm based on mutative scale chaos optimization strategy
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In this algorithm, a mutative scale chaos optimization strategy is operated on the population after a genetic operation. And according to the searching process, the searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population. The algorithm has such advantages as fast search, precise results and convenient using etc. The simulation results show that the performance of the method is better than that of simple genetic algorithms.
How to Find Dental Survival Articles: Using the New Search Strategies.
Layton, Danielle M
2016-01-01
Clinicians and readers rely on accurate identification of articles to answer clinical questions and explore hypotheses. Commonly, these questions relate to the outcome and survival of dental treatments. Errors in indexing and inconsistencies in descriptions of these studies have meant that such articles are difficult to locate. To help address this problem, sensitive, precise and optimized electronic search strategies have been developed, and this article aims to explain how these new strategies can be used. These electronic search strategies have been shown to improve the identification of dental survival analyses.
Direction-Optimizing Breadth-First Search
Directory of Open Access Journals (Sweden)
Scott Beamer
2013-01-01
Full Text Available Breadth-First Search is an important kernel used by many graph-processing applications. In many of these emerging applications of BFS, such as analyzing social networks, the input graphs are low-diameter and scale-free. We propose a hybrid approach that is advantageous for low-diameter graphs, which combines a conventional top-down algorithm along with a novel bottom-up algorithm. The bottom-up algorithm can dramatically reduce the number of edges examined, which in turn accelerates the search as a whole. On a multi-socket server, our hybrid approach demonstrates speedups of 3.3–7.8 on a range of standard synthetic graphs and speedups of 2.4–4.6 on graphs from real social networks when compared to a strong baseline. We also typically double the performance of prior leading shared memory (multicore and GPU implementations.
Optimal strategies for throwing accurately
Venkadesan, Madhusudhan
2010-01-01
Accuracy of throwing in games and sports is governed by how errors at projectile release are propagated by flight dynamics. To address the question of what governs the choice of throwing strategy, we use a simple model of throwing with an arm modelled as a hinged bar of fixed length that can release a projectile at any angle and angular velocity. We show that the amplification of deviations in launch parameters from a one parameter family of solution curves is quantified by the largest singular value of an appropriate Jacobian. This allows us to predict a preferred throwing style in terms of this singular value, which itself depends on target location and the target shape. Our analysis also allows us to characterize the trade-off between speed and accuracy despite not including any effects of signal-dependent noise. Using nonlinear calculations for propagating finite input-noise, we find that an underarm throw to a target leads to an undershoot, but an overarm throw does not. Finally, we consider the limit of...
Getting to the top of Google: search engine optimization.
Maley, Catherine; Baum, Neil
2010-01-01
Search engine optimization is the process of making your Web site appear at or near the top of popular search engines such as Google, Yahoo, and MSN. This is not done by luck or knowing someone working for the search engines but by understanding the process of how search engines select Web sites for placement on top or on the first page. This article will review the process and provide methods and techniques to use to have your site rated at the top or very near the top.
Optimal experimental design strategies for detecting hormesis.
Dette, Holger; Pepelyshev, Andrey; Wong, Weng Kee
2011-12-01
Hormesis is a widely observed phenomenon in many branches of life sciences, ranging from toxicology studies to agronomy, with obvious public health and risk assessment implications. We address optimal experimental design strategies for determining the presence of hormesis in a controlled environment using the recently proposed Hunt-Bowman model. We propose alternative models that have an implicit hormetic threshold, discuss their advantages over current models, and construct and study properties of optimal designs for (i) estimating model parameters, (ii) estimating the threshold dose, and (iii) testing for the presence of hormesis. We also determine maximin optimal designs that maximize the minimum of the design efficiencies when we have multiple design criteria or there is model uncertainty where we have a few plausible models of interest. We apply these optimal design strategies to a teratology study and show that the proposed designs outperform the implemented design by a wide margin for many situations.
Optimal vehicle planning and the search tour problem
Wettergren, Thomas A.; Bays, Matthew J.
2016-05-01
We describe a problem of optimal planning for unmanned vehicles and illustrate two distinct procedures for its solution. The problem under consideration, which we refer to as the search tour problem, involves the determination of multi-stage plans for unmanned vehicles conducting search operations. These types of problems are important in situations where the searcher has varying performance in different regions throughout the domain due to environmental complexity. The ability to provide robust planning for unmanned systems under diﬃcult environmental conditions is critical for their use in search operations. The problem we consider consists of searches with variable times for each of the stages, as well as an additional degree of freedom for each stage to select from one of a finite set of operational configurations. As each combination of configuration and stage time leads to a different performance level, there is a need to determine the optimal configuration of these stages. When the complexity of constraints on total time, as well as resources expended at each stage for a given configuration, are added, the problem becomes one of non-trivial search effort allocation and numerical methods of optimization are required. We show two solution approaches for this numerical optimization problem. The first solution technique is to use a mixed-integer linear programming formulation, for which commercially available solvers can find optimal solutions in a reasonable amount of time. We use this solution as a baseline and compare against a new inner/outer optimization formulation. This inner/outer optimization compares favorably to the baseline solution, but is also amenable to adaptation as the search operation progresses. Numerical examples illustrate the utility of the approach for unmanned vehicle search planning.
Tabu search method with random moves for globally optimal design
Hu, Nanfang
1992-09-01
Optimum engineering design problems are usually formulated as non-convex optimization problems of continuous variables. Because of the absence of convexity structure, they can have multiple minima, and global optimization becomes difficult. Traditional methods of optimization, such as penalty methods, can often be trapped at a local optimum. The tabu search method with random moves to solve approximately these problems is introduced. Its reliability and efficiency are examined with the help of standard test functions. By the analysis of the implementations, it is seen that this method is easy to use, and no derivative information is necessary. It outperforms the random search method and composite genetic algorithm. In particular, it is applied to minimum weight design examples of a three-bar truss, coil springs, a Z-section and a channel section. For the channel section, the optimal design using the tabu search method with random moves saved 26.14 percent over the weight of the SUMT method.
Optimal Deterministic Investment Strategies for Insurers
Directory of Open Access Journals (Sweden)
Ulrich Rieder
2013-11-01
Full Text Available We consider an insurance company whose risk reserve is given by a Brownian motion with drift and which is able to invest the money into a Black–Scholes financial market. As optimization criteria, we treat mean-variance problems, problems with other risk measures, exponential utility and the probability of ruin. Following recent research, we assume that investment strategies have to be deterministic. This leads to deterministic control problems, which are quite easy to solve. Moreover, it turns out that there are some interesting links between the optimal investment strategies of these problems. Finally, we also show that this approach works in the Lévy process framework.
Directory of Open Access Journals (Sweden)
Sukanta Nama
2016-04-01
Full Text Available Differential evolution (DE is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA is a new evolutionary algorithm (EA for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy.
Target contact and exploration strategies in haptic search
Polanen, van V.; Bergmann Tiest, W.M.; Kappers, A.M.L.
2014-01-01
In a haptic search task, one has to detect the presence of a target among distractors using the sense of touch. A salient target can be detected faster than a non-salient target. However, little is known about the exploration strategies that are used, especially in 3D search tasks where items are
Search algorithms as a framework for the optimization of drug combinations.
Directory of Open Access Journals (Sweden)
Diego Calzolari
2008-12-01
Full Text Available Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.
Waffenschmidt, Siw; Hermanns, Tatjana; Gerber-Grote, Andreas; Mostardt, Sarah
2017-02-01
To determine a suitable approach to a systematic search for epidemiologic publications in bibliographic databases. For this purpose, suitable sensitive, precise, and optimized filters were to be selected for MEDLINE searches. In addition, the relevance of bibliographic databases was determined. Epidemiologic systematic reviews (SRs) retrieved in a systematic search and company dossiers were screened to identify epidemiologic publications (primary studies and SRs) published since 2007. These publications were used to generate a test and validation set. Furthermore, each SR's search strategy was reviewed, and epidemiologic filters were extracted. The search syntaxes were validated using the relative recall method. The test set comprises 729 relevant epidemiologic publications, of which 566 were MEDLINE-indexed. About 27 epidemiologic filters were extracted. One suitable sensitive filter was identified (Larney et al. 2013: 95.94% sensitivity). Precision was presumably underestimated so that no precise or optimized filters can be recommended. About 77.64% of the publications were found in MEDLINE. There is currently no suitable approach to conducting efficient systematic searches for epidemiologic publications in bibliographic databases. The filter by Larney et al. (2013) can be used for sensitive MEDLINE searches. No robust conclusions can be drawn on precise or optimized filters. Additional search approaches should be considered. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.
Optimization strategies for complex engineering applications
Energy Technology Data Exchange (ETDEWEB)
Eldred, M.S.
1998-02-01
LDRD research activities have focused on increasing the robustness and efficiency of optimization studies for computationally complex engineering problems. Engineering applications can be characterized by extreme computational expense, lack of gradient information, discrete parameters, non-converging simulations, and nonsmooth, multimodal, and discontinuous response variations. Guided by these challenges, the LDRD research activities have developed application-specific techniques, fundamental optimization algorithms, multilevel hybrid and sequential approximate optimization strategies, parallel processing approaches, and automatic differentiation and adjoint augmentation methods. This report surveys these activities and summarizes the key findings and recommendations.
Directory of Open Access Journals (Sweden)
Haynes R Brian
2005-03-01
Full Text Available Abstract Background Accurate diagnosis by clinicians is the cornerstone of decision making for recommending clinical interventions. The current best evidence from research concerning diagnostic tests changes unpredictably as science advances. Both clinicians and researchers need dependable access to published evidence concerning diagnostic accuracy. Bibliographic databases such as EMBASE provide the most widely available entrée to this literature. The objective of this study was to develop search strategies that optimize the retrieval of methodologically sound diagnostic studies from EMBASE for use by clinicians. Methods An analytic survey was conducted, comparing hand searches of 55 journals with retrievals from EMBASE for 4,843 candidate search terms and 6,574 combinations. All articles were rated using purpose and quality indicators, and clinically relevant diagnostic accuracy articles were categorized as 'pass' or 'fail' according to explicit criteria for scientific merit. Candidate search strategies were run in EMBASE, the retrievals being compared with the hand search data. The proposed search strategies were treated as "diagnostic tests" for sound studies and the manual review of the literature was treated as the "gold standard." The sensitivity, specificity, precision and accuracy of the search strategies were calculated. Results Of the 433 articles about diagnostic tests, 97 (22.4% met basic criteria for scientific merit. Combinations of search terms reached peak sensitivities of 100% with specificity at 70.4%. Compared with best single terms, best multiple terms increased sensitivity for sound studies by 8.2% (absolute increase, but decreased specificity (absolute decrease 6% when sensitivity was maximized. When terms were combined to maximize specificity, the single term "specificity.tw." (specificity of 98.2% outperformed combinations of terms. Conclusion Empirically derived search strategies combining indexing terms and textwords
Optimizing the search for transiting planets in long time series
Ofir, Aviv
2014-01-01
Context. Transit surveys, both ground- and space-based, have already accumulated a large number of light curves that span several years. Aims: The search for transiting planets in these long time series is computationally intensive. We wish to optimize the search for both detection and computational efficiencies. Methods: We assume that the searched systems can be described well by Keplerian orbits. We then propagate the effects of different system parameters to the detection parameters. Results: We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). We also show how the physical system parameters, such as the host star's size and mass, directly affect transit detection. This understanding can then be used to optimize the search for every star individually. Conclusions: By considering Keplerian dynamics explicitly rather than implicitly one can optimally search the BLS parameter space. The presented Optimal BLS enhances the detectability of both very short and very long period planets, while allowing such searches to be done with much reduced resources and time. The Matlab/Octave source code for Optimal BLS is made available. The MATLAB code is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/561/A138
Gradient-Based Cuckoo Search for Global Optimization
Directory of Open Access Journals (Sweden)
Seif-Eddeen K. Fateen
2014-01-01
Full Text Available One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradient-based modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS and evaluated its performance vis-à-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available.
Optimizing Infant Development: Strategies for Day Care.
Chambliss, Catherine
This guide for infant day care providers examines the importance of early experience for brain development and strategies for providing optimal infant care. The introduction discusses the current devaluation of day care and idealization of maternal care and identifies benefits of quality day care experience for intellectual development, sleep…
Optimal Heating Strategies for a Convection Oven
Stigter, J.D.; Scheerlinck, N.; Nicolai, B.M.; Impe, van J.F.
2001-01-01
In this study classical control theory is applied to a heat conduction model with convective boundary conditions. Optimal heating strategies are obtained through solution of an associated algebraic Riccati equation for a finite horizon linear quadratic regulator (LQR). The large dimensional system
Instance Optimality of the Adaptive Maximum Strategy
L. Diening; C. Kreuzer; R. Stevenson
2016-01-01
In this paper, we prove that the standard adaptive finite element method with a (modified) maximum marking strategy is instance optimal for the total error, being the square root of the squared energy error plus the squared oscillation. This result will be derived in the model setting of Poisson’s e
Designing Search UX Strategies for eCommerce Success
Nudelman, Greg
2011-01-01
Best practices, practical advice, and design ideas for successful ecommerce search A glaring gap has existed in the market for a resource that offers a comprehensive, actionable design patterns and design strategies for ecommerce search-but no longer. With this invaluable book, user experience designer and user researcher Greg Nudelman shares his years of experience working on popular ecommerce sites as he tackles even the most difficult ecommerce search design problems. Nudelman helps you create highly effective and intuitive ecommerce search design solutions and he takes a unique forward-thi
Optimal inspection Strategies for Offshore Structural Systems
DEFF Research Database (Denmark)
Faber, M. H.; Sørensen, John Dalsgaard; Kroon, I. B.
1992-01-01
Optimal planning of inspection and maintenance strategies for structures has become a subject of increasing interest especially for offshore structures for which large costs are associated with structural failure, inspections and repairs. During the last five years a methodology has been formulated...... a mathematical framework for the estimation of the failure and repair costs a.ssociated with systems failure. Further a strategy for selecting the components to inspect based on decision tree analysis is suggested. Methods and analysis schemes are illustrated by a simple example....... to perform optimal inspection and repair strategies for structural components subject to uncertain loading conditions and material behavior. In this paper this methodology is extended to inelude also system failure i.e. failure of a given sub set of all the structural components. This extension ineludes...
Directory of Open Access Journals (Sweden)
Ferhatosmanoglu Nilgun
2009-09-01
Full Text Available Abstract Background Dual-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. Description An 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. Conclusion A 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 http://db.cse.ohio-state.edu/MicroarrayDesigner. Source code and binary distributions are available for academic use upon request.
Decoherence in optimized quantum random-walk search algorithm
Zhang, Yu-Chao; Bao, Wan-Su; Wang, Xiang; Fu, Xiang-Qun
2015-08-01
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative. Project supported by the National Basic Research Program of China (Grant No. 2013CB338002).
Budget constraints and optimization in sponsored search auctions
Yang, Yanwu
2013-01-01
The Intelligent Systems Series publishes reference works and handbooks in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. They include theoretical studies, design methods, and real-world implementations and applications. The series' readership is broad, but focuses on engineering, electronics, and computer science. Budget constraints and optimization in sponsored search auctions takes into account consideration of the entire life cycle of campaigns for researchers and developers working on search systems and ROI maximization
Search for Directed Networks by Different Random Walk Strategies
Institute of Scientific and Technical Information of China (English)
ZHU Zi-Qi; JIN Xiao-Ling; HUANG Zhi-Long
2012-01-01
A comparative study is carried out on the effciency of five different random walk strategies searching on directed networks constructed based on several typical complex networks.Due to the difference in search effciency of the strategies rooted in network clustering,the clustering coeFfcient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks.The search processes are performed on the directed networks based on Erd(o)s-Rényi model,Watts-Strogatz model,Barabási-Albert model and clustered scale-free network model.It is found that self-avoiding random walk strategy is the best search strategy for such directed networks.Compared to unrestricted random walk strategy,path-iteration-avoiding random walks can also make the search process much more effcient. However,no-triangle-loop and no-quadrangle-loop random walks do not improve the search effciency as expected,which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.
Individual differences and metacognitive knowledge of visual search strategy.
Proulx, Michael J
2011-01-01
A crucial ability for an organism is to orient toward important objects and to ignore temporarily irrelevant objects. Attention provides the perceptual selectivity necessary to filter an overwhelming input of sensory information to allow for efficient object detection. Although much research has examined visual search and the 'template' of attentional set that allows for target detection, the behavior of individual subjects often reveals the limits of experimental control of attention. Few studies have examined important aspects such as individual differences and metacognitive strategies. The present study analyzes the data from two visual search experiments for a conjunctively defined target (Proulx, 2007). The data revealed attentional capture blindness, individual differences in search strategies, and a significant rate of metacognitive errors for the assessment of the strategies employed. These results highlight a challenge for visual attention studies to account for individual differences in search behavior and distractibility, and participants that do not (or are unable to) follow instructions.
Individual differences and metacognitive knowledge of visual search strategy.
Directory of Open Access Journals (Sweden)
Michael J Proulx
Full Text Available A crucial ability for an organism is to orient toward important objects and to ignore temporarily irrelevant objects. Attention provides the perceptual selectivity necessary to filter an overwhelming input of sensory information to allow for efficient object detection. Although much research has examined visual search and the 'template' of attentional set that allows for target detection, the behavior of individual subjects often reveals the limits of experimental control of attention. Few studies have examined important aspects such as individual differences and metacognitive strategies. The present study analyzes the data from two visual search experiments for a conjunctively defined target (Proulx, 2007. The data revealed attentional capture blindness, individual differences in search strategies, and a significant rate of metacognitive errors for the assessment of the strategies employed. These results highlight a challenge for visual attention studies to account for individual differences in search behavior and distractibility, and participants that do not (or are unable to follow instructions.
Tsai, Pei-Shan; Tsai, Chin-Chung; Hwang, Gwo-Jen
2011-01-01
This study aimed to explore the correlates among teachers' epistemological beliefs concerning Internet environments, their web search strategies and search outcomes. The sample of this study included 105 teachers from 63 grades 1 to 9 schools in Taiwan. The results show that the teachers with more advanced epistemological beliefs concerning…
Dual-mode nested search method for categorical uncertain multi-objective optimization
Tang, Long; Wang, Hu
2016-10-01
Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.
Wolf Search Algorithm for Solving Optimal Reactive Power Dispatch Problem
Directory of Open Access Journals (Sweden)
Kanagasabai Lenin
2015-03-01
Full Text Available This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio – inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm .The proposed (WSA algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm .
Improved symbiotic organisms search algorithm for solving unconstrained function optimization
Directory of Open Access Journals (Sweden)
Sukanta Nama
2016-09-01
Full Text Available Recently, Symbiotic Organisms Search (SOS algorithm is being used for solving complex problems of optimization. This paper proposes an Improved Symbiotic Organisms Search (I-SOS algorithm for solving different complex unconstrained global optimization problems. In the improved algorithm, a random weighted reflective parameter and predation phase are suggested to enhance the performance of the algorithm. The performances of this algorithm are compared with the other state-of-the-art algorithms. The parametric study of the common control parameter has also been performed.
Directory of Open Access Journals (Sweden)
Kui-Ting CHEN
2015-12-01
Full Text Available Capacitated vehicle routing problem with pickups and deliveries (CVRPPD is one of the most challenging combinatorial optimization problems which include goods delivery/pickup optimization, vehicle number optimization, routing path optimization and transportation cost minimization. The conventional particle swarm optimization (PSO is difficult to find an optimal solution of the CVRPPD due to its simple search strategy. A PSO with adaptive multi-swarm strategy (AMSPSO is proposed to solve the CVRPPD in this paper. The proposed AMSPSO employs multiple PSO algorithms and an adaptive algorithm with punishment mechanism to search the optimal solution, which can deal with large-scale optimization problems. The simulation results prove that the proposed AMSPSO can solve the CVRPPD with the least number of vehicles and less transportation cost, simultaneously.
Dynamic Grover search: applications in recommendation systems and optimization problems
Chakrabarty, Indranil; Khan, Shahzor; Singh, Vanshdeep
2017-06-01
In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.
Particle Swarm Optimization With Interswarm Interactive Learning Strategy.
Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui
2016-10-01
The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.
Optimization by GRASP greedy randomized adaptive search procedures
Resende, Mauricio G C
2016-01-01
This is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimizat...
Searching for Planets using Particle Swarm Optimization
Chambers, John E.
2008-05-01
The Doppler radial velocity technique has been highly successful in discovering planetary-mass companions in orbit around nearby stars. A typical data set contains around one hundred instantaneous velocities for the star, spread over a period of several years,with each observation measuring only the radial component of velocity. From this data set, one would like to determine the masses and orbital parameters of the system of planets responsible for the star's reflex motion. Assuming coplanar orbits, each planet is characterized by five parameters, with an additional parameter for each telescope used to make observations, representing the instrument's velocity offset. The large number of free parameters and the relatively sparse data sets make the fitting process challenging when multiple planets are present, especially if some of these objects have low masses. Conventional approaches using periodograms often perform poorly when the orbital periods are not separated by large amounts or the longest period is comparable to the length of the data set. Here, I will describe a new approach to fitting Doppler radial velocity sets using particle swarm optimization (PSO). I will describe how the PSO method works, and show examples of PSO fits to existing radial velocity data sets, with comparisons to published solutions and those submitted to the Systemic website (http://www.oklo.org).
Sit-and-wait versus active-search hunting: A behavioral ecological model of optimal search mode.
Ross, Cody T; Winterhalder, Bruce
2015-12-21
Drawing on Skellam׳s (1958) work on sampling animal populations using transects, we derive a behavioral ecological model of the choice between sit-and-wait and active-search hunting. Using simple, biologically based assumptions about the characteristics of predator and prey, we show how an empirically definable parameter space favoring active-search hunting expands as: (1) the average rate of movement of prey decreases, or (2) the energetic costs of hunter locomotion decline. The same parameter space narrows as: (3) prey skittishness increases as a function of a hunter׳s velocity, or (4) prey become less detectable as a function of a hunter׳s velocity. Under either search tactic, encounter rate increases as a function of increasing prey velocity and increasing detection zone radius. Additionally, we investigate the roles of habitat heterogeneity and spatial auto-correlation or grouping of prey on the optimal search mode of a hunter, finding that habitat heterogeneity has the potential to complicate application of the model to some empirical examples, while the effects of prey grouping lead to relatively similar model outcomes. As predicted by the model, the introduction of the horse to the Great Plains and the introduction of the snowmobile to Arctic foraging communities decreased the metabolic costs of active-search and led to a change in normative hunting strategies that favored active-search in place of sit-and-wait hunting.
The Optimal Nash Equilibrium Strategies Under Competition
Institute of Scientific and Technical Information of China (English)
孟力; 王崇喜; 汪定伟; 张爱玲
2004-01-01
This paper presented a game theoretic model to study the competition for a single investment oppertunity under uncertainty. It models the hazard rate of investment as a function of competitors' trigger level. Under uncertainty and different information structure, the option and game theory was applied to researching the optimal Nash equilibrium strategies of one or more firm. By means of Matlab software, the paper simulates a real estate developing project example and illustrates how parameter affects investment strategies. The paper's work will contribute to the present investment practice in China.
Buffer management optimization strategy for satellite ATM
Institute of Scientific and Technical Information of China (English)
Lu Rong; Cao Zhigang
2006-01-01
ECTD (erroneous cell tail drop), a buffer management optimization strategy is suggested which can improve the utilization of buffer resources in satellite ATM (asynchronous transfer mode) networks. The strategy, in which erroneous cells caused by satellite channel and the following cells that belong to the same PDU (protocol data Unit) are discarded, concerns non-real-time data services that use higher layer protocol for retransmission. Based on EPD (early packet drop) policy, mathematical models are established with and without ECTD. The numerical results show that ECTD would optimize buffer management and improve effective throughput (goodput), and the increment of goodput is relative to the CER (cell error ratio) and the PDU length. The higher their values are, the greater the increment. For example,when the average PDU length values are 30 and 90, the improvement of goodput are respectively about 4% and 10%.
PR Students' Perceptions and Readiness for Using Search Engine Optimization
Moody, Mia; Bates, Elizabeth
2013-01-01
Enough evidence is available to support the idea that public relations professionals must possess search engine optimization (SEO) skills to assist clients in a full-service capacity; however, little research exists on how much college students know about the tactic and best practices for incorporating SEO into course curriculum. Furthermore, much…
Wrapped Progressive Sampling Search for Optimizing Learning Algorithm Parameters
Bosch, Antal van den
2005-01-01
We present a heuristic meta-learning search method for finding a set of optimized algorithmic parameters for a range of machine learning algo- rithms. The method, wrapped progressive sampling, is a combination of classifier wrapping and progressive sampling of training data. A series of experiments
Wrapped Progressive Sampling Search for Optimizing Learning Algorithm Parameters
Bosch, Antal van den
2005-01-01
We present a heuristic meta-learning search method for finding a set of optimized algorithmic parameters for a range of machine learning algo- rithms. The method, wrapped progressive sampling, is a combination of classifier wrapping and progressive sampling of training data. A series of experiments
PR Students' Perceptions and Readiness for Using Search Engine Optimization
Moody, Mia; Bates, Elizabeth
2013-01-01
Enough evidence is available to support the idea that public relations professionals must possess search engine optimization (SEO) skills to assist clients in a full-service capacity; however, little research exists on how much college students know about the tactic and best practices for incorporating SEO into course curriculum. Furthermore, much…
Optimal Investment Strategy to Minimize Occupation Time
Bayraktar, Erhan
2008-01-01
We find the optimal investment strategy to minimize the expected time that an individual's wealth stays below zero, the so-called {\\it occupation time}. The individual consumes at a constant rate and invests in a Black-Scholes financial market consisting of one riskless and one risky asset, with the risky asset's price process following a geometric Brownian motion. We also consider an extension of this problem by penalizing the occupation time for the degree to which wealth is negative.
Minimax Strategy of Optimal Unambiguous State Discrimination
Institute of Scientific and Technical Information of China (English)
张文海; 余龙宝; 曹卓良; 叶柳
2012-01-01
In this paper, we consider the minimax strategy to unambiguously discriminate two pure nonorthogonal quantum states without knowing a priori probability. By exploiting the positive-operator valued measure, we derive the upper bound of the minimax measurement of the optimal unambiguous state discrimination. Based on the linear optical devices, we propose an experimentally feasible scheme to implement a minimax measure of a general pair of two nonorthogonal quantum states.
Optimal experimental design strategies for detecting hormesis
2010-01-01
Hormesis is a widely observed phenomenon in many branches of life sciences ranging from toxicology studies to agronomy with obvious public health and risk assessment implications. We address optimal experimental design strategies for determining the presence of hormesis in a controlled environment using the recently proposed Hunt-Bowman model. We propose alternative models that have an implicit hormetic threshold, discuss their advantages over current models, construct and study properties of...
Optimal strategies for pricing general insurance
Emms, P.; Haberman, S.; Savoulli, I.
2006-01-01
Optimal premium pricing policies in a competitive insurance environment are investigated using approximation methods and simulation of sample paths. The market average premium is modelled as a diffusion process, with the premium as the control function and the maximization of the expected total utility of wealth, over a finite time horizon, as the objective. In order to simplify the optimisation problem, a linear utility function is considered and two particular premium strategies are adopted...
Optimal network protection against diverse interdictor strategies
Energy Technology Data Exchange (ETDEWEB)
Ramirez-Marquez, Jose E., E-mail: jmarquez@stevens.ed [Systems Development and Maturity Lab, School of Systems and Enterprises, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030 (United States); Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central de Venezuela, Caracas (Venezuela, Bolivarian Republic of); Levitin, Gregory [Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China (China); Israel Electric Corporation, Reliability and Equipment Department, Haifa 31000 (Israel)
2011-03-15
The objective of this paper is to provide optimal protection configurations for a network with components vulnerable to an interdictor with potentially different attacking strategies. Under this new setting, a solution/configuration describes the defender's optimal amount of defense resources allocated to each link against a potential interdictor strategy. Previous to this research decisions were of a binary nature, restricted to defend or not. Obtaining these configurations is important because along with describing the protection scheme, they are also useful for identifying sets of components critical to the successful performance of the network. The application of the approach can be beneficial for networks in telecommunications, energy, and supply chains to name a few. To obtain an optimal solution, the manuscript describes an evolutionary algorithm that considers continuous decision variables. The results obtained for different examples illustrate that equal resource allocation is optimal for the case of homogeneous component vulnerability. These findings are the basis for discussion and for describing future research directives in this area.
STRATEGIES IN SEARCH FOR INTERNATIONAL PARTNERSHIPS
Directory of Open Access Journals (Sweden)
Denise de Freitas
2015-01-01
Full Text Available Introduction: Internationalization is the process which integrates universal, intercultural or global dimension in a program, in this case, postgraduate. It can be understood as the process of increasing participation in international operations. Method: To offer design and motivational logistics on how to run a process of international scientific relationship. Results: It is necessary to develop several different aspects to be reached internationalization: to know the fundamentals of internationalization; to have international vision; to promote strategy for internationalization; to know the characteristics of an institutional center of internationalization; and to understand the institutional advantages of internationalization. Conclusion: Internationalization is essential to aerate the Brazilian postgraduate and not mischaracterize or weakens the process. On the contrary, it contributes to increase its vitality and capacity for innovation. Today is not possible to imagine science without internationalization.
Theory of Randomized Search Heuristics in Combinatorial Optimization
DEFF Research Database (Denmark)
The rigorous mathematical analysis of randomized search heuristics(RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics includes well-known approaches such as Randomized Local Search (RLS), the Metr......The rigorous mathematical analysis of randomized search heuristics(RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics includes well-known approaches such as Randomized Local Search (RLS...... analysis of randomized algorithms to RSHs. Mostly, the expected runtime of RSHs on selected problems is analzyed. Thereby, we understand why and when RSHs are efficient optimizers and, conversely, when they cannot be efficient. The tutorial will give an overview on the analysis of RSHs for solving...
Digital marketing strategies in the Age of search.
Directory of Open Access Journals (Sweden)
Sionara Ioco Okada
2011-05-01
Full Text Available The evolution of information and communication technologies (ICTs launches an era of exponential growth and distribution of relevant content. With the expansion of the pervasiveness of online growth and ease of mobile web access via mobile devices, "the search" has become popular and search engines have grown and become more sophisticated. This article focuses on making an update to the latest publications of Digital Marketing Strategies, in order to increase the visibility of important concepts and trends, in particular, the evolution of Web 2.0 semantic web, Search Marketing, SEO and SEM strategies, and mobile tagging, QR codes and Augmented Reality. The research was exploratory along with secondary research, particularly literature, focusing on specialized publications, in the period of 1995 to 2010. Some innovations have become marked and turned into powerful trends impacting marketers and Information Technology (IT. The movement of consumption for the web, the ease of search engines for improved products and referrals, and the increased consumption in real time act as irreversible trends for organizations that require efficient marketing strategies. It is imperative to manage the communication business in the "age search" and to make marketing strategies targeted and sustainable upgrades would be required.DOI:10.5585/remark.v10i1.2199
On optimal strategies for upgrading networks
Energy Technology Data Exchange (ETDEWEB)
Krumke, S.O.; Noltemeier, H. [Wuerzburg Univ. (Germany). Dept. of Computer Science; Marathe, M.V. [Los Alamos National Lab., NM (United States); Ravi, S.S. [State Univ. of New York, Albany, NY (United States). Dept. of Computer Science; Ravi, R. [Carnegie-Mellon Univ., Pittsburgh, PA (United States). Graduate School of Industrial Administration; Sundaram, R. [Massachusetts Inst. of Tech., Cambridge, MA (United States)
1996-07-02
We study {ital budget constrained optimal network upgrading problems}. Such problems aim at finding optimal strategies for improving a network under some cost measure subject to certain budget constraints. Given an edge weighted graph {ital G(V,E)}, in the {ital edge based upgrading model}, it is assumed that each edge {ital e} of the given network has an associated function {ital c(e)} that specifies for each edge {ital e} the amount by which the length {ital l(e)} is to be reduced. In the {ital node based upgrading model} a node {ital v} can be upgraded at an expense of cost {ital (v)}. Such an upgrade reduces the cost of each edge incident on {ital v} by a fixed factor {rho}, where 0 < {rho} < 1. For a given budget, {ital B}, the goal is to find an improvement strategy such that the total cost of reduction is a most the given budget {ital B} and the cost of a subgraph (e.g. minimum spanning tree) under the modified edge lengths is the best over all possible strategies which obey the budget constraint. Define an ({alpha},{beta})-approximation algorithm as a polynomial-time algorithm that produces a solution within {alpha} times the optimal function value, violating the budget constraint by a factor of at most {Beta}. The results obtained in this paper include the following 1. We show that in general the problem of computing optimal reduction strategy for modifying the network as above is {bold NP}-hard. 2. In the node based model, we show how to devise a near optimal strategy for improving the bottleneck spanning tree. The algorithms have a performance guarantee of (2 ln {ital n}, 1). 3. for the edge based improvement problems we present improved (in terms of performance and time) approximation algorithms. 4. We also present pseudo-polynomial time algorithms (extendible to polynomial time approximation schemes) for a number of edge/node based improvement problems when restricted to the class of treewidth-bounded graphs.
Joint optimization toward effective and efficient image search.
Wei, Shikui; Xu, Dong; Li, Xuelong; Zhao, Yao
2013-12-01
The bag-of-words (BoW) model has been known as an effective method for large-scale image search and indexing. Recent work shows that the performance of the model can be further improved by using the embedding method. While different variants of the BoW model and embedding method have been developed, less effort has been made to discover their underlying working mechanism. In this paper, we systematically investigate the image search performance variation with respect to a few factors of the BoW model, and study how to employ the embedding method to further improve the image search performance. Subsequently, we summarize several observations based on the experiments on descriptor matching. To validate these observations in a real image search, we propose an effective and efficient image search scheme, in which the BoW model and embedding method are jointly optimized in terms of effectiveness and efficiency by following these observations. Our comprehensive experiments demonstrate that it is beneficial to employ these observations to develop an image search algorithm, and the proposed image search scheme outperforms state-of-the art methods in both effectiveness and efficiency.
A Cooperative Harmony Search Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Gang Li
2014-01-01
Full Text Available Harmony search algorithm (HS is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs and particle swarm optimizers (PSOs. It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.
Aromataris, Edoardo; Riitano, Dagmara
2014-05-01
This article is the third in a new series on the systematic review from the Joanna Briggs Institute, an international collaborative supporting evidence-based practice in nursing, medicine, and allied health fields. The purpose of the series is to show nurses how to conduct a systematic review-one step at a time. This article details the major considerations surrounding search strategies and presents an example of a search using the PubMed platform (pubmed.gov).
Trading Strategy Adipted Optimization of European Call Option
Fukumi, Toshio
2005-01-01
Optimal pricing of European call option is described by linear stochastic differential equation. Trading strategy given by a twin of stochastic variables was integrated w.r.t. Black-Scholes formula to adopt optimal pricing to tarading strategy.
Fishing for Data: Using Particle Swarm Optimization to Search Data
Caputo, Daniel P.; Dolan, R.
2010-01-01
As the size of data and model sets continue to increase, more efficient ways are needed to sift through the available information. We present a computational method which will efficiently search large parameter spaces to either map the space or find individual data/models of interest. Particle swarm optimization (PSO) is a subclass of artificial life computer algorithms. The PSO algorithm attempts to leverage "swarm intelligence” against finding optimal solutions to a problem. This system is often based on a biological model of a swarm (e.g. schooling fish). These biological models are broken down into a few simple rules which govern the behavior of the system. "Agents” (e.g. fish) are introduced and the agents, following the rules, search out solutions much like a fish would seek out food. We have made extensive modifications to the standard PSO model which increase its efficiency as-well-as adding the capacity to map a parameter space and find multiple solutions. Our modified PSO is ideally suited to search and map large sets of data/models which are degenerate or to search through data/models which are too numerous to analyze by hand. One example of this would include radiative transfer models, which are inherently degenerate. Applying the PSO algorithm will allow the degeneracy space to be mapped and thus better determine limits on dust shell parameters. Another example is searching through legacy data from a survey for hints of Polycyclic Aromatic Hydrocarbon emission. What might have once taken years of searching (and many frustrated graduate students) can now be relegated to the task of a computer which will work day and night for only the cost of electricity. We hope this algorithm will allow fellow astronomers to more efficiently search data and models, thereby freeing them to focus on the physics of the Universe.
Stochastic optimal foraging: tuning intensive and extensive dynamics in random searches.
Directory of Open Access Journals (Sweden)
Frederic Bartumeus
Full Text Available Recent theoretical developments had laid down the proper mathematical means to understand how the structural complexity of search patterns may improve foraging efficiency. Under information-deprived scenarios and specific landscape configurations, Lévy walks and flights are known to lead to high search efficiencies. Based on a one-dimensional comparative analysis we show a mechanism by which, at random, a searcher can optimize the encounter with close and distant targets. The mechanism consists of combining an optimal diffusivity (optimally enhanced diffusion with a minimal diffusion constant. In such a way the search dynamics adequately balances the tension between finding close and distant targets, while, at the same time, shifts the optimal balance towards relatively larger close-to-distant target encounter ratios. We find that introducing a multiscale set of reorientations ensures both a thorough local space exploration without oversampling and a fast spreading dynamics at the large scale. Lévy reorientation patterns account for these properties but other reorientation strategies providing similar statistical signatures can mimic or achieve comparable efficiencies. Hence, the present work unveils general mechanisms underlying efficient random search, beyond the Lévy model. Our results suggest that animals could tune key statistical movement properties (e.g. enhanced diffusivity, minimal diffusion constant to cope with the very general problem of balancing out intensive and extensive random searching. We believe that theoretical developments to mechanistically understand stochastic search strategies, such as the one here proposed, are crucial to develop an empirically verifiable and comprehensive animal foraging theory.
Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation
Directory of Open Access Journals (Sweden)
S.K. Saha
2015-01-01
Full Text Available This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA and Wavelet Mutation (WM strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM was adopted for the design of an 8th-order infinite impulse response (IIR filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP, high-pass (HP, band-pass (BP and band-stop (BS IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability.
Early detection network design and search strategy issues
We conducted a series of field and related modeling studies (2005-2012) to evaluate search strategies for Great Lakes coastal ecosystems that are at risk of invasion by non-native aquatic species. In developing a network, we should design to achieve an acceptable limit of detect...
Feedback strategies for visual search in airframe structural inspection.
Gramopadhye, A K; Drury, C G; Sharit, J
1997-05-01
Feedback of information has consistently shown positive results in human inspection, provided it is given in a timely and appropriate manner. Feedback serves as the basis of most training schemes; traditionally this has been performance feedback. Other forms of feedback which provide strategy information rather than performance information may have a role in improving inspection. This study compared performance feedback and cognitive feedback in a realistic simulation of an aircraft structural inspection task. Performance (time, errors) feedback showed the greatest improvements in performance measures. Cognitive feedback enhanced efficiency measures of search strategy. When cognitive feedback consisted of visual representations of the path and the coverage of the search sequence, subjects also were able to use this task information to improve their search performance.
Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
P. Sabarinath
2015-01-01
Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.
Optimization Under Uncertainty for Wake Steering Strategies
Energy Technology Data Exchange (ETDEWEB)
Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University
2017-08-03
Offsetting turbines' yaw orientations from incoming wind is a powerful tool that may be leveraged to reduce undesirable wake effects on downstream turbines. First, we examine a simple two-turbine case to gain intuition as to how inflow direction uncertainty affects the optimal solution. The turbines are modeled with unidirectional inflow such that one turbine directly wakes the other, using ten rotor diameter spacing. We perform optimization under uncertainty (OUU) via a parameter sweep of the front turbine. The OUU solution generally prefers less steering. We then do this optimization for a 60-turbine wind farm with unidirectional inflow, varying the degree of inflow uncertainty and approaching this OUU problem by nesting a polynomial chaos expansion uncertainty quantification routine within an outer optimization. We examined how different levels of uncertainty in the inflow direction effect the ratio of the expected values of deterministic and OUU solutions for steering strategies in the large wind farm, assuming the directional uncertainty used to reach said OUU solution (this ratio is defined as the value of the stochastic solution or VSS).
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
Wei Chen Esmonde Lim
2014-01-01
Full Text Available Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB, the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
Optimal defense strategy: storage vs. new production.
Shudo, Emi; Iwasa, Yoh
2002-12-07
If hosts produce defense proteins after they are infected by pathogens, it may take hours to days before defense becomes fully active. By producing defense proteins beforehand, and storing them until infection, the host can cope with pathogens with a short time delay. However, producing and storing defense proteins require energy, and the activated defense proteins often cause harm to the host's body as well as to pathogens. Here, we study the optimal strategy for a host who chooses the amount of stored defense proteins, the activation of the stored proteins upon infection, and the new production of the proteins. The optimal strategy is the one that minimizes the sum of the harm by pathogens and the cost of defense. The host chooses the storage size of defense proteins based on the probability distribution of the magnitude of pathogen infection. When the infection size is predictable, all the stored proteins are to be activated upon infection. The optimal strategy is to have no storage and to rely entirely on new production if the expected infection size n(0) is small, but to have a big storage without new production if n(0) is large. The transition from the "new production" phase to "storage" phase occurs at a smaller n(0) when storage cost is small, activation cost is large, pathogen toxicity is large, pathogen growth is fast, the defense is effective, the delay is long, and the infection is more likely. On the other hand, the storage size to produce for a large n(0) decreases with three cost parameters and the defense effectiveness, increases with the likelihood of infection, the toxicity and the growth rate of pathogens, and it is independent of the time delay. When infection size is much smaller than the expected size, some of the stored proteins may stay unused.
Salehi, Mojtaba; Bahreininejad, Ardeshir
2011-08-01
Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.
Solving the wind farm layout optimization problem using random search algorithm
DEFF Research Database (Denmark)
Feng, Ju; Shen, Wen Zhong
2015-01-01
is presented, which starts from an initial feasible layout and then improves the layout iteratively in the feasible solution space. It was first proposed in our previous study and improved in this study by adding some adaptive mechanisms. It can serve both as a refinement tool to improve an initial design......Wind farm (WF) layout optimization is to find the optimal positions of wind turbines (WTs) inside a WF, so as to maximize and/or minimize a single objective or multiple objectives, while satisfying certain constraints. In this work, a random search (RS) algorithm based on continuous formulation...... by expert guesses or other optimization methods, and as an optimization tool to find the optimal layout of WF with a certain number of WTs. A new strategy to evaluate layouts is also used, which can largely save the computation cost. This method is first applied to a widely studied ideal test problem...
Indian Academy of Sciences (India)
NOR AZLINA AB AZIZ; ZUWAIRIE IBRAHIM; MARIZAN MUBIN; SHAHDAN SUDIN
2017-07-01
An adaptive gravitational search algorithm (GSA) that switches between synchronous and asynchronous update is presented in this work. The proposed adaptive switching synchronous–asynchronous GSA (ASw-GSA) improves GSA through manipulation of its iteration strategy. The iteration strategy is switched from synchronous to asynchronous update and vice versa. The switching is conducted so that the population is adaptively switched between convergence and divergence. Synchronous update allows convergence, while switching to asynchronous update causes disruption to the population’s convergence.The ASw-GSA agents switch their iteration strategy when the best found solution is not improved after a period of time. The period is based on a switching threshold. The threshold determines how soon is the switching, and also the frequency of switching in ASw-GSA. ASw-GSA has been comprehensively evaluated based on CEC2014’s benchmark functions. The effect of the switchingthreshold has been studied and it is found that, in comparison with multiple and early switches, one-time switching towards the end of the search is better and substantially enhances the performance of ASw-GSA. The proposed ASw-GSA is also compared to original GSA, particle swarm optimization (PSO),genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). The statistical analysis results show that ASw-GSA performs significantly better than GA and BA and as well as PSO,the original GSA and GWO.12
Searching for pathways involving dressed states in optimal control theory.
von den Hoff, Philipp; Kowalewski, Markus; de Vivie-Riedle, Regina
2011-01-01
Selective population of dressed states has been proposed as an alternative control pathway in molecular reaction dynamics [Wollenhaupt et al., J. Photochem. Photobiol. A: Chem., 2006, 180, 248]. In this article we investigate if, and under which conditions, this strong field pathway is included in the search space of optimal control theory. For our calculations we used the proposed example of the potassium dimer, in which the different target states can be reached via dressed states by resonant transition. Especially, we investigate whether the optimization algorithm is able to find the route involving the dressed states although the target state lies out of resonance in the bare state picture.
Parallel Harmony Search Based Distributed Energy Resource Optimization
Energy Technology Data Exchange (ETDEWEB)
Ceylan, Oguzhan [ORNL; Liu, Guodong [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)
2015-01-01
This paper presents a harmony search based parallel optimization algorithm to minimize voltage deviations in three phase unbalanced electrical distribution systems and to maximize active power outputs of distributed energy resources (DR). The main contribution is to reduce the adverse impacts on voltage profile during a day as photovoltaics (PVs) output or electrical vehicles (EVs) charging changes throughout a day. The IEEE 123- bus distribution test system is modified by adding DRs and EVs under different load profiles. The simulation results show that by using parallel computing techniques, heuristic methods may be used as an alternative optimization tool in electrical power distribution systems operation.
An Elite Decision Making Harmony Search Algorithm for Optimization Problem
Directory of Open Access Journals (Sweden)
Lipu Zhang
2012-01-01
Full Text Available This paper describes a new variant of harmony search algorithm which is inspired by a well-known item “elite decision making.” In the new algorithm, the good information captured in the current global best and the second best solutions can be well utilized to generate new solutions, following some probability rule. The generated new solution vector replaces the worst solution in the solution set, only if its fitness is better than that of the worst solution. The generating and updating steps and repeated until the near-optimal solution vector is obtained. Extensive computational comparisons are carried out by employing various standard benchmark optimization problems, including continuous design variables and integer variables minimization problems from the literature. The computational results show that the proposed new algorithm is competitive in finding solutions with the state-of-the-art harmony search variants.
Multilevel Thresholding Segmentation Based on Harmony Search Optimization
Directory of Open Access Journals (Sweden)
Diego Oliva
2013-01-01
Full Text Available In this paper, a multilevel thresholding (MT algorithm based on the harmony search algorithm (HSA is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu’s or Kapur’s methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.
PubMed searches: overview and strategies for clinicians.
Lindsey, Wesley T; Olin, Bernie R
2013-04-01
PubMed is a biomedical and life sciences database maintained by a division of the National Library of Medicine known as the National Center for Biotechnology Information (NCBI). It is a large resource with more than 5600 journals indexed and greater than 22 million total citations. Searches conducted in PubMed provide references that are more specific for the intended topic compared with other popular search engines. Effective PubMed searches allow the clinician to remain current on the latest clinical trials, systematic reviews, and practice guidelines. PubMed continues to evolve by allowing users to create a customized experience through the My NCBI portal, new arrangements and options in search filters, and supporting scholarly projects through exportation of citations to reference managing software. Prepackaged search options available in the Clinical Queries feature also allow users to efficiently search for clinical literature. PubMed also provides information regarding the source journals themselves through the Journals in NCBI Databases link. This article provides an overview of the PubMed database's structure and features as well as strategies for conducting an effective search.
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
2014-09-01
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
Proximity search heuristics for wind farm optimal layout
DEFF Research Database (Denmark)
Fischetti, Martina; Monaci, Michele
2016-01-01
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-......-proposed proximity search paradigm. Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach....
Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution
Hongxue Yang; Lingling Xuan
2014-01-01
Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distr...
Global Optimization for Advertisement Selection in Sponsored Search
Institute of Scientific and Technical Information of China (English)
崔卿; 白峰杉; 高斌; 刘铁岩
2015-01-01
Advertisement (ad) selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad selection methods regard ad selection as a relatively independent module, and only consider the literal or semantic matching between queries and keywords during the ad selection process. In this paper, we argue that this approach is not globally optimal. Our proposal is to formulate ad selection as such an optimization problem that the selected ads can work together with downstream components (e.g., the auction mechanism) to achieve the maximization of user clicks, advertiser social welfare, and search engine revenue (we call the combination of these ob jective functions as the marketplace ob jective for ease of reference). To this end, we 1) extract a bunch of features to represent each pair of query and keyword, and 2) train a machine learning model that maps the features to a binary variable indicating whether the keyword is selected or not, by maximizing the aforementioned marketplace ob jective. This formalization seems quite natural; however, it is technically diﬃcult because the marketplace objective is non-convex, discontinuous, and indifferentiable regarding the model parameter due to the ranking and second-price rules in the auction mechanism. To tackle the challenge, we propose a probabilistic approximation of the marketplace objective, which is smooth and can be effectively optimized by conventional optimization techniques. We test the ad selection model learned with our proposed method using the sponsored search log from a commercial search engine. The experimental results show that our method can significantly outperform several ad selection algorithms on all the metrics under investigation.
Optimizing the search for transiting planets in long time series
Ofir, Aviv
2013-01-01
Context: Transit surveys, both ground- and space- based, have already accumulated a large number of light curves that span several years. Aims: The search for transiting planets in these long time series is computationally intensive. We wish to optimize the search for both detection and computational efficiencies. Methods: We assume that the searched systems can be well described by Keplerian orbits. We then propagate the effects of different system parameters to the detection parameters. Results: We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS one is either rather insensitive to long-period planets, or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3yr long dataset...
Cuckoo Search Optimization for Reduction of a Greenhouse Climate Model
Directory of Open Access Journals (Sweden)
Hasni Abdelhafid
2016-07-01
Full Text Available Greenhouse climate and crop models and specially reduced models are necessary for bettering environmental management and control ability. In this paper, we present a new metaheuristic method, called Cuckoo Search (CS algorithm, established on the life of a bird family for selecting the parameters of a reduced model which optimizes their choice by minimizing a cost function. The reduced model was already developed for control purposes and published in the literature. The proposed models target at simulating and predicting the greenhouse environment. [?]. This study focuses on the dynamical behaviors of the inside air temperature and pressure using ventilation. Some experimental results are used for model validation, the greenhouse being automated with actuators and sensors connected to a greenhouse control system on the cuckoo search methods to determine the best set of parameters allowing for the convergence of a criteria based on the difference between calculated and observed state variables (inside air temperature and water vapour pressure content. The results shown that the tested Cuckoo Search algorithm allows for a faster convergence towards the optimal solution than classical optimization methods.
A Nonhomogeneous Cuckoo Search Algorithm Based on Quantum Mechanism for Real Parameter Optimization.
Cheung, Ngaam J; Ding, Xue-Ming; Shen, Hong-Bin
2017-02-01
Cuckoo search (CS) algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this paper presents a new variant of CS algorithm with nonhomogeneous search strategies based on quantum mechanism to enhance search ability of the classical CS algorithm. Featured contributions in this paper include: 1) quantum-based strategy is developed for nonhomogeneous update laws and 2) we, for the first time, present a set of theoretical analyses on CS algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the CS algorithm and the proposed algorithm. On 24 benchmark functions, we compare our method with five existing CS-based methods and other ten state-of-the-art algorithms. The numerical results demonstrate that the proposed algorithm is significantly better than the original CS algorithm and the rest of compared methods according to two nonparametric tests.
Combined optimization model for sustainable energization strategy
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
Local Optimization Strategies in Urban Vehicular Mobility.
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Pierpaolo Mastroianni
Full Text Available The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints--physical, environmental, social, economic--that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.
An Improved Search Algorithm for Optimal Multiple-Sequence Alignment
Schroedl, S
2011-01-01
Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschbergs algorithm, Dynamic Programming needs O(kN^(k-1)) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at hig...
Searching for evidence: Knowledge and search strategies used by forensic scientists
Schraagen, J.M.C.; Leijenhorst, H.
2001-01-01
The Forensic Science Laboratory of The Netherlands is suffering from a growing backlog due to an increasing number of cases, which results in long delivery times of research reports within a number of departments. The project, "Strategies for Searching Trace Evidence," was started by the Forensic Sc
Perspective: n-type oxide thermoelectrics via visual search strategies
Directory of Open Access Journals (Sweden)
Guangzong Xing
2016-05-01
Full Text Available We discuss and present search strategies for finding new thermoelectric compositions based on first principles electronic structure and transport calculations. We illustrate them by application to a search for potential n-type oxide thermoelectric materials. This includes a screen based on visualization of electronic energy isosurfaces. We report compounds that show potential as thermoelectric materials along with detailed properties, including SrTiO3, which is a known thermoelectric, and appropriately doped KNbO3 and rutile TiO2.
Gravitation search algorithm: Application to the optimal IIR filter design
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Suman Kumar Saha
2014-01-01
Full Text Available This paper presents a global heuristic search optimization technique known as Gravitation Search Algorithm (GSA for the design of 8th order Infinite Impulse Response (IIR, low pass (LP, high pass (HP, band pass (BP and band stop (BS filters considering various non-linear characteristics of the filter design problems. This paper also adopts a novel fitness function in order to improve the stop band attenuation to a great extent. In GSA, law of gravity and mass interactions among different particles are adopted for handling the non-linear IIR filter design optimization problem. In this optimization technique, searcher agents are the collection of masses and interactions among them are governed by the Newtonian gravity and the laws of motion. The performances of the GSA based IIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA and standard Particle Swarm Optimization (PSO. Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
Optimization of Sensor Monitoring Strategies for Emissions
Klise, K. A.; Laird, C. D.; Downey, N.; Baker Hebert, L.; Blewitt, D.; Smith, G. R.
2016-12-01
Continuous or regularly scheduled monitoring has the potential to quickly identify changes in air quality. However, even with low-cost sensors, only a limited number of sensors can be placed to monitor airborne pollutants. The physical placement of these sensors and the sensor technology used can have a large impact on the performance of a monitoring strategy. Furthermore, sensors can be placed for different objectives, including maximum coverage, minimum time to detection or exposure, or to quantify emissions. Different objectives may require different monitoring strategies, which need to be evaluated by stakeholders before sensors are placed in the field. In this presentation, we outline methods to enhance ambient detection programs through optimal design of the monitoring strategy. These methods integrate atmospheric transport models with sensor characteristics, including fixed and mobile sensors, sensor cost and failure rate. The methods use site specific pre-computed scenarios which capture differences in meteorology, terrain, concentration averaging times, gas concentration, and emission characteristics. The pre-computed scenarios become input to a mixed-integer, stochastic programming problem that solves for sensor locations and types that maximize the effectiveness of the detection program. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.
Optimal allocation of trend following strategies
Grebenkov, Denis S.; Serror, Jeremy
2015-09-01
We consider a portfolio allocation problem for trend following (TF) strategies on multiple correlated assets. Under simplifying assumptions of a Gaussian market and linear TF strategies, we derive analytical formulas for the mean and variance of the portfolio return. We construct then the optimal portfolio that maximizes risk-adjusted return by accounting for inter-asset correlations. The dynamic allocation problem for n assets is shown to be equivalent to the classical static allocation problem for n2 virtual assets that include lead-lag corrections in positions of TF strategies. The respective roles of asset auto-correlations and inter-asset correlations are investigated in depth for the two-asset case and a sector model. In contrast to the principle of diversification suggesting to treat uncorrelated assets, we show that inter-asset correlations allow one to estimate apparent trends more reliably and to adjust the TF positions more efficiently. If properly accounted for, inter-asset correlations are not deteriorative but beneficial for portfolio management that can open new profit opportunities for trend followers. These concepts are illustrated using daily returns of three highly correlated futures markets: the E-mini S&P 500, Euro Stoxx 50 index, and the US 10-year T-note futures.
Search strategies on the Internet: general and specific.
Bottrill, Krys
2004-06-01
Some of the most up-to-date information on scientific activity is to be found on the Internet; for example, on the websites of academic and other research institutions and in databases of currently funded research studies provided on the websites of funding bodies. Such information can be valuable in suggesting new approaches and techniques that could be applicable in a Three Rs context. However, the Internet is a chaotic medium, not subject to the meticulous classification and organisation of classical information resources. At the same time, Internet search engines do not match the sophistication of search systems used by database hosts. Also, although some offer relatively advanced features, user awareness of these tends to be low. Furthermore, much of the information on the Internet is not accessible to conventional search engines, giving rise to the concept of the "Invisible Web". General strategies and techniques for Internet searching are presented, together with a comparative survey of selected search engines. The question of how the Invisible Web can be accessed is discussed, as well as how to keep up-to-date with Internet content and improve searching skills.
A novel adaptive Cuckoo search for optimal query plan generation.
Gomathi, Ramalingam; Sharmila, Dhandapani
2014-01-01
The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.
A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation
Directory of Open Access Journals (Sweden)
Ramalingam Gomathi
2014-01-01
Full Text Available The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C standard for storing semantic web data is the resource description framework (RDF. To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.
Signatures of active and passive optimized Lévy searching in jellyfish.
Reynolds, Andy M
2014-10-06
Some of the strongest empirical support for Lévy search theory has come from telemetry data for the dive patterns of marine predators (sharks, bony fishes, sea turtles and penguins). The dive patterns of the unusually large jellyfish Rhizostoma octopus do, however, sit outside of current Lévy search theory which predicts that a single search strategy is optimal. When searching the water column, the movement patterns of these jellyfish change over time. Movement bouts can be approximated by a variety of Lévy and Brownian (exponential) walks. The adaptive value of this variation is not known. On some occasions movement pattern data are consistent with the jellyfish prospecting away from a preferred depth, not finding an improvement in conditions elsewhere and so returning to their original depth. This 'bounce' behaviour also sits outside of current Lévy walk search theory. Here, it is shown that the jellyfish movement patterns are consistent with their using optimized 'fast simulated annealing'--a novel kind of Lévy walk search pattern--to locate the maximum prey concentration in the water column and/or to locate the strongest of many olfactory trails emanating from more distant prey. Fast simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a large search space. This new finding shows that the notion of active optimized Lévy walk searching is not limited to the search for randomly and sparsely distributed resources, as previously thought, but can be extended to embrace other scenarios, including that of the jellyfish R. octopus. In the presence of convective currents, it could become energetically favourable to search the water column by riding the convective currents. Here, it is shown that these passive movements can be represented accurately by Lévy walks of the type occasionally seen in R. octopus. This result vividly illustrates that Lévy walks are not necessarily
Food searching strategy of amoeboid cells by starvation induced run length extension.
Directory of Open Access Journals (Sweden)
Peter J M Van Haastert
Full Text Available Food searching strategies of animals are key to their success in heterogeneous environments. The optimal search strategy may include specialized random walks such as Levy walks with heavy power-law tail distributions, or persistent walks with preferred movement in a similar direction. We have investigated the movement of the soil amoebae Dictyostelium searching for food. Dictyostelium cells move by extending pseudopodia, either in the direction of the previous pseudopod (persistent step or in a different direction (turn. The analysis of approximately 4000 pseudopodia reveals that step and turn pseudopodia are drawn from a probability distribution that is determined by cGMP/PLA2 signaling pathways. Starvation activates these pathways thereby suppressing turns and inducing steps. As a consequence, starved cells make very long nearly straight runs and disperse over approximately 30-fold larger areas, without extending more or larger pseudopodia than vegetative cells. This 'win-stay/lose-shift' strategy for food searching is called Starvation Induced Run-length Extension. The SIRE walk explains very well the observed differences in search behavior between fed and starving organisms such as bumble-bees, flower bug, hoverfly and zooplankton.
Risky Arbitrage Strategies: Optimal Portfolio Choice and Economic Implications
Liu, Jun; Timmermann, Allan G
2009-01-01
We define risky arbitrages as self-financing trading strategies that have a strictly positive market price but a zero expected cumulative payoff. A continuous time cointegrated system is used to model risky arbitrages as arising from a mean-reverting mispricing component. We derive the optimal trading strategy in closed-form and show that the standard textbook arbitrage strategy is not optimal. In a calibration exercise, we show that the optimal strategy makes a sizeable difference in economi...
AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n—DIMENSIONAL SPACE
Institute of Scientific and Technical Information of China (English)
TangBin; HuGuangrui
2002-01-01
An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented,which encodes movement direction and distance and searches from coarse to precise.The algorithm can realize global optimization and improve the search efficiency,and can be applied effectively in industrial optimization ,data mining and pattern recognition.
AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n-DIMENSIONAL SPACE
Institute of Scientific and Technical Information of China (English)
Tang Bin; Hu Guangrui
2002-01-01
An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.
Optimal portfolio strategies under a shortfall constraint
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D Akuma
2009-06-01
Full Text Available We impose dynamically, a shortfall constraint in terms of Tail Conditional Expectation on the portfolio selection problem in continuous time, in order to obtain optimal strategies. The financial market is assumed to comprise n risky assets driven by geometric Brownian motion and one risk-free asset. The method of Lagrange multipliers is combined with the Hamilton-Jacobi-Bellman equation to insert the constraint into the resolution framework. The constraint is re-calculated at short intervals of time throughout the investment horizon. A numerical method is applied to obtain an approximate solution to the problem. It is found that the imposition of the constraint curbs investment in the risky assets.
Optimal state discrimination and unstructured search in nonlinear quantum mechanics
Childs, Andrew M.; Young, Joshua
2016-02-01
Nonlinear variants of quantum mechanics can solve tasks that are impossible in standard quantum theory, such as perfectly distinguishing nonorthogonal states. Here we derive the optimal protocol for distinguishing two states of a qubit using the Gross-Pitaevskii equation, a model of nonlinear quantum mechanics that arises as an effective description of Bose-Einstein condensates. Using this protocol, we present an algorithm for unstructured search in the Gross-Pitaevskii model, obtaining an exponential improvement over a previous algorithm of Meyer and Wong. This result establishes a limitation on the effectiveness of the Gross-Pitaevskii approximation. More generally, we demonstrate similar behavior under a family of related nonlinearities, giving evidence that the ability to quickly discriminate nonorthogonal states and thereby solve unstructured search is a generic feature of nonlinear quantum mechanics.
Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization
Energy Technology Data Exchange (ETDEWEB)
HART,WILLIAM E.
2000-06-01
The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework of EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.
Structural design optimization of vehicle components using Cuckoo Search Algorithm
Energy Technology Data Exchange (ETDEWEB)
Yildiz, Ali Riza [Bursa Technical Univ., Bursa (Turkey). Dept. of Mechanical Engineering; Durgun, Ismail
2012-07-01
In order to meet today's vehicle design requirements and to improve the cost and fuel efficiency, there is an increasing interest to design light-weight and cost-effective vehicle components. In this research, a new optimization algorithm, called the Cuckoo Search Algorithm (CS) algorithm, is introduced for solving structural design optimization problems. This research is the first application of the CS to the shape design optimization problems in the literature. The CS algorithm is applied to the structural design optimization of a vehicle component to illustrate how the present approach can be applied for solving structural design problems. Results show the ability of the CS to find better optimal structural design. [German] Um heutige Anforderungen an das Fahrzeugdesign zu beruecksichtigen und um die Kosten- und Kraftstoffeffektivitaet zu erhoehen, nimmt das Interesse am Design leichter und kosteneffektiver Fahrzeugkomponenten weiterhin zu. In der diesem Beitrag zugrunde liegenden Studie wurde ein neuer Optimierungsalgorithmus angewendet, der so genannte Cuckoo Suchalgorithmus (CS). Es handelt sich um die erste CS-Applikation fuer das Formdesign in der Literatur. Der CS-Algorithmus wird hierbei zur Strukturdesignoptimierung einer Fahrzeugkomponente angewendet, um zu zeigen, wie er bei der Loesung von Strukturdesignaufgaben angewendet werden kann. Die Ergebnisse zeigen, wie damit ein verbessertes Design erreicht werden kann.
Optimization of Equipment Maintenance Strategy Based on Availability
Institute of Scientific and Technical Information of China (English)
张友诚
2001-01-01
It is very important to optimize maintenance strategy in maintenance plan. Proper parameters play a decisive role for the optimization. In the opinion of writer, availability is a basic parameter, failure consequence cost and failure characteristic are also important parameters. Maintenance strategy can be optimized on the base by means of quantitative analysis and diagram.
Derivation of Optimal Cropping Pattern in Part of Hirakud Command using Cuckoo Search
Rath, Ashutosh; Biswal, Sudarsan; Samantaray, Sandeep; Swain, Prakash Chandra, PROF.
2017-08-01
The economicgrowth of a Nation depends on agriculture which relies on the obtainable water resources, available land and crops. The contribution of water in an appropriate quantity at appropriate time plays avitalrole to increase the agricultural production. Optimal utilization of available resources can be achieved by proper planning and management of water resources projects and adoption of appropriate technology. In the present work, the command area of Sambalpur distribrutary System is taken up for investigation. Further, adoption of a fixed cropping pattern causes the reduction of yield. The present study aims at developing different crop planning strategies to increase the net benefit from the command area with minimum investment. Optimization models are developed for Kharif season using LINDO and Cuckoo Search (CS) algorithm for maximization of the net benefits. In process of development of Optimization model the factors such as cultivable land, seeds, fertilizers, man power, water cost, etc. are taken as constraints. The irrigation water needs of major crops and the total available water through canals in the command of Sambalpur Distributary are estimated. LINDO and Cuckoo Search models are formulated and used to derive the optimal cropping pattern yielding maximum net benefits. The net benefits of Rs.585.0 lakhs in Kharif Season are obtained by adopting LINGO and 596.07 lakhs from Cuckoo Search, respectively, whereas the net benefits of 447.0 lakhs is received by the farmers of the locality with the adopting present cropping pattern.
Hasegawa, Manabu; Hiramatsu, Kotaro
2013-10-01
The effectiveness of the Metropolis algorithm (MA) (constant-temperature simulated annealing) in optimization by the method of search-space smoothing (SSS) (potential smoothing) is studied on two types of random traveling salesman problems. The optimization mechanism of this hybrid approach (MASSS) is investigated by analyzing the exploration dynamics observed in the rugged landscape of the cost function (energy surface). The results show that the MA can be successfully utilized as a local search algorithm in the SSS approach. It is also clarified that the optimization characteristics of these two constituent methods are improved in a mutually beneficial manner in the MASSS run. Specifically, the relaxation dynamics generated by employing the MA work effectively even in a smoothed landscape and more advantage is taken of the guiding function proposed in the idea of SSS; this mechanism operates in an adaptive manner in the de-smoothing process and therefore the MASSS method maintains its optimization function over a wider temperature range than the MA.
A Cooperative Coevolutionary Cuckoo Search Algorithm for Optimization Problem
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Hongqing Zheng
2013-01-01
Full Text Available Taking inspiration from an organizational evolutionary algorithm for numerical optimization, this paper designs a kind of dynamic population and combining evolutionary operators to form a novel algorithm, a cooperative coevolutionary cuckoo search algorithm (CCCS, for solving both unconstrained, constrained optimization and engineering problems. A population of this algorithm consists of organizations, and an organization consists of dynamic individuals. In experiments, fifteen unconstrained functions, eleven constrained functions, and two engineering design problems are used to validate the performance of CCCS, and thorough comparisons are made between the CCCS and the existing approaches. The results show that the CCCS obtains good performance in the solution quality. Moreover, for the constrained problems, the good performance is obtained by only incorporating a simple constraint handling technique into the CCCS. The results show that the CCCS is quite robust and easy to use.
Novel Back Propagation Optimization by Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
Jiao-hong Yi
2014-01-01
Full Text Available The traditional Back Propagation (BP has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS, called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN. Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.
Novel back propagation optimization by Cuckoo Search algorithm.
Yi, Jiao-hong; Xu, Wei-hong; Chen, Yuan-tao
2014-01-01
The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.
Optimizing Cryogenic Detectors for Low-Mass WIMP Searches
Arnaud, Q.; Billard, J.; Juillard, A.
2016-07-01
This paper describes the methodology and results from a study dedicated to the optimization of cryogenic detectors for low-mass WIMP searches. Considering a data-driven background model from the EDELWEISS-III experiment, and two analysis methods, namely profile likelihood and boosted decision tree, we indentify the main experimental constraints and performances that have to be improved. We found that there is a clear difference in how to optimize the detector setup whether focusing on WIMPs with masses below 5 GeV or above. Finally, in the case of a hundred-kg scale experiment, we discuss the requirements to probe most of the parameter space region delimited by the ultimate neutrino bound below 6 GeV.
Nonlinearly-constrained optimization using asynchronous parallel generating set search.
Energy Technology Data Exchange (ETDEWEB)
Griffin, Joshua D.; Kolda, Tamara Gibson
2007-05-01
Many optimization problems in computational science and engineering (CS&E) are characterized by expensive objective and/or constraint function evaluations paired with a lack of derivative information. Direct search methods such as generating set search (GSS) are well understood and efficient for derivative-free optimization of unconstrained and linearly-constrained problems. This paper addresses the more difficult problem of general nonlinear programming where derivatives for objective or constraint functions are unavailable, which is the case for many CS&E applications. We focus on penalty methods that use GSS to solve the linearly-constrained problems, comparing different penalty functions. A classical choice for penalizing constraint violations is {ell}{sub 2}{sup 2}, the squared {ell}{sub 2} norm, which has advantages for derivative-based optimization methods. In our numerical tests, however, we show that exact penalty functions based on the {ell}{sub 1}, {ell}{sub 2}, and {ell}{sub {infinity}} norms converge to good approximate solutions more quickly and thus are attractive alternatives. Unfortunately, exact penalty functions are discontinuous and consequently introduce theoretical problems that degrade the final solution accuracy, so we also consider smoothed variants. Smoothed-exact penalty functions are theoretically attractive because they retain the differentiability of the original problem. Numerically, they are a compromise between exact and {ell}{sub 2}{sup 2}, i.e., they converge to a good solution somewhat quickly without sacrificing much solution accuracy. Moreover, the smoothing is parameterized and can potentially be adjusted to balance the two considerations. Since many CS&E optimization problems are characterized by expensive function evaluations, reducing the number of function evaluations is paramount, and the results of this paper show that exact and smoothed-exact penalty functions are well-suited to this task.
Zhao, Shi-Zheng; Suganthan, Ponnuthurai Nagaratnam; Das, Swagatam
In order to solve large scale continuous optimization problems, Self-adaptive DE (SaDE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS). The JADE mutation strategy, the "DE/current-to-pbest" which is a variation of the classic "DE/current-to-best", is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared, consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 20 numerical optimization problems for the CEC'2010 Special Session and Competition on Large Scale Global Optimization and competitive results are presented.
Job-Search Strategies and Reemployment Quality: The Impact of Career Adaptability
Koen, Jessie; Klehe, Ute-Christine; Van Vianen, Annelies E. M.; Zikic, Jelena; Nauta, Aukje
2010-01-01
Past job-search research has focused on how hard unemployed people search for a job, but we still know little about the strategies that people use during their search and how we can predict the quality of the reemployment found. The first aim of this study was to predict the use of different job-search strategies via job-seekers' career…
Job-Search Strategies and Reemployment Quality: The Impact of Career Adaptability
Koen, Jessie; Klehe, Ute-Christine; Van Vianen, Annelies E. M.; Zikic, Jelena; Nauta, Aukje
2010-01-01
Past job-search research has focused on how hard unemployed people search for a job, but we still know little about the strategies that people use during their search and how we can predict the quality of the reemployment found. The first aim of this study was to predict the use of different job-search strategies via job-seekers' career…
Galaxy Strategy for Ligo-Virgo Gravitational Wave Counterpart Searches
Gehrels, Neil; Cannizzo, John K.; Kanner, Jonah; Kasliwal, Mansi M.; Nissanke, Samaya; Singer, Leo P.
2016-01-01
In this work we continue a line of inquiry begun in Kanner et al. which detailed a strategy for utilizing telescopes with narrow fields of view, such as the Swift X-Ray Telescope (XRT), to localize gravity wave (GW) triggers from LIGO (Laser Interferometer Gravitational-Wave Observatory) / Virgo. If one considers the brightest galaxies that produce 50 percent of the light, then the number of galaxies inside typical GW error boxes will be several tens. We have found that this result applies both in the early years of Advanced LIGO when the range is small and the error boxes large, and in the later years when the error boxes will be small and the range large. This strategy has the beneficial property of reducing the number of telescope pointings by a factor 10 to 100 compared with tiling the entire error box. Additional galaxy count reduction will come from a GW rapid distance estimate which will restrict the radial slice in search volume. Combining the bright galaxy strategy with a convolution based on anticipated GW localizations, we find that the searches can be restricted to about 18 plus or minus 5 galaxies for 2015, about 23 plus or minus 4 for 2017, and about 11 plus or minus for 2020. This assumes a distance localization at the putative neutron star-neutron star (NS-NS) merger range mu for each target year, and these totals are integrated out to the range. Integrating out to the horizon would roughly double the totals. For localizations with r (rotation) greatly less than mu the totals would decrease. The galaxy strategy we present in this work will enable numerous sensitive optical and X-ray telescopes with small fields of view to participate meaningfully in searches wherein the prospects for rapidly fading afterglow place a premium on a fast response time.
Improving Data Transfer Throughput with Direct Search Optimization
Energy Technology Data Exchange (ETDEWEB)
Balaprakash, Prasanna; Morozov, Vitali; Kettimuthu, Rajkumar; Kumaran, Kalyan; Foster, Ian
2016-01-01
Improving data transfer throughput over high-speed long-distance networks has become increasingly difficult. Numerous factors such as nondeterministic congestion, dynamics of the transfer protocol, and multiuser and multitask source and destination endpoints, as well as interactions among these factors, contribute to this difficulty. A promising approach to improving throughput consists in using parallel streams at the application layer.We formulate and solve the problem of choosing the number of such streams from a mathematical optimization perspective. We propose the use of direct search methods, a class of easy-to-implement and light-weight mathematical optimization algorithms, to improve the performance of data transfers by dynamically adapting the number of parallel streams in a manner that does not require domain expertise, instrumentation, analytical models, or historic data. We apply our method to transfers performed with the GridFTP protocol, and illustrate the effectiveness of the proposed algorithm when used within Globus, a state-of-the-art data transfer tool, on productionWAN links and servers. We show that when compared to user default settings our direct search methods can achieve up to 10x performance improvement under certain conditions. We also show that our method can overcome performance degradation due to external compute and network load on source end points, a common scenario at high performance computing facilities.
Adaptive symbiotic organisms search (SOS algorithm for structural design optimization
Directory of Open Access Journals (Sweden)
Ghanshyam G. Tejani
2016-07-01
Full Text Available The symbiotic organisms search (SOS algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.
A Cascade Optimization Strategy for Solution of Difficult Multidisciplinary Design Problems
Patnaik, Surya N.; Coroneos, Rula M.; Hopkins, Dale A.; Berke, Laszlo
1996-01-01
A research project to comparatively evaluate 10 nonlinear optimization algorithms was recently completed. A conclusion was that no single optimizer could successfully solve all 40 problems in the test bed, even though most optimizers successfully solved at least one-third of the problems. We realized that improved search directions and step lengths, available in the 10 optimizers compared, were not likely to alleviate the convergence difficulties. For the solution of those difficult problems we have devised an alternative approach called cascade optimization strategy. The cascade strategy uses several optimizers, one followed by another in a specified sequence, to solve a problem. A pseudorandom scheme perturbs design variables between the optimizers. The cascade strategy has been tested successfully in the design of supersonic and subsonic aircraft configurations and air-breathing engines for high-speed civil transport applications. These problems could not be successfully solved by an individual optimizer. The cascade optimization strategy, however, generated feasible optimum solutions for both aircraft and engine problems. This paper presents the cascade strategy and solutions to a number of these problems.
Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems
Institute of Scientific and Technical Information of China (English)
Xiao-Min Hu; Jun Zhang; Yun Li
2008-01-01
Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -- API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.
Researching Native Americans: Tips on Vocabulary, Search Strategies and Internet Resources.
Mueller-Alexander, Jeanette M.; Seaton, Helen J.
1994-01-01
Describes search strategies and vocabulary selection for researching information on Native Americans using DIALOG databases as examples. Highlights include tribal names search strategy; examples of false drops; revised search strategies for full-text databases; Internet sources, including various software systems; and CD-ROM products about Native…
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2014-01-01
Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.
Asynchronous parallel generating set search for linearly-constrained optimization.
Energy Technology Data Exchange (ETDEWEB)
Kolda, Tamara G.; Griffin, Joshua; Lewis, Robert Michael
2007-04-01
We describe an asynchronous parallel derivative-free algorithm for linearly-constrained optimization. Generating set search (GSS) is the basis of ourmethod. At each iteration, a GSS algorithm computes a set of search directionsand corresponding trial points and then evaluates the objective function valueat each trial point. Asynchronous versions of the algorithm have been developedin the unconstrained and bound-constrained cases which allow the iterations tocontinue (and new trial points to be generated and evaluated) as soon as anyother trial point completes. This enables better utilization of parallel resourcesand a reduction in overall runtime, especially for problems where the objec-tive function takes minutes or hours to compute. For linearly-constrained GSS,the convergence theory requires that the set of search directions conform to the3 nearby boundary. The complexity of developing the asynchronous algorithm forthe linearly-constrained case has to do with maintaining a suitable set of searchdirections as the search progresses and is the focus of this research. We describeour implementation in detail, including how to avoid function evaluations bycaching function values and using approximate look-ups. We test our imple-mentation on every CUTEr test problem with general linear constraints and upto 1000 variables. Without tuning to individual problems, our implementationwas able to solve 95% of the test problems with 10 or fewer variables, 75%of the problems with 11-100 variables, and nearly half of the problems with100-1000 variables. To the best of our knowledge, these are the best resultsthat have ever been achieved with a derivative-free method. Our asynchronousparallel implementation is freely available as part of the APPSPACK software.4
Galaxy Strategy for LIGO-Virgo Gravitational Wave Counterpart Searches
Gehrels, Neil; Kanner, Jonah; Kasliwal, Mansi M; Nissanke, Samaya; Singer, Leo P
2015-01-01
In this work we continue a line of inquiry begun in Kanner et al. which detailed a strategy for utilizing telescopes with narrow fields of view, such as the Swift X-ray Telescope (XRT), to localize gravity wave (GW) triggers from LIGO/Virgo. If one considers the brightest galaxies that produce ~50% of the light, then the number of galaxies inside typical GW error boxes will be several tens. We have found that this result applies both in the early years of Advanced LIGO when the range is small and the error boxes large, and in the later years when the error boxes will be small and the range large. This strategy has the beneficial property of reducing the number of telescope pointings by a factor 10 to 100 compared with tiling the entire error box. Additional galaxy count reduction will come from a GW rapid distance estimate which will restrict the radial slice in search volume. Combining the bright galaxy strategy with a convolution based on anticipated GW localizations, we find that the searches can be restri...
面向Google搜索引擎的优化技术%Optimization Techniques of Google Oriented Search Engine
Institute of Scientific and Technical Information of China (English)
陆军; 杨德仁
2011-01-01
针对Google搜索引擎,提出了网站优化策略.从导航优化、链接优化和页面优化等角度进行了分析.本文有益于网站的初级优化.%On the basis of Google search engine, website strategies were proposed.The optimizations of navigation, link and page were analyzed which are useful to primary optimization of web site.
Optimal restructuring strategies under various dynamic factors
Institute of Scientific and Technical Information of China (English)
MENG Qing-xuan
2007-01-01
Corporate restructuring was identified as a new industrial force that has great impact on economic values and that therefore has become central in daily financial decision making. This article investigates the optimal restructuring strategies under different dynamic factors and their numerous impacts on firm value. The concept of quasi-leverage is introduced and valuation models are built for corporate debt and equity under imperfect market conditions. The model's input variables include the quasi-leverage and other firm-specific parameters, the output variables include multiple corporate security values. The restructuring cost is formulated in the form of exponential function, which allows us to observe the sensitivity of the variation in security values. The unified model and its analytical solution developed in this research allow us to examine the continuous changes of security values by dynamically changing the coupon rates, riskless interest rate, bankruptcy cost, quasi-leverage, personal tax rate, corporate taxes rate, transaction cost, firm risk, etc., so that the solutions provide useful guidance for financing and restructuring decisions.
Mesh refinement strategy for optimal control problems
Paiva, L. T.; Fontes, F. A. C. C.
2013-10-01
Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform nodes collocation. In the method presented in this paper, a time mesh refinement strategy based on the local error is developed. After computing a solution in a coarse mesh, the local error is evaluated, which gives information about the subintervals of time domain where refinement is needed. This procedure is repeated until the local error reaches a user-specified threshold. The technique is applied to solve the car-like vehicle problem aiming minimum consumption. The approach developed in this paper leads to results with greater accuracy and yet with lower overall computational time as compared to using a time meshes having equidistant spacing.
Prosumers strategy for DHC energy flow optimization
Directory of Open Access Journals (Sweden)
Vasek Lubomir
2016-01-01
Full Text Available This article introduces the proposal of discrete model of district heating and cooling system (DHC for energy flow optimization. The aim is to achieve the best solution of the objective function, usually determined by minimizing the production and distribution costs and providing meets the needs of energy consumers. The model also introduces the idea of general prosumers strategy, where all active elements within the modern DHC system are representing by prosumers object. The prosumers are perceived as objects able to actively participate in the planning of production and consumption of energy. It is assumed that the general behaviour of the object in DHC is the same, no matter how they differ in sizes and designs. Thus, all the objects are defined by two characteristics - the ability to produce and consume. The model based on this basic principle, of course, with the most accurate information about the particular values at a time, object properties and other, should provide tools for simulation and control of modern DHC, possibly superior units as Smart Energy Grids - understood as a system integrating Smart Grids (electricity and Smart Thermal Grids (heat a cool.
Fractional particle swarm optimization in multidimensional search space.
Kiranyaz, Serkan; Ince, Turker; Yildirim, Alper; Gabbouj, Moncef
2010-04-01
In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better "guide" than the PSO's native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem.
Wolf, Stephan; Nicholls, Elizabeth; Reynolds, Andrew M.; Wells, Patricia; Lim, Ka S.; Paxton, Robert J.; Osborne, Juliet L.
2016-01-01
Lévy flights are scale-free (fractal) search patterns found in a wide range of animals. They can be an advantageous strategy promoting high encounter rates with rare cues that may indicate prey items, mating partners or navigational landmarks. The robustness of this behavioural strategy to ubiquitous threats to animal performance, such as pathogens, remains poorly understood. Using honeybees radar-tracked during their orientation flights in a novel landscape, we assess for the first time how two emerging infectious diseases (Nosema sp. and the Varroa-associated Deformed wing virus (DWV)) affect bees’ behavioural performance and search strategy. Nosema infection, unlike DWV, affected the spatial scale of orientation flights, causing significantly shorter and more compact flights. However, in stark contrast to disease-dependent temporal fractals, we find the same prevalence of optimal Lévy flight characteristics (μ ≈ 2) in both healthy and infected bees. We discuss the ecological and evolutionary implications of these surprising insights, arguing that Lévy search patterns are an emergent property of fundamental characteristics of neuronal and sensory components of the decision-making process, making them robust against diverse physiological effects of pathogen infection and possibly other stressors. PMID:27615605
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
Directory of Open Access Journals (Sweden)
Wee Loon Lim
2016-01-01
Full Text Available The quadratic assignment problem (QAP is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO, a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
Optimality of feedback control strategies for qubit purification
Wiseman, Howard M.; Bouten, Luc
2007-01-01
Recently two papers [K. Jacobs, Phys. Rev. A {\\bf 67}, 030301(R) (2003); H. M. Wiseman and J. F. Ralph, New J. Physics {\\bf 8}, 90 (2006)] have derived control strategies for rapid purification of qubits, optimized with respect to various goals. In the former paper the proof of optimality was not mathematically rigorous, while the latter gave only heuristic arguments for optimality. In this paper we provide rigorous proofs of optimality in all cases, by applying simple concepts from optimal c...
Optimization strategies for discrete multi-material stiffness optimization
DEFF Research Database (Denmark)
Hvejsel, Christian Frier; Lund, Erik; Stolpe, Mathias
2011-01-01
Design of composite laminated lay-ups are formulated as discrete multi-material selection problems. The design problem can be modeled as a non-convex mixed-integer optimization problem. Such problems are in general only solvable to global optimality for small to moderate sized problems. To attack...... larger problem instances we formulate convex and non-convex continuous relaxations which can be solved using gradient based optimization algorithms. The convex relaxation yields a lower bound on the attainable performance. The optimal solution to the convex relaxation is used as a starting guess...
Developing an Integrated Design Strategy for Chip Layout Optimization
Wits, Wessel Willems; Jauregui Becker, Juan Manuel; van Vliet, Frank Edward; te Riele, G.J.
2011-01-01
This paper presents an integrated design strategy for chip layout optimization. The strategy couples both electric and thermal aspects during the conceptual design phase to improve chip performances; thermal management being one of the major topics. The layout of the chip circuitry is optimized acco
Optimizing metapopulation sustainability through a checkerboard strategy.
Zion, Yossi Ben; Yaari, Gur; Shnerb, Nadav M
2010-01-22
The persistence of a spatially structured population is determined by the rate of dispersal among habitat patches. If the local dynamic at the subpopulation level is extinction-prone, the system viability is maximal at intermediate connectivity where recolonization is allowed, but full synchronization that enables correlated extinction is forbidden. Here we developed and used an algorithm for agent-based simulations in order to study the persistence of a stochastic metapopulation. The effect of noise is shown to be dramatic, and the dynamics of the spatial population differs substantially from the predictions of deterministic models. This has been validated for the stochastic versions of the logistic map, the Ricker map and the Nicholson-Bailey host-parasitoid system. To analyze the possibility of extinction, previous studies were focused on the attractiveness (Lyapunov exponent) of stable solutions and the structure of their basin of attraction (dependence on initial population size). Our results suggest that these features are of secondary importance in the presence of stochasticity. Instead, optimal sustainability is achieved when decoherence is maximal. Individual-based simulations of metapopulations of different sizes, dimensions and noise types, show that the system's lifetime peaks when it displays checkerboard spatial patterns. This conclusion is supported by the results of a recently published Drosophila experiment. The checkerboard strategy provides a technique for the manipulation of migration rates (e.g., by constructing corridors) in order to affect the persistence of a metapopulation. It may be used in order to minimize the risk of extinction of an endangered species, or to maximize the efficiency of an eradication campaign.
Optimizing metapopulation sustainability through a checkerboard strategy.
Directory of Open Access Journals (Sweden)
Yossi Ben Zion
2010-01-01
Full Text Available The persistence of a spatially structured population is determined by the rate of dispersal among habitat patches. If the local dynamic at the subpopulation level is extinction-prone, the system viability is maximal at intermediate connectivity where recolonization is allowed, but full synchronization that enables correlated extinction is forbidden. Here we developed and used an algorithm for agent-based simulations in order to study the persistence of a stochastic metapopulation. The effect of noise is shown to be dramatic, and the dynamics of the spatial population differs substantially from the predictions of deterministic models. This has been validated for the stochastic versions of the logistic map, the Ricker map and the Nicholson-Bailey host-parasitoid system. To analyze the possibility of extinction, previous studies were focused on the attractiveness (Lyapunov exponent of stable solutions and the structure of their basin of attraction (dependence on initial population size. Our results suggest that these features are of secondary importance in the presence of stochasticity. Instead, optimal sustainability is achieved when decoherence is maximal. Individual-based simulations of metapopulations of different sizes, dimensions and noise types, show that the system's lifetime peaks when it displays checkerboard spatial patterns. This conclusion is supported by the results of a recently published Drosophila experiment. The checkerboard strategy provides a technique for the manipulation of migration rates (e.g., by constructing corridors in order to affect the persistence of a metapopulation. It may be used in order to minimize the risk of extinction of an endangered species, or to maximize the efficiency of an eradication campaign.
Optimizing Vetoes for Gravitational-wave Transient Searches
Essick, R.; Blackburn, Lindy L.; Katsavounidis, E.
2014-01-01
Interferometric gravitational-wave detectors like LIGO, GEO600 and Virgo record a surplus of information above and beyond possible gravitational-wave events. These auxiliary channels capture information about the state of the detector and its surroundings which can be used to infer potential terrestrial noise sources of some gravitational-wave-like events. We present an algorithm addressing the ordering (or equivalently optimizing) of such information from auxiliary systems in gravitational-wave detectors to establish veto conditions in searches for gravitational-wave transients. The procedure was used to identify vetoes for searches for unmodelled transients by the LIGO and Virgo collaborations during their science runs from 2005 through 2007. In this work we present the details of the algorithm; we also use a limited amount of data from LIGO's past runs in order to examine the method, compare it with other methods, and identify its potential to characterize the instruments themselves. We examine the dependence of Receiver Operating Characteristic curves on the various parameters of the veto method and the implementation on real data. We find that the method robustly determines important auxiliary channels, ordering them by the apparent strength of their correlations to the gravitational-wave channel. This list can substantially reduce the background of noise events in the gravitational-wave data. In this way it can identify the source of glitches in the detector as well as assist in establishing confidence in the detection of gravitational-wave transients.
Measuring the Utilization of On-Page Search Engine Optimization in Selected Domain
National Research Council Canada - National Science Library
Goran Matošević
2015-01-01
Search engine optimization (SEO) techniques involve „on-page“ and „off-page“ actions taken by web developers and SEO specialists with aim to increase the ranking of web pages in search engine results pages (SERP...
Research on Design Optimization Strategy in Virtual Product Development
Institute of Scientific and Technical Information of China (English)
潘军; 韩帮军; 范秀敏; 马登哲
2004-01-01
Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.
Optimal vaccination strategies and rational behaviour in seasonal epidemics.
Doutor, Paulo; Rodrigues, Paula; Soares, Maria do Céu; Chalub, Fabio A C C
2016-12-01
We consider a SIRS model with time dependent transmission rate. We assume time dependent vaccination which confers the same immunity as natural infection. We study two types of vaccination strategies: (i) optimal vaccination, in the sense that it minimizes the effort of vaccination in the set of vaccination strategies for which, for any sufficiently small perturbation of the disease free state, the number of infectious individuals is monotonically decreasing; (ii) Nash-equilibria strategies where all individuals simultaneously minimize the joint risk of vaccination versus the risk of the disease. The former case corresponds to an optimal solution for mandatory vaccinations, while the second corresponds to the equilibrium to be expected if vaccination is fully voluntary. We are able to show the existence of both optimal and Nash strategies in a general setting. In general, these strategies will not be functions but Radon measures. For specific forms of the transmission rate, we provide explicit formulas for the optimal and the Nash vaccination strategies.
Harmony search optimization algorithm for a novel transportation problem in a consolidation network
Davod Hosseini, Seyed; Akbarpour Shirazi, Mohsen; Taghi Fatemi Ghomi, Seyed Mohammad
2014-11-01
This article presents a new harmony search optimization algorithm to solve a novel integer programming model developed for a consolidation network. In this network, a set of vehicles is used to transport goods from suppliers to their corresponding customers via two transportation systems: direct shipment and milk run logistics. The objective of this problem is to minimize the total shipping cost in the network, so it tries to reduce the number of required vehicles using an efficient vehicle routing strategy in the solution approach. Solving several numerical examples confirms that the proposed solution approach based on the harmony search algorithm performs much better than CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for realistic size problem instances.
McKeever, Liam; Nguyen, Van; Peterson, Sarah J; Gomez-Perez, Sandra; Braunschweig, Carol
2015-08-01
A thorough review of the literature is the basis of all research and evidence-based practice. A gold-standard efficient and exhaustive search strategy is needed to ensure all relevant citations have been captured and that the search performed is reproducible. The PubMed database comprises both the MEDLINE and non-MEDLINE databases. MEDLINE-based search strategies are robust but capture only 89% of the total available citations in PubMed. The remaining 11% include the most recent and possibly relevant citations but are only searchable through less efficient techniques. An effective search strategy must employ both the MEDLINE and the non-MEDLINE portion of PubMed to ensure all studies have been identified. The robust MEDLINE search strategies are used for the MEDLINE portion of the search. Usage of the less robust strategies is then efficiently confined to search only the remaining 11% of PubMed citations that have not been indexed for MEDLINE. The current article offers step-by-step instructions for building such a search exploring methods for the discovery of medical subject heading (MeSH) terms to search MEDLINE, text-based methods for exploring the non-MEDLINE database, information on the limitations of convenience algorithms such as the "related citations feature," the strengths and pitfalls associated with commonly used filters, the proper usage of Boolean operators to organize a master search strategy, and instructions for automating that search through "MyNCBI" to receive search query updates by email as new citations become available.
Parallel Guided Local Search and Some Preliminary Experimental Results for Continuous Optimization
Directory of Open Access Journals (Sweden)
Nasser Tairan
2014-02-01
Full Text Available This paper proposes a Parallel Guided Local Search (PGLS framework for continuous optimization. In PGLS, several guided local search (GLS procedures (agents are run for solving the optimization problem. The agents exchan ge information for speeding up the search. For example, the information exchanged could be kno wledge about the landscape obtained by the agents. The proposed algorithm is applied to co ntinuous optimization problems. The preliminary experimental results show that the algo rithm is very promising .
An optimal replication strategy for data grid systems
Institute of Scientific and Technical Information of China (English)
JIANG Jianjin; YANG Guangwen
2007-01-01
Data access latency is an important metric of system performance in data grid.By means of efficient replication strategy,the amount of data transferred in a wide area network will decrease,and the average access latency of data will decrease ultimately.The motivation of our research is to solve the optimized replica distribution problem in a data grid;that is,the system should utilize many replicas for every data with storage constraints to minimize the average access latency of data.This paper proposes a model of replication strategy in federated data grid and gives the optimized solution.The analysis results and simulation results show that the optimized replication strategy proposed in this paper is superior to LRU caching strategy,uniform replication strategy,proportional replication strategy and square root replication strategy in terms of wide area network bandwidth requirement and in the average access latency of data.
An optimal tuning strategy for tidal turbines
Vennell, Ross
2016-11-01
Tuning wind and tidal turbines is critical to maximizing their power output. Adopting a wind turbine tuning strategy of maximizing the output at any given time is shown to be an extremely poor strategy for large arrays of tidal turbines in channels. This `impatient-tuning strategy' results in far lower power output, much higher structural loads and greater environmental impacts due to flow reduction than an existing `patient-tuning strategy' which maximizes the power output averaged over the tidal cycle. This paper presents a `smart patient tuning strategy', which can increase array output by up to 35% over the existing strategy. This smart strategy forgoes some power generation early in the half tidal cycle in order to allow stronger flows to develop later in the cycle. It extracts enough power from these stronger flows to produce more power from the cycle as a whole than the existing strategy. Surprisingly, the smart strategy can often extract more power without increasing maximum structural loads on the turbines, while also maintaining stronger flows along the channel. This paper also shows that, counterintuitively, for some tuning strategies imposing a cap on turbine power output to limit loads can increase a turbine's average power output.
An optimal tuning strategy for tidal turbines.
Vennell, Ross
2016-11-01
Tuning wind and tidal turbines is critical to maximizing their power output. Adopting a wind turbine tuning strategy of maximizing the output at any given time is shown to be an extremely poor strategy for large arrays of tidal turbines in channels. This 'impatient-tuning strategy' results in far lower power output, much higher structural loads and greater environmental impacts due to flow reduction than an existing 'patient-tuning strategy' which maximizes the power output averaged over the tidal cycle. This paper presents a 'smart patient tuning strategy', which can increase array output by up to 35% over the existing strategy. This smart strategy forgoes some power generation early in the half tidal cycle in order to allow stronger flows to develop later in the cycle. It extracts enough power from these stronger flows to produce more power from the cycle as a whole than the existing strategy. Surprisingly, the smart strategy can often extract more power without increasing maximum structural loads on the turbines, while also maintaining stronger flows along the channel. This paper also shows that, counterintuitively, for some tuning strategies imposing a cap on turbine power output to limit loads can increase a turbine's average power output.
Optimal vaccination schedule search using genetic algorithm over MPI technology
Directory of Open Access Journals (Sweden)
Calonaci Cristiano
2012-11-01
Full Text Available Abstract Background Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. Methods To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. Results & Conclusions The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented.
Stochastic search, optimization and regression with energy applications
Hannah, Lauren A.
Designing clean energy systems will be an important task over the next few decades. One of the major roadblocks is a lack of mathematical tools to economically evaluate those energy systems. However, solutions to these mathematical problems are also of interest to the operations research and statistical communities in general. This thesis studies three problems that are of interest to the energy community itself or provide support for solution methods: R&D portfolio optimization, nonparametric regression and stochastic search with an observable state variable. First, we consider the one stage R&D portfolio optimization problem to avoid the sequential decision process associated with the multi-stage. The one stage problem is still difficult because of a non-convex, combinatorial decision space and a non-convex objective function. We propose a heuristic solution method that uses marginal project values---which depend on the selected portfolio---to create a linear objective function. In conjunction with the 0-1 decision space, this new problem can be solved as a knapsack linear program. This method scales well to large decision spaces. We also propose an alternate, provably convergent algorithm that does not exploit problem structure. These methods are compared on a solid oxide fuel cell R&D portfolio problem. Next, we propose Dirichlet Process mixtures of Generalized Linear Models (DPGLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate. We also give examples for when those conditions hold, including models for compactly supported continuous distributions and a model with continuous covariates and categorical response. We empirically analyze the properties of the DP-GLM and why it provides better results than existing Dirichlet process mixture regression
Optimal relocation strategies for spatially mobile consumers
Iordanov, Iordan
2007-01-01
We develop a model of the behaviour of a dynamically optimizing economic agent who makes consumption-saving and spatial relocation decisions. We formulate an existence result for the model, derive the necessary conditions for optimality and study the behaviour of the economic agent, focusing on the case of a wage distribution with a single maximum.
Strategies for Optimal Design of Structural Systems
DEFF Research Database (Denmark)
Enevoldsen, I.; Sørensen, John Dalsgaard
1992-01-01
Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...... problems are described. Numerical tests indicate that a sequential technique called the bounds iteration method (BIM) is particularly fast and stable....
Accelerated Simplified Swarm Optimization with Exploitation Search Scheme for Data Clustering.
Directory of Open Access Journals (Sweden)
Wei-Chang Yeh
Full Text Available Data clustering is commonly employed in many disciplines. The aim of clustering is to partition a set of data into clusters, in which objects within the same cluster are similar and dissimilar to other objects that belong to different clusters. Over the past decade, the evolutionary algorithm has been commonly used to solve clustering problems. This study presents a novel algorithm based on simplified swarm optimization, an emerging population-based stochastic optimization approach with the advantages of simplicity, efficiency, and flexibility. This approach combines variable vibrating search (VVS and rapid centralized strategy (RCS in dealing with clustering problem. VVS is an exploitation search scheme that can refine the quality of solutions by searching the extreme points nearby the global best position. RCS is developed to accelerate the convergence rate of the algorithm by using the arithmetic average. To empirically evaluate the performance of the proposed algorithm, experiments are examined using 12 benchmark datasets, and corresponding results are compared with recent works. Results of statistical analysis indicate that the proposed algorithm is competitive in terms of the quality of solutions.
Directory of Open Access Journals (Sweden)
Guangyu Chen
2014-01-01
Full Text Available An improved differential evolution (DE method based on the dynamic search strategy (IDEBDSS is proposed to solve dynamic economic dispatch problem with valve-point effects in this paper. The proposed method combines the DE algorithm with the dynamic search strategy, which improves the performance of the algorithm. DE is the main optimizer in the method proposed. While chaotic sequences are applied to obtain the dynamic parameter settings in DE, dynamic search strategy which consists of two steps, global search strategy and local search strategy, is used to improve algorithm efficiency. To accelerate convergence, a new infeasible solution handing method is adopted in the local search strategy; meanwhile, an orthogonal crossover (OX operator is added to the global search strategy to enhance the optimization search ability. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by three test systems, and the simulation results reveal that the IDEBDSS method can obtain better solutions with higher efficiency than the standard DE and other methods reported in the recent literature.
Tuo, Shouheng; Yong, Longquan; Deng, Fang’an; Li, Yanhai; Lin, Yong; Lu, Qiuju
2017-01-01
Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application. PMID:28403224
Finding current evidence: search strategies and common databases.
Gillespie, Lesley Diane; Gillespie, William John
2003-08-01
With more than 100 orthopaedic, sports medicine, or hand surgery journals indexed in MEDLINE, it is no longer possible to keep abreast of developments in orthopaedic surgery by reading a few journals each month. Electronic resources are easier to search and more current than most print sources. We provide a practical approach to finding useful information to guide orthopaedic practice. We focus first on where to find the information by providing details about many useful databases and web links. Sources for identifying guidelines, systematic reviews, and randomized controlled trials are identified. The second section discusses how to find the information, from the first stage of formulating a question and identifying the concepts of interest, through to writing a simple strategy. Sources for additional self-directed learning are provided.
Strategy to minimize dust foregrounds in B -mode searches
Kovetz, Ely D.; Kamionkowski, Marc
2015-04-01
The Planck satellite has identified several patches of sky with low polarized dust emission, obvious targets for searches for the cosmic microwave background B -mode signal from inflationary gravitational waves. Still, given the Planck measurement uncertainties, the polarized dust foregrounds in these different candidate patches may differ by an order of magnitude or more. Here we show that a brief initial experiment to map these candidate patches more deeply at a single high frequency can efficiently zero in on the cleanest patch(es) and thus improve significantly the sensitivity of subsequent B -mode searches. A ground-based experiment with current detector technology operating at ≳220 GHz for three months can efficiently identify a low-dust-amplitude patch and thus improve by up to a factor 2 or 3 on the sensitivity to cosmic B modes of the subsequent lower-frequency deep integration. A balloon experiment with current detector sensitivities covering the set of patches and operating at ˜350 GHz can reach a similar result in less than two weeks. This strategy may prove crucial in accessing the smallest gravitational-wave signals possible in large-field inflation. The high-frequency data from this exploratory experiment should also provide valuable foreground templates to subsequent experiments that integrate on any of the candidate patches explored.
Strategies in tower solar power plant optimization
Ramos, A.; Ramos, F.
2012-09-01
A method for optimizing a central receiver solar thermal electric power plant is studied. We parametrize the plant design as a function of eleven design variables and reduce the problem of finding optimal designs to the numerical problem of finding the minimum of a function of several variables. This minimization problem is attacked with different algorithms both local and global in nature. We find that all algorithms find the same minimum of the objective function. The performance of each of the algorithms and the resulting designs are studied for two typical cases. We describe a method to evaluate the impact of design variables in the plant performance. This method will tell us what variables are key to the optimal plant design and which ones are less important. This information can be used to further improve the plant design and to accelerate the optimization procedure.
Strategies in tower solar power plant optimization
Ramos, A
2012-01-01
A method for optimizing a central receiver solar thermal electric power plant is studied. We parametrize the plant design as a function of eleven design variables and reduce the problem of finding optimal designs to the numerical problem of finding the minimum of a function of several variables. This minimization problem is attacked with different algorithms both local and global in nature. We find that all algorithms find the same minimum of the objective function. The performance of each of the algorithms and the resulting designs are studied for two typical cases. We describe a method to evaluate the impact of design variables in the plant performance. This method will tell us what variables are key to the optimal plant design and which ones are less important. This information can be used to further improve the plant design and to accelerate the optimization procedure.
Optimization Under Uncertainty for Wake Steering Strategies
Energy Technology Data Exchange (ETDEWEB)
Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-03
This presentation covers the motivation for this research, optimization under the uncertainty problem formulation, a two-turbine case, the Princess Amalia Wind Farm case, and conclusions and next steps.
Strategies in tower solar power plant optimization
RAMOS, A.; RAMOS, F.
2012-01-01
A method for optimizing a central receiver solar thermal electric power plant is studied. We parametrize the plant design as a function of eleven design variables and reduce the problem of finding optimal designs to the numerical problem of finding the minimum of a function of several variables. This minimization problem is attacked with different algorithms both local and global in nature. We find that all algorithms find the same minimum of the objective function. The performance of each of...
Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan
2015-12-01
With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.
An approximation based global optimization strategy for structural synthesis
Sepulveda, A. E.; Schmit, L. A.
1991-01-01
A global optimization strategy for structural synthesis based on approximation concepts is presented. The methodology involves the solution of a sequence of highly accurate approximate problems using a global optimization algorithm. The global optimization algorithm implemented consists of a branch and bound strategy based on the interval evaluation of the objective function and constraint functions, combined with a local feasible directions algorithm. The approximate design optimization problems are constructed using first order approximations of selected intermediate response quantities in terms of intermediate design variables. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure setforth.
Hsu, Chung-Yuan; Tsai, Meng-Jung; Hou, Huei-Tse; Tsai, Chin-Chung
2014-06-01
Online information searching tasks are usually implemented in a technology-enhanced science curriculum or merged in an inquiry-based science curriculum. The purpose of this study was to examine the role students' different levels of scientific epistemic beliefs (SEBs) play in their online information searching strategies and behaviors. Based on the measurement of an SEB survey, 42 undergraduate and graduate students in Taiwan were recruited from a pool of 240 students and were divided into sophisticated and naïve SEB groups. The students' self-perceived online searching strategies were evaluated by the Online Information Searching Strategies Inventory, and their search behaviors were recorded by screen-capture videos. A sequential analysis was further used to analyze the students' searching behavioral patterns. The results showed that those students with more sophisticated SEBs tended to employ more advanced online searching strategies and to demonstrate a more metacognitive searching pattern.
A minimax optimal control strategy for uncertain quasi-Hamiltonian systems
Institute of Scientific and Technical Information of China (English)
Yong WANC; Zu-guang YING; Wei-qiu ZHU
2008-01-01
A minimax optimal control strategy for quasi-Hamiltonian systems with bounded parametric and/or external disturbances is proposed based on the stochastic averaging method and stochastic differential game. To conduct the system energy control, the partially averaged It6 stochastic differential equations for the energy processes are first derived by using the stochastic averaging method for quasi-Hamiltonian systems. Combining the above equations with an appropriate performance index, the proposed strategy is searching for an optimal worst-case controller by solving a stochastic differential game problem. The worst-case disturbances and the optimal controls are obtained by solving a Hamilton-Jacobi-Isaacs (HJI) equation. Numerical results for a controlled and stochastically excited Duffing oscillator with uncertain disturbances exhibit the efficacy of the proposed control strategy.
Institute of Scientific and Technical Information of China (English)
LI Liang; CHU Xue-song
2011-01-01
The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy can be adopted instead of the three parameters which are required in the original particle swarm optimization algorithm to update the positions of all the particles. The improved particle swarm optimization is used in the location of the critical slip surface of soil slope, and it is found that the improved particle swarm optimization algorithm is insensitive to the two parameters while the original particle swarm optimization algorithm can be sensitive to its three parameters.
Searching and optimizing structure ensembles for complex flexible sugars.
Xia, Junchao; Margulis, Claudio J; Case, David A
2011-10-05
NMR restrictions are suitable to specify the geometry of a molecule when a single well-defined global free energy minimum exists that is significantly lower than other local minima. Carbohydrates are quite flexible, and therefore, NMR observables do not always correlate with a single conformer but instead with an ensemble of low free energy conformers that can be accessed by thermal fluctuations. In this communication, we describe a novel procedure to identify and weight the contribution to the ensemble of local minima conformers based on comparison to residual dipolar couplings (RDCs) or other NMR observables, such as scalar couplings. A genetic algorithm is implemented to globally minimize the R factor comparing calculated RDCs to experiment. This is done by optimizing the weights of different conformers derived from the exhaustive local minima conformational search program, fast sugar structure prediction software (FSPS). We apply this framework to six human milk sugars, LND-1, LNF-1, LNF-2, LNF-3, LNnT, and LNT, and are able to determine corresponding population weights for the ensemble of conformers. Interestingly, our results indicate that in all cases the RDCs can be well represented by only a few most important conformers. This confirms that several, but not all of the glycosidic linkages in histo-blood group "epitopes" are quite rigid.
Two Complementary Strategies for New Physics Searches at Lepton Colliders
Energy Technology Data Exchange (ETDEWEB)
Hooberman, Benjamin Henry [Univ. of California, Berkeley, CA (United States)
2009-07-06
In this thesis I present two complementary strategies for probing beyond-the-Standard Model physics using data collected in e^{+}e^{-} collisions at lepton colliders. One strategy involves searching for effects at low energy mediated by new particles at the TeV mass scale, at which new physics is expected to manifest. Several new physics scenarios, including Supersymmetry and models with leptoquarks or compositeness, may lead to observable rates for charged lepton-flavor violating processes, which are forbidden in the Standard Model. I present a search for lepton-flavor violating decays of the Υ(3S) using data collected with the BABAR detector. This study establishes the 90% confidence level upper limits BF(Υ(3S) → eτ) < 5.0 x 10^{-6} and BF(Υ(3S) → μτ) < 4.1 x 10^{-6} which are used to place constraints on new physics contributing to lepton-flavor violation at the TeV mass scale. An alternative strategy is to increase the collision energy above the threshold for new particles and produce them directly. I discuss research and development efforts aimed at producing a vertex tracker which achieves the physics performance required of a high energy lepton collider. A small-scale vertex tracker prototype is constructed using Silicon sensors of 50 μm thickness and tested using charged particle beams. This tracker achieves the targeted impact parameter resolution of σ_{LP} = (5⊕10 GeV/p_{T}) as well as a longitudinal vertex resolution of (260 ± 10) μm, which is consistent with the requirements of a TeV-scale lepton collider. This detector research and development effort must be motivated and directed by simulation studies of physics processes. Investigation of a dark matter-motivated Supersymmetry scenario is presented, in which the dark matter is composed of Supersymmetric neutralinos. In this scenario, studies of the e^{+}e^{-} → H^{0}A^{0} production process allow for
Optimizing the 3R study strategy to learn from text
Reijners, Pauline; Kester, Liesbeth; Wetzels, Sandra; Kirschner, Paul A.
2013-01-01
Reijners, P. B. G., Kester, L., Wetzels, S. A. J., & Kirschner, P. A. (2013, 29 May). Optimizing the 3R study strategy to learn from text. Presentation at plenary meeting Learning & Cogntion, Heerlen, The Netherlands.
Optimizing the 3R study strategy to learn from text
Reijners, Pauline; Kester, Liesbeth; Wetzels, Sandra; Kirschner, Paul A.
2012-01-01
Reijners, P. B. G., Kester, L., Wetzels, S. A. J., & Kirschner, P. A. (2012, 21 November). Optimizing the 3R study strategy to learn from text. Presentation at research meeting Educational and Developmental Psychology, Erasmus University, Rotterdam, The Netherlands.
Optimizing the 3R study strategy to learn from text
Reijners, Pauline; Kester, Liesbeth; Wetzels, Sandra; Kirschner, Paul A.
2013-01-01
Reijners, P. B. G., Kester, L., Wetzels, S. A. J., & Kirschner, P. A. (2013, 7 November). Optimizing the 3R study strategy to learn from text. Paper presented at the ICO National Fall School, Maastricht, The Netherlands.
The construction of optimal hedging portfolio strategies of an investor
African Journals Online (AJOL)
We categorised the investor's portfolio into two folds: the initial investment and the capital gain ... We will also describe the dynamic of our stock price using Binomial lattice model ... equation to derive the optimal values of our trading strategies.
Strategy optimization for controlled Markov process with descriptive complexity constraint
Institute of Scientific and Technical Information of China (English)
JIA QingShan; ZHAO QianChuan
2009-01-01
Due to various advantages in storage and Implementation,simple strategies are usually preferred than complex strategies when the performances are close.Strategy optimization for controlled Markov process with descriptive complexity constraint provides a general framework for many such problems.In this paper,we first show by examples that the descriptive complexity and the performance of a strategy could be Independent,and use the F-matrix in the No-Free-Lunch Theorem to show the risk that approximating complex strategies may lead to simple strategies that are unboundedly worse in cardinal performance than the original complex strategies.We then develop a method that handles the descriptive complexity constraint directly,which describes simple strategies exactly and only approximates complex strategies during the optimization.The ordinal performance difference between the resulting strategies of this selective approximation method and the global optimum is quantified.Numerical examples on an engine maintenance problem show how this method Improves the solution quality.We hope this work sheds some insights to solving general strategy optimization for controlled Markov procase with descriptive complexity constraint.
Long-Run Savings and Investment Strategy Optimization
Directory of Open Access Journals (Sweden)
Russell Gerrard
2014-01-01
Full Text Available We focus on automatic strategies to optimize life cycle savings and investment. Classical optimal savings theory establishes that, given the level of risk aversion, a saver would keep the same relative amount invested in risky assets at any given time. We show that, when optimizing lifecycle investment, performance and risk assessment have to take into account the investor’s risk aversion and the maximum amount the investor could lose, simultaneously. When risk aversion and maximum possible loss are considered jointly, an optimal savings strategy is obtained, which follows from constant rather than relative absolute risk aversion. This result is fundamental to prove that if risk aversion and the maximum possible loss are both high, then holding a constant amount invested in the risky asset is optimal for a standard lifetime saving/pension process and outperforms some other simple strategies. Performance comparisons are based on downside risk-adjusted equivalence that is used in our illustration.
Optimal search: a practical interpretation of information-driven sensor management
Katsilieris, F.; Boers, Y.
We consider the problem of scheduling an agile sensor for performing optimal search for a target. A probability density function is created for representing our knowledge about where the target might be and it is utilized by the proposed sensor management criteria for finding optimal search
Chapter 4: effective search strategies for systematic reviews of medical tests.
Relevo, Rose
2012-06-01
This article discusses techniques that are appropriate when developing search strategies for systematic reviews of medical tests. This includes general advice for searching for systematic reviews and issues specific to systematic reviews of medical tests. Diagnostic search filters are currently not sufficiently developed for use when searching for systematic reviews. Instead, authors should construct a highly sensitive search strategy that uses both controlled vocabulary and text words. A comprehensive search should include multiple databases and sources of grey literature. A list of subject-specific databases is included in this article.
Power consumption optimization strategy for wireless networks
DEFF Research Database (Denmark)
Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola
2011-01-01
in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...
Search Engine Optimization through Spanning Forest Generation Algorithm
Directory of Open Access Journals (Sweden)
SATYA PAVAN KUMAR SOMAYAJULA
2011-09-01
Full Text Available Search engine technology has had to scale dramatically to keep up with the growth of the web. With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. The following are our research objectives: Isolate characteristics of informational, navigational, and transactional for Web searching queries by identifying characteristics of each query type that will lead toreal world classification. Validate the taxonomy by automatically classifying a large set of queries from a Web search engine. This paper we deal with now is semantic web search engines is the layeredarchitecture and we use this with relation based page rank algorithm.
Search and optimization by metaheuristics techniques and algorithms inspired by nature
Du, Ke-Lin
2016-01-01
This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computin...
Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation
Krastev, Vladimir
2011-12-01
We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.
Existence of optimal consumption strategies in markets with longevity risk
de Kort, Jan; Vellekoop, M.H.
2017-01-01
Survival bonds are financial instruments with a payoff that depends on human mortality rates. In markets that contain such bonds, agents optimizing expected utility of consumption and terminal wealth can mitigate their longevity risk. To examine how this influences optimal portfolio strategies and c
Synthesis of Optimal Strategies Using HyTech
DEFF Research Database (Denmark)
Bouyer, Patricia; Cassez, Franck; Larsen, Kim Guldstrand
2005-01-01
Priced timed (game) automata extend timed (game) automata with costs on both locations and transitions. The problem of synthesizing an optimal winning strategy for a priced timed game under some hypotheses has been shown decidable in [P. Bouyer, F. Cassez, E. Fleury, and K.G. Larsen. Optimal...
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Cao, Leilei; Xu, Lihong; Goodman, Erik D.
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Directory of Open Access Journals (Sweden)
Leilei Cao
2016-01-01
Full Text Available A Guiding Evolutionary Algorithm (GEA with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
Lin, Chia-Ching; Tsai, Chin-Chung
2007-10-01
To acquire a better understanding of the online search strategies that students employ to use the Internet, this study investigated six university students' approaches to Web-based information searches. A new method, called navigation flow map (NFM), is presented that graphically displays the fluid and multilayered relationships between Web navigation and information retrieval that students use while navigating the Web. To document the application of NFM, the Web search strategies of six university students were analyzed as they used the Internet to perform two different tasks: scientific-based and social studies-based information searches. Through protocol analyses using the NFM method, the students' searching strategies were categorized into two types: Match or Exploration. The findings revealed that participants with an Exploration approach had more complicated and richer task-specific ways of searching information than those with a Match approach; and further, through between-task comparisons, we found that participants appeared to use different searching strategies to process natural science information compared to social studies information. Finally, the participants in the Exploration group also exhibited better task performance on the criterion measures than those in the Match group.
Health benefit modelling and optimization of vehicular pollution control strategies
Sonawane, Nayan V.; Patil, Rashmi S.; Sethi, Virendra
2012-12-01
This study asserts that the evaluation of pollution reduction strategies should be approached on the basis of health benefits. The framework presented could be used for decision making on the basis of cost effectiveness when the strategies are applied concurrently. Several vehicular pollution control strategies have been proposed in literature for effective management of urban air pollution. The effectiveness of these strategies has been mostly studied as a one at a time approach on the basis of change in pollution concentration. The adequacy and practicality of such an approach is studied in the present work. Also, the assessment of respective benefits of these strategies has been carried out when they are implemented simultaneously. An integrated model has been developed which can be used as a tool for optimal prioritization of various pollution management strategies. The model estimates health benefits associated with specific control strategies. ISC-AERMOD View has been used to provide the cause-effect relation between control options and change in ambient air quality. BenMAP, developed by U.S. EPA, has been applied for estimation of health and economic benefits associated with various management strategies. Valuation of health benefits has been done for impact indicators of premature mortality, hospital admissions and respiratory syndrome. An optimization model has been developed to maximize overall social benefits with determination of optimized percentage implementations for multiple strategies. The model has been applied for sub-urban region of Mumbai city for vehicular sector. Several control scenarios have been considered like revised emission standards, electric, CNG, LPG and hybrid vehicles. Reduction in concentration and resultant health benefits for the pollutants CO, NOx and particulate matter are estimated for different control scenarios. Finally, an optimization model has been applied to determine optimized percentage implementation of specific
Novel cued search strategy based on information gain for phased array radar
Institute of Scientific and Technical Information of China (English)
Lu Jianbin; Hu Weidong; Xiao Hui; Yu Wenxian
2008-01-01
A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then,two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy,respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.
Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong
2016-09-01
In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.
Mesh refinement strategy for optimal control problems
Paiva, Luis Tiago; Fontes, Fernando,
2013-01-01
International audience; Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform node...
Sleep As A Strategy For Optimizing Performance.
Yarnell, Angela M; Deuster, Patricia
2016-01-01
Recovery is an essential component of maintaining, sustaining, and optimizing cognitive and physical performance during and after demanding training and strenuous missions. Getting sufficient amounts of rest and sleep is key to recovery. This article focuses on sleep and discusses (1) why getting sufficient sleep is important, (2) how to optimize sleep, and (3) tools available to help maximize sleep-related performance. Insufficient sleep negatively impacts safety and readiness through reduced cognitive function, more accidents, and increased military friendly-fire incidents. Sufficient sleep is linked to better cognitive performance outcomes, increased vigor, and better physical and athletic performance as well as improved emotional and social functioning. Because Special Operations missions do not always allow for optimal rest or sleep, the impact of reduced rest and sleep on readiness and mission success should be minimized through appropriate preparation and planning. Preparation includes periods of "banking" or extending sleep opportunities before periods of loss, monitoring sleep by using tools like actigraphy to measure sleep and activity, assessing mental effectiveness, exploiting strategic sleep opportunities, and consuming caffeine at recommended doses to reduce fatigue during periods of loss. Together, these efforts may decrease the impact of sleep loss on mission and performance. 2016.
Discuss Optimal Approaches to Learning Strategy Instruction for EFL Learners
Institute of Scientific and Technical Information of China (English)
邢菊如
2009-01-01
Numerous research studies reveal that learning strategies have played an important role in language learning processes.This paper explores as English teachers.can we impmve students' language proficiency by giving them optimal learning strategy instruction and what approaches are most effective and efficient?
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
Optimal Portfolio Strategy under Rolling Economic Maximum Drawdown Constraints
Xiaojian Yu; Siyu Xie; Weijun Xu
2014-01-01
This paper deals with the problem of optimal portfolio strategy under the constraints of rolling economic maximum drawdown. A more practical strategy is developed by using rolling Sharpe ratio in computing the allocation proportion in contrast to existing models. Besides, another novel strategy named “REDP strategy” is further proposed, which replaces the rolling economic drawdown of the portfolio with the rolling economic drawdown of the risky asset. The simulation tests prove that REDP stra...
Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp.
Nguyen, Phi-Vu; Ghezal, Ali; Hsueh, Ya-Chih; Boudier, Thomas; Gan, Samuel Ken-En; Lee, Hwee Kuan
2016-08-01
In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the segmentation algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9 ± 2.0% for protein polyacrylamide gels, and 35.9 ± 2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimize general image segmentation. The improved version of GelApp-GelApp 2.0-is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform). © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Optimization and universality of Brownian search in a basic model of quenched heterogeneous media
Godec, Aljaž; Metzler, Ralf
2015-05-01
The kinetics of a variety of transport-controlled processes can be reduced to the problem of determining the mean time needed to arrive at a given location for the first time, the so-called mean first-passage time (MFPT) problem. The occurrence of occasional large jumps or intermittent patterns combining various types of motion are known to outperform the standard random walk with respect to the MFPT, by reducing oversampling of space. Here we show that a regular but spatially heterogeneous random walk can significantly and universally enhance the search in any spatial dimension. In a generic minimal model we consider a spherically symmetric system comprising two concentric regions with piecewise constant diffusivity. The MFPT is analyzed under the constraint of conserved average dynamics, that is, the spatially averaged diffusivity is kept constant. Our analytical calculations and extensive numerical simulations demonstrate the existence of an optimal heterogeneity minimizing the MFPT to the target. We prove that the MFPT for a random walk is completely dominated by what we term direct trajectories towards the target and reveal a remarkable universality of the spatially heterogeneous search with respect to target size and system dimensionality. In contrast to intermittent strategies, which are most profitable in low spatial dimensions, the spatially inhomogeneous search performs best in higher dimensions. Discussing our results alongside recent experiments on single-particle tracking in living cells, we argue that the observed spatial heterogeneity may be beneficial for cellular signaling processes.
Computer Forensics, Search Strategies, and the Particularity Requirement
Directory of Open Access Journals (Sweden)
Wayne Jekot
2007-04-01
Full Text Available Assuming that a person subject to a search and seizure of his or her computer has a reasonable expectation of privacy in the contents of the computer, and thus a warrant is required, should the warrant outline a “search strategy”? Or should comprehensive computer searches be permitted? In other words, how should the particularity requirement be applied to computer searches? Correspondingly, what can a forensic examiner do under a warrant while collecting potential evidence from a computer? [...
On Global Optimal Sailplane Flight Strategy
Sander, G. J.; Litt, F. X.
1979-01-01
The derivation and interpretation of the necessary conditions that a sailplane cross-country flight has to satisfy to achieve the maximum global flight speed is considered. Simple rules are obtained for two specific meteorological models. The first one uses concentrated lifts of various strengths and unequal distance. The second one takes into account finite, nonuniform space amplitudes for the lifts and allows, therefore, for dolphin style flight. In both models, altitude constraints consisting of upper and lower limits are shown to be essential to model realistic problems. Numerical examples illustrate the difference with existing techniques based on local optimality conditions.
Optimal Inspection and Maintenance Strategies for Structural Systems
DEFF Research Database (Denmark)
Sommer, A. M.
The aim of this thesis is to give an overview of conventional and optimal reliability-based inspection and maintenance strategies and to examine for specific structures how the cost can be reduced and/or the safety can be improved by using optimal reliability-based inspection strategies. For stru......The aim of this thesis is to give an overview of conventional and optimal reliability-based inspection and maintenance strategies and to examine for specific structures how the cost can be reduced and/or the safety can be improved by using optimal reliability-based inspection strategies....... Furthermore, in relation to the calculations performed the intention is to modify an existing program for determination of optimal inspection strategies. The main purpose of inspection and maintenance of structural systems is to prevent or delay damage or deterioration to protect people, environment......, and investments made in the structure. The inspection and maintenance should be performed so that the structural system is operating as much of the time as possible and the cost is kept at a minimum and so that the safety of the structure is satisfactory. Up till now inspection strategies have been based...
Job-search strategies and reemployment quality: the impact of career adaptability
Koen, J.; Klehe, U.-C.; van Vianen, A.E.M.; Zikic, J.; Nauta, A.
2010-01-01
Past job-search research has focused on how hard unemployed people search for a job, but we still know little about the strategies that people use during their search and how we can predict the quality of the reemployment found. The first aim of this study was to predict the use of different job-sea
Institute of Scientific and Technical Information of China (English)
姜喜瑞; 贺之渊; 汤广福; 谢敏华; 刘栋
2013-01-01
This paper is written on the valve base control strategy specifically for high-voltage large-capacity modular multi-level converter (MMC) and on the sub-module capacitor voltage balance control strategy using tabu search optimization algorithm. In the context of high-voltage large-capacity VSC-HVDC transmission system, the paper analyzes MMC topology and related valve base control technique. The criteria for sub-module capacitor voltage balance algorithm are brought forward; the switching principle of sub-module pulse distribution is given in-depth study. Based on a distributed system structure, a novel sub-module voltage balance model using tabu search optimization algorithm is presented applicable to VSC-HVDC valve base control technique. In order to set up the objective function and constraints, establish the state decision optimization model, and formulate the optimization algorithm process, this model considers various factors, such as sub-module switch- on/off, energy fluctuations, and key parameters quintuple information tree. Simulation test and off-line test were carried out on the dynamic simulation platform employing PSCAD/EMTDC software. The test results showed that this approach was as good as nearest level approximation in terms of functionality and performance reliability, thus providing theoretical support and engineering basis.% 针对高压大容量模块化多电平换流器(modular multilevel converter，MMC)的阀基控制技术策略，研究基于禁忌搜索优化算法的子模块电容电压平衡控制策略。以高压大容量柔性直流输电系统为应用背景，对MMC技术及其阀基控制技术进行分析研究，通过对MMC技术子模块电压平衡策略的研究，提出了子模块电容电压平衡算法的评判指标，并对子模块脉冲分配投切机理进行了深入的分析研究；在分布式系统架构基础上，提出了一种新颖的适用于高压大容量柔性直流输电系统阀基控制技
Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy
Institute of Scientific and Technical Information of China (English)
Wenjing Lyu; Weilin Luo
2014-01-01
In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body’s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.
The development of PubMed search strategies for patient preferences for treatment outcomes
Directory of Open Access Journals (Sweden)
Ralph van Hoorn
2016-07-01
Full Text Available Abstract Background The importance of respecting patients’ preferences when making treatment decisions is increasingly recognized. Efficiently retrieving papers from the scientific literature reporting on the presence and nature of such preferences can help to achieve this goal. The objective of this study was to create a search filter for PubMed to help retrieve evidence on patient preferences for treatment outcomes. Methods A total of 27 journals were hand-searched for articles on patient preferences for treatment outcomes published in 2011. Selected articles served as a reference set. To develop optimal search strategies to retrieve this set, all articles in the reference set were randomly split into a development and a validation set. MeSH-terms and keywords retrieved using PubReMiner were tested individually and as combinations in PubMed and evaluated for retrieval performance (e.g. sensitivity (Se and specificity (Sp. Results Of 8238 articles, 22 were considered to report empirical evidence on patient preferences for specific treatment outcomes. The best search filters reached Se of 100 % [95 % CI 100-100] with Sp of 95 % [94–95 %] and Sp of 97 % [97–98 %] with 75 % Se [74–76 %]. In the validation set these queries reached values of Se of 90 % [89–91 %] with Sp 94 % [93–95 %] and Se of 80 % [79–81 %] with Sp of 97 % [96–96 %], respectively. Conclusions Narrow and broad search queries were developed which can help in retrieving literature on patient preferences for treatment outcomes. Identifying such evidence may in turn enhance the incorporation of patient preferences in clinical decision making and health technology assessment.
Optimization of energy planning strategies in municipalities
DEFF Research Database (Denmark)
Petersen, Jens-Phillip
The paper evaluates the current status of community energy planning in northern Europe via a review of literature, practice and the performance of a barrier analysis for successful community energy planning. Main findings of the paper are that current community energy planning lacks a systematic...... approach, suffers from insufficient information, tools and resources. Municipalities are often unable to take on a steering role in community energy planning. To overcome these barriers and guide municipalities in the pre-project phase, a decision-support methodology, based on community energy profiles...... (CEP), is presented. The methodology was applied in a case study in Germany. With CEPs, a possibility to merge qualitative data from local settings into generic energy modelling is shown, which could contribute to improved community energy strategies....
Applying the Taguchi method to river water pollution remediation strategy optimization.
Yang, Tsung-Ming; Hsu, Nien-Sheng; Chiu, Chih-Chiang; Wang, Hsin-Ju
2014-04-15
Optimization methods usually obtain the travel direction of the solution by substituting the solutions into the objective function. However, if the solution space is too large, this search method may be time consuming. In order to address this problem, this study incorporated the Taguchi method into the solution space search process of the optimization method, and used the characteristics of the Taguchi method to sequence the effects of the variation of decision variables on the system. Based on the level of effect, this study determined the impact factor of decision variables and the optimal solution for the model. The integration of the Taguchi method and the solution optimization method successfully obtained the optimal solution of the optimization problem, while significantly reducing the solution computing time and enhancing the river water quality. The results suggested that the basin with the greatest water quality improvement effectiveness is the Dahan River. Under the optimal strategy of this study, the severe pollution length was reduced from 18 km to 5 km.
Online Information Searching Strategy Inventory (OISSI): A Quick Version and a Complete Version
Tsai, Meng-Jung
2009-01-01
This study developed an instrument to evaluate student online information searching strategies based on a framework comprising three domains and seven aspects. Two versions of the Online Information Searching Strategies Inventory (OISSI), including both quick and complete versions, were finally established and exhibited good validities and…
Directory of Open Access Journals (Sweden)
Saifullah Khalid
2016-09-01
Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.
Optimizing Search and Ranking in Folksonomy Systems by Exploiting Context Information
Abel, Fabian; Henze, Nicola; Krause, Daniel
Tagging systems enable users to annotate resources with freely chosen keywords. The evolving bunch of tag assignments is called folksonomy and there exist already some approaches that exploit folksonomies to improve resource retrieval. In this paper, we analyze and compare graph-based ranking algorithms: FolkRank and SocialPageRank. We enhance these algorithms by exploiting the context of tags, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity itself is easy for users to perform. However, it delivers valuable semantic information about resources and their context. We present GRank that uses the context information to improve and optimize the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.
Optimal Demand Side Bidding with Carbon Emission by Using MRS Strategy
Directory of Open Access Journals (Sweden)
Kaustubh Dwivedi
2014-01-01
Full Text Available A new moderate-random-search strategy (MRPSO is used for an optimal bidding strategy of a supplier, considering linear bidding curve model with a precise model and emission as constraints, and who delivered electricity to end users in oligopolistic dynamic electricity is studied. Bidding strategy of a supplier is solved by MRPSO, where mean best position (mbest boosts the diversity and the exploration ability of particle. The MRPSO adopts an attractor pd as the main moving direction of particles, which replaces the velocity update procedure in the particle swarm optimization. The effectiveness of the proposed approach is tested with linear bidding model and the results are compared with the solutions obtained using classical PSO. In this paper, a comparative study has been done by a competitive bidding model tested on IEEE 14- and IEEE 39-bus systems and results motivate the suppliers towards opting green technologies.
Optimal Portfolio Strategy under Rolling Economic Maximum Drawdown Constraints
Directory of Open Access Journals (Sweden)
Xiaojian Yu
2014-01-01
Full Text Available This paper deals with the problem of optimal portfolio strategy under the constraints of rolling economic maximum drawdown. A more practical strategy is developed by using rolling Sharpe ratio in computing the allocation proportion in contrast to existing models. Besides, another novel strategy named “REDP strategy” is further proposed, which replaces the rolling economic drawdown of the portfolio with the rolling economic drawdown of the risky asset. The simulation tests prove that REDP strategy can ensure the portfolio to satisfy the drawdown constraint and outperforms other strategies significantly. An empirical comparison research on the performances of different strategies is carried out by using the 23-year monthly data of SPTR, DJUBS, and 3-month T-bill. The investment cases of single risky asset and two risky assets are both studied in this paper. Empirical results indicate that the REDP strategy successfully controls the maximum drawdown within the given limit and performs best in both return and risk.
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions.
Shutao Li; Mingkui Tan; Tsang, I W; Kwok, James Tin-Yau
2011-08-01
Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems.
White Hat Search Engine Optimization (SEO: Structured Web Data for Libraries
Directory of Open Access Journals (Sweden)
Dan Scott
2015-06-01
Full Text Available “White hat” search engine optimization refers to the practice of publishing web pages that are useful to humans, while enabling search engines and web applications to better understand the structure and content of your website. This article teaches you to add structured data to your website so that search engines can more easily connect patrons to your library locations, hours, and contact information. A web page for a branch of the Greater Sudbury Public Library retrieved in January 2015 is used as the basis for examples that progressively enhance the page with structured data. Finally, some of the advantages structured data enables beyond search engine optimization are explored
Optimized Graph Search Using Multi-Level Graph Clustering
Kala, Rahul; Shukla, Anupam; Tiwari, Ritu
Graphs find a variety of use in numerous domains especially because of their capability to model common problems. The social networking graphs that are used for social networking analysis, a feature given by various social networking sites are an example of this. Graphs can also be visualized in the search engines to carry search operations and provide results. Various searching algorithms have been developed for searching in graphs. In this paper we propose that the entire network graph be clustered. The larger graphs are clustered to make smaller graphs. These smaller graphs can again be clustered to further reduce the size of graph. The search is performed on the smallest graph to identify the general path, which may be further build up to actual nodes by working on the individual clusters involved. Since many searches are carried out on the same graph, clustering may be done once and the data may be used for multiple searches over the time. If the graph changes considerably, only then we may re-cluster the graph.
Optimized Information Transmission Scheduling Strategy Oriented to Advanced Metering Infrastructure
Directory of Open Access Journals (Sweden)
Weiming Tong
2013-01-01
Full Text Available Advanced metering infrastructure (AMI is considered to be the first step in constructing smart grid. AMI allows customers to make real-time choices about power utilization and enables power utilities to increase the effectiveness of the regional power grids by managing demand load during peak times and reducing unneeded power generation. These initiatives rely heavily on the prompt information transmission inside AMI. Aiming at the information transmission problem, this paper researches the communication scheduling strategy in AMI at a macroscopic view. First, the information flow of AMI is analyzed, and the power users are classified into several grades by their importance. Then, the defect of conventional information transmission scheduling strategy is analyzed. On this basis, two optimized scheduling strategies are proposed. In the wide area, an optimized scheduling strategy based on user importance and time critical is proposed to guarantee the important power users’ information transmission being handled promptly. In the local area, an optimized scheduling strategy based on device and information importance and time critical is proposed to guarantee the important devices and information in AMI user end system being handled promptly. At last, the two optimized scheduling strategies are simulated. The simulation results show that they can effectively improve the real-time performance and reliability of AMI information transmission.
Strategies for optimizing nitrogen use by ruminants
DEFF Research Database (Denmark)
Calsamiglia, S; Ferret, A; Reynolds, C K
2010-01-01
The efficiency of N utilization in ruminants is typically low (around 25%) and highly variable (10% to 40%) compared with the higher efficiency of other production animals. The low efficiency has implications for the production performance and environment. Many efforts have been devoted to improv......The efficiency of N utilization in ruminants is typically low (around 25%) and highly variable (10% to 40%) compared with the higher efficiency of other production animals. The low efficiency has implications for the production performance and environment. Many efforts have been devoted...... to improving the efficiency of N utilization in ruminants, and while major improvements in our understanding of N requirements and metabolism have been achieved, the overall efficiency remains low. In general, maximal efficiency of N utilization will only occur at the expense of some losses in production...... performance. However, optimal production and N utilization may be achieved through the understanding of the key mechanisms involved in the control of N metabolism. Key factors in the rumen include the efficiency of N capture in the rumen (grams of bacterial N per grams of rumen available N...
Directory of Open Access Journals (Sweden)
Yuliang Su
2015-04-01
Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.
Directory of Open Access Journals (Sweden)
Daniel Corneliu LEUCUŢA
2009-12-01
Full Text Available Introduction: Quality research and quality evidence based medicine practice has an important pillar in a solid bibliographic documentation. Quality bibliographic documentation makes use of search strategies to retrieve articles from search engines of bibliographic databases. The AIM of this study was the identification of useful search terms to be used in search strategies that try to find meta-analyses of survival data. Materials and methods: A qualitative study based on text analysis was undertaken to identify useful terms for search strategies in abstracts of scientific papers. Survival analysis meta-analyses publication type studies, published between 1996 and 2005, were searched in Medline bibliographic database through Pubmed web interface. Each abstract was analysed and each important terms were noted down if they were considered to be useful in the creation of search strategies for analysis of survival data, or meta-analyses. Results: Pubmed search yielded 773 results. From these search results 401 (52% fulfilled inclusion criteria. The terms that were identified as useful in search strategies for meta-analyses of survival data are presented in the paper.
Optimization of Secondary Concentrators with the Continuous Information Entropy Strategy
Schmidt, Tobias Christian; Ries, Harald
2010-10-01
In this contribution, a method for global optimization of noisy functions, the Continuous Information Entropy Strategy (CIES), is explained and its applicability for the optimization of solar concentrators is shown. The CIES is efficient because all decisions made during optimizations are based on criteria that are derived from the concept of information entropy. Two secondary concentrators have been optimized with the CIES. The optimized secondary concentrators convert circular light distributions of round focal spots to square light distributions to match with the shape of square PV cells. The secondary concentrators are highly efficient and have geometrical concentration ratios of 2.25 and 8 respectively. Part of this material has been published in: T. C. Schmidt, "Information Entropy-Based Decision Making in Optimization", Ph.D. Thesis, Philipps University Marburg, 2010.
On the relationship between human search strategies, conspicuity and search performance
Hogervorst, M.A.; Toet, A.; Bijl, P.
2005-01-01
We determined the relationship between search performance with a limited field of view (FOV) and several scanning- and scene parameters in human observer experiments. The observers (38 trained army scouts) searched through a large search sector for a target (a camouflaged person) on a heath. From tr
On the relationship between human search strategies, conspicuity and search performance
Hogervorst, M.A.; Toet, A.; Bijl, P.
2005-01-01
We determined the relationship between search performance with a limited field of view (FOV) and several scanning- and scene parameters in human observer experiments. The observers (38 trained army scouts) searched through a large search sector for a target (a camouflaged person) on a heath. From
Jochmann-Mannak, Hanna
2014-01-01
Every day, more children use digital media to search for information. While most children aged 8-12 make use of Google to search for information, research shows that children experience all kinds of problems using search interfaces such as Google. One of the reasons is that these informational
On the relationship between human search strategies, conspicuity and search performance
Hogervorst, M.A.; Toet, A.; Bijl, P.
2005-01-01
We determined the relationship between search performance with a limited field of view (FOV) and several scanning- and scene parameters in human observer experiments. The observers (38 trained army scouts) searched through a large search sector for a target (a camouflaged person) on a heath. From tr
Strategies for chemical reaction searching in SciFinder
Ridley
2000-09-01
The bibliographic, chemical structure, and chemical reaction databases produced by Chemical Abstracts Service allow a number of possibilities for chemical reaction searching. While these same databases may be searched through the STN network, many end-users find the intuitive software interface SciFinder simpler, but there still are issues to address. Searching may be performed through keywords, chemical structures, or chemical reactions, and the answers may vary with respect to precision and comprehension. Often combinations of search options may be needed to best solve the problem. Retrosynthetic analyses are easily performed in the chemical reaction database and can give unique insights into synthetic alternatives.
Optimization model of vaccination strategy for dengue transmission
Widayani, H.; Kallista, M.; Nuraini, N.; Sari, M. Y.
2014-02-01
Dengue fever is emerging tropical and subtropical disease caused by dengue virus infection. The vaccination should be done as a prevention of epidemic in population. The host-vector model are modified with consider a vaccination factor to prevent the occurrence of epidemic dengue in a population. An optimal vaccination strategy using non-linear objective function was proposed. The genetic algorithm programming techniques are combined with fourth-order Runge-Kutta method to construct the optimal vaccination. In this paper, the appropriate vaccination strategy by using the optimal minimum cost function which can reduce the number of epidemic was analyzed. The numerical simulation for some specific cases of vaccination strategy is shown.
Turbine Control Strategies for Wind Farm Power Optimization
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Göçmen Bozkurt, Tuhfe; Giebel, Gregor
2015-01-01
In recent decades there has been increasing interest in green energies, of which wind energy is the most important one. In order to improve the competitiveness of the wind power plants, there are ongoing researches to decrease cost per energy unit and increase the efficiency of wind turbines...... and wind farms. One way of achieving these goals is to optimize the power generated by a wind farm. One optimization method is to choose appropriate operating points for the individual wind turbines in the farm. We have made three models of a wind farm based on three difference control strategies....... Basically, the control strategies determine the steady state operating points of the wind turbines. Except the control strategies of the individual wind turbines, the wind farm models are similar. Each model consists of a row of 5MW reference wind turbines. In the models we are able to optimize...
Optimal Search Strategy for the Definition of a DNAPL Source
2009-08-01
inappropriately? – Did the facility have any underground storage tanks? – Did the waste dump receive any DNAPL and in what quantities? The discrete Choquet...2/( 2)( σ+= meXE . Definition 9. The variance of a lognormal random variable X denoted by Var(X), is: 22 2)(2)( σσ ++ −= mm eeXVar . Usually
Optimal generator bidding strategies for power and ancillary services
Morinec, Allen G.
As the electric power industry transitions to a deregulated market, power transactions are made upon price rather than cost. Generator companies are interested in maximizing their profits rather than overall system efficiency. A method to equitably compensate generation providers for real power, and ancillary services such as reactive power and spinning reserve, will ensure a competitive market with an adequate number of suppliers. Optimizing the generation product mix during bidding is necessary to maximize a generator company's profits. The objective of this research work is to determine and formulate appropriate optimal bidding strategies for a generation company in both the energy and ancillary services markets. These strategies should incorporate the capability curves of their generators as constraints to define the optimal product mix and price offered in the day-ahead and real time spot markets. In order to achieve such a goal, a two-player model was composed to simulate market auctions for power generation. A dynamic game methodology was developed to identify Nash Equilibria and Mixed-Strategy Nash Equilibria solutions as optimal generation bidding strategies for two-player non-cooperative variable-sum matrix games with incomplete information. These games integrated the generation product mix of real power, reactive power, and spinning reserve with the generators's capability curves as constraints. The research includes simulations of market auctions, where strategies were tested for generators with different unit constraints, costs, types of competitors, strategies, and demand levels. Studies on the capability of large hydrogen cooled synchronous generators were utilized to derive useful equations that define the exact shape of the capability curve from the intersections of the arcs defined by the centers and radial vectors of the rotor, stator, and steady-state stability limits. The available reactive reserve and spinning reserve were calculated given a
Wang, Ling; Xu, Huihui; Zhang, Xue; Fang, Ping
2017-08-01
The job search process is a stressful experience. This study investigated the effect of emotion regulation strategies on job search behavior in combination with anxiety and job search self-efficacy among Chinese university fourth-year students (N = 816, mean age = 21.98, 31.5% male, 34.9% majored in science, 18.0% from "211 Project" universities). Results showed that cognitive reappraisal was positively related to job search behavior, while expressive suppression was negatively related to job search behavior. Additionally, anxiety was negatively related to job search behavior, while job search self-efficacy was positively associated with job search behavior. Moreover, both anxiety and job search self-efficacy mediated the relationship between emotion regulation strategies and job search behavior. In general, emotion regulation strategies played an important role in job search behavior. Implications include the notion that emotion regulation interventions may be helpful to increase job search behavior among university students. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
Optimal design of coordination control strategy for distributed generation system
Institute of Scientific and Technical Information of China (English)
WANG Ai-hua; Norapon Kanjanapadit
2009-01-01
This paper presents a novel design procedure for optimizing the power distribution strategy in distributed generation system. A coordinating controller, responsible to distribute the total load power request among multiple DG units, is suggested based on the conception of hierarchical control structure in the dynamic system.The optimal control problem was formulated as a nonlinear optimization problem subject to set of constraints.The resulting problem was solved using the Kutm-Tucker method. Computer simulation results demonstrate that the proposed method can provide better efficiency in terms of reducing total costs compared to existing methods.In addition, the proposed optimal load distribution strategy can be easily implemented in real-time thanks to the simplicity of closed-form solutions.
Optimal Watermark Embedding and Detection Strategies Under Limited Detection Resources
Merhav, Neri
2007-01-01
An information-theoretic approach is proposed to watermark embedding and detection under limited detector resources. First, we consider the attack-free scenario under which asymptotically optimal decision regions in the Neyman-Pearson sense are proposed, along with the optimal embedding rule. Later, we explore the case of zero-mean i.i.d. Gaussian covertext distribution with unknown variance under the attack-free scenario. For this case, we propose a lower bound on the exponential decay rate of the false-negative probability and prove that the optimal embedding and detecting strategy is superior to the customary linear, additive embedding strategy in the exponential sense. Finally, these results are extended to the case of memoryless attacks and general worst case attacks. Optimal decision regions and embedding rules are offered, and the worst attack channel is identified.
Social Optimization and Pricing Policy in Cognitive Radio Networks with an Energy Saving Strategy
Directory of Open Access Journals (Sweden)
Shunfu Jin
2016-01-01
Full Text Available The rapid growth of wireless application results in an increase in demand for spectrum resource and communication energy. In this paper, we firstly introduce a novel energy saving strategy in cognitive radio networks (CRNs and then propose an appropriate pricing policy for secondary user (SU packets. We analyze the behavior of data packets in a discrete-time single-server priority queue under multiple-vacation discipline. With the help of a Quasi-Birth-Death (QBD process model, we obtain the joint distribution for the number of SU packets and the state of base station (BS via the Matrix-Geometric Solution method. We assess the average latency of SU packets and the energy saving ratio of system. According to a natural reward-cost structure, we study the individually optimal behavior and the socially optimal behavior of the energy saving strategy and use an optimization algorithm based on standard particle swarm optimization (SPSO method to search the socially optimal arrival rate of SU packets. By comparing the individually optimal behavior and the socially optimal behavior, we impose an appropriate admission fee to SU packets. Finally, we present numerical results to show the impacts of system parameters on the system performance and the pricing policy.
NEW OPTIMAL LARGE ANGLE MANEUVER STRATEGY FOR SINGLE FLEXIBLE LINK
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A component synthesis vibration suppression (CSVS) method for flexible structures is put forward. It can eliminate any unwanted orders of flexible vibration modes while achieves desired rigid motion. This method has robustness to uncertainty of frequency, which makes it practical in engineering. Several time optimal and time-fuel optimal control strategies are designed for a kind of single flexible link. Simulation results validate the feasibility of our method.
Rahman, Imran; Vasant, Pandian M.; Singh, Balbir Singh Mahinder; Abdullah-Al-Wadud, M.
2014-10-01
Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.
Optimal Control Strategies in Delayed Sharing Information Structures
Nayyar, Ashutosh; Teneketzis, Demosthenis
2010-01-01
The $n$-step delayed sharing information structure is investigated. This information structure comprises of $K$ controllers that share their information with a delay of $n$ time steps. This information structure is a link between the classical information structure, where information is shared perfectly between the controllers, and a non-classical information structure, where there is no "lateral" sharing of information among the controllers. Structural results for optimal control strategies for systems with such information structures are presented. A sequential methodology for finding the optimal strategies is also derived. The solution approach provides an insight for identifying structural results and sequential decomposition for general decentralized stochastic control problems.
Optimization Under Uncertainty for Wake Steering Strategies: Preprint
Energy Technology Data Exchange (ETDEWEB)
Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University
2017-05-01
Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.
Furuta, Atsuhiro; Mori, Hiroyuki
This paper proposes a hybrid method of hierarchical optimization and Parallel Tabu Search (PTS) for distribution system service restoration with distributed generators. The objective is to evaluate the optimal route to recover the service. The improvement of power quality makes the service restoration more important. Distribution system service restoration is one of complicated combinational optimization problems that are expressed as nonlinear mixed integer programming. In this paper, an efficient method is proposed to restore the service in a hierarchical optimization with Parallel Tabu Search. The proposed method is tested in a sample system.
Wang, Jiang; Liu, Hong
2013-10-01
Lead compound optimization plays an important role in new drug discovery and development. The strategies for changing metabolic pathways can modulate pharmacokinetic properties, prolong the half life, improve metabolism stability and bioavailability of lead compounds. The strategies for changing metabolic pathways and improving metabolism stability are reviewed. These methods include blocking metabolic site, reduing lipophilicity, changing ring size, bioisosterism, and prodrug.
Development of a scatter search optimization algorithm for BWR fuel lattice design
Energy Technology Data Exchange (ETDEWEB)
Francois, J.L.; Martin-del-Campo, C. [Mexico Univ. Nacional Autonoma, Facultad de Ingenieria (Mexico); Morales, L.B.; Palomera, M.A. [Mexico Univ. Nacional Autonoma, Instituto de Investigaciones en Matematicas Aplicadas y Sistemas, D.F. (Mexico)
2005-07-01
A basic Scatter Search (SS) method, applied to the optimization of radial enrichment and gadolinia distributions for BWR fuel lattices, is presented in this paper. Scatter search is considered as an evolutionary algorithm that constructs solutions by combining others. The goal of this methodology is to enable the implementation of solution procedures that can derive new solutions from combined elements. The main mechanism for combining solutions is such that a new solution is created from the strategic combination of two other solutions to explore the solutions' space. Results show that the Scatter Search method is an efficient optimization algorithm applied to the BWR design and optimization problem. Its main features are based on the use of heuristic rules since the beginning of the process, which allows directing the optimization process to the solution, and to use the diversity mechanism in the combination operator, which allows covering the search space in an efficient way. (authors)
Search Result Merging and Ranking Strategies in Meta-Search Engines: A Survey
Directory of Open Access Journals (Sweden)
Hossein Jadidoleslamy
2012-07-01
Full Text Available MetaSearch is utilizing multiple other search systems to perform simultaneous search. A MetaSearch Engine (MSE is a search system that enables MetaSearch. To perform a MetaSearch, user query is sent to multiple search engines; once the search results returned, they are received by the MSE, then merged into a single ranked list and the ranked list is presented to the user. When a query is submitted to a MSE, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query and how to score the results. These decisions are typically made by considering only the user€™s keyword query, neglecting the larger information need. The cornerstone of their technology is their rank aggregation method. In other words, Result merging is a key component in a MSE. The effectiveness of a MSE is closely related to the result merging algorithm it employs. In this paper, we want to investigate a variety of result merging methods based on a wide range of available information about the retrieved results, from their local ranks, their titles and snippets, to the full documents of these results.
Directory of Open Access Journals (Sweden)
Gobind Preet Singh
2014-02-01
Full Text Available Today, in computer science, a computational challenge exists in finding a globally optimized solution from an enormously large search space. Various meta-heuristic methods can be used for finding the solution in a large search space. These methods can be explained as iterative search processes that efficiently perform the exploration and exploitation in the solution space. In this context, three such nature inspired meta-heuristic algorithms namely Krill Herd Algorithm (KH, Firefly Algorithm (FA and Cuckoo search Algorithm (CS can be used to find optimal solutions of various mathematical optimization problems. In this paper, the proposed algorithms were used to find the optimal solution of fifteen unimodal and multimodal benchmark test functions commonly used in the field of optimization and then compare their performances on the basis of efficiency, convergence, time and conclude that for both unimodal and multimodal optimization Cuckoo Search Algorithm via Lévy flight has outperformed others and for multimodal optimization Krill Herd algorithm is superior than Firefly algorithm but for unimodal optimization Firefly is superior than Krill Herd algorithm.
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs
Directory of Open Access Journals (Sweden)
Zijian Cao
2015-01-01
Full Text Available Brain Storm Optimization (BSO algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.
A Computationally Efficient Aggregation Optimization Strategy of Model Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Model Predictive Control (MPC) is a popular technique and has been successfully used in various industrial applications. However, the big drawback of MPC involved in the formidable on-line computational effort limits its applicability to relatively slow and/or small processes with a moderate number of inputs. This paper develops an aggregation optimization strategy for MPC that can improve the computational efficiency of MPC. For the regulation problem, an input decaying aggregation optimization algorithm is presented by aggregating all the original optimized variables on control horizon with the decaying sequence in respect of the current control action.
ERRATUM: TOWARDS ACTIVE SEO (SEARCH ENGINE OPTIMIZATION 2.0
Directory of Open Access Journals (Sweden)
Charles-Victor Boutet
2013-04-01
Full Text Available In the age of writable web, new skills and new practices are appearing. In an environment that allows everyone to communicate information globally, internet referencing (or SEO is a strategic discipline that aims to generate visibility, internet traffic and a maximum exploitation of sites publications. Often misperceived as a fraud, SEO has evolved to be a facilitating tool for anyone who wishes to reference their website with search engines. In this article we show that it is possible to achieve the first rank in search results of keywords that are very competitive. We show methods that are quick, sustainable and legal; while applying the principles of active SEO 2.0. This article also clarifies some working functions of search engines, some advanced referencing techniques (that are completely ethical and legal and we lay the foundations for an in depth reflection on the qualities and advantages of these techniques.
TOWARDS ACTIVE SEO (SEARCH ENGINE OPTIMIZATION 2.0
Directory of Open Access Journals (Sweden)
Charles-Victor Boutet
2012-12-01
Full Text Available In the age of writable web, new skills and new practices are appearing. In an environment that allows everyone to communicate information globally, internet referencing (or SEO is a strategic discipline that aims to generate visibility, internet traffic and a maximum exploitation of sites publications. Often misperceived as a fraud, SEO has evolved to be a facilitating tool for anyone who wishes to reference their website with search engines. In this article we show that it is possible to achieve the first rank in search results of keywords that are very competitive. We show methods that are quick, sustainable and legal; while applying the principles of active SEO 2.0. This article also clarifies some working functions of search engines, some advanced referencing techniques (that are completely ethical and legal and we lay the foundations for an in depth reflection on the qualities and advantages of these techniques.
Chaotic Charged System Search with a Feasible-Based Method for Constraint Optimization Problems
Directory of Open Access Journals (Sweden)
B. Nouhi
2013-01-01
Full Text Available Recently developed chaotic charged system search was combined to feasible-based method to solve constraint engineering optimization problems. Using chaotic maps into the CSS increases the global search mobility for a better global optimization. In the present method, an improved feasible-based method is utilized to handle the constraints. Some constraint design examples are tested using the new chaotic-based methods, and the results are compared to recognize the most efficient and powerful algorithm.
Search Engine Optimization for Flash Best Practices for Using Flash on the Web
Perkins, Todd
2009-01-01
Search Engine Optimization for Flash dispels the myth that Flash-based websites won't show up in a web search by demonstrating exactly what you can do to make your site fully searchable -- no matter how much Flash it contains. You'll learn best practices for using HTML, CSS and JavaScript, as well as SWFObject, for building sites with Flash that will stand tall in search rankings.
Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle
Directory of Open Access Journals (Sweden)
Linhui Li
2014-01-01
Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.
Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm
Directory of Open Access Journals (Sweden)
Yu-Huei Cheng
2017-03-01
Full Text Available Hybrid electric vehicle (HEV control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a “memetic algorithm (MA” to optimize the control parameters in parallel HEVs. The “local search” mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR and is set up according to an electric assist control strategy (EACS to minimize the fuel consumption (FC and emissions (HC, CO, and NOx of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC, Federal Test Procedure (FTP, Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC, and urban dynamometer driving schedule (UDDS are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.
Logic synthesis strategy based on BDD decomposition and PAL-oriented optimization
Opara, Adam; Kania, Dariusz
2015-12-01
A new strategy of logic synthesis for PAL-based CPLDs is presented in the paper. This approach consists of an original method of two-stage BDD-based decomposition and a two-level PAL-oriented optimization. The aim of the proposed approach is oriented towards balanced (speed/area) optimization. The first element of the strategy is original PAL-oriented decomposition. This decomposition consists in the sequential search for an input partition providing the feasibility for implementation of the free block in one PAL-based logic block containing a predefined number of product terms. The presented non-standard decomposition provides a means to minimize the area of the implemented circuit and to reduce of the necessary logic blocks in the programmable structure. The second element of the proposed logic synthesis strategy is oriented towards speed optimization. This optimization is based on utilizing tri-state buffers. Results of experiments prove that the presented synthesis strategy is especially effective for CPLD structures, which consist of PAL-based logic blocks containing a low number of product terms.
In Search of the Optimal Fund of Hedge Funds
Harry M. Kat
2002-01-01
In this paper we investigate whether it is possible for a fund of hedge funds to not only offer investors access to a diversified basket of hedge funds but to provide skewness protection at the same time. We study two different strategies. The first is for a fund to buy stock index puts and leverage itself, in line with the skewness reduction strategy proposed earlier in Kat (2002). In general, the latter strategy is too dependent on the actual asset allocation strategy followed by investors ...
Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences
Directory of Open Access Journals (Sweden)
Bala Murali Gunji
2017-06-01
Full Text Available An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS in combination with the Genetic Algorithm (GA to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1 Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2 Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques.
Optimal scan strategies for future CMB satellite experiments
Wallis, Christopher G R; Battye, Richard A; Delabrouille, Jacques
2016-01-01
The B-mode polarisation power spectrum in the Cosmic Microwave Background (CMB) is about four orders of magnitude fainter than the CMB temperature power spectrum. Any instrumental imperfections that couple temperature fluctuations to B-mode polarisation must therefore be carefully controlled and/or removed. We investigate the role that a scan strategy can have in mitigating certain common systematics by averaging systematic errors down with many crossing angles. We present approximate analytic forms for the error on the recovered B-mode power spectrum that would result from differential gain, differential pointing and differential ellipticity for the case where two detector pairs are used in a polarisation experiment. We use these analytic predictions to search the parameter space of common satellite scan strategies in order to identify those features of a scan strategy that have most impact in mitigating systematic effects. As an example we go on to identify a scan strategy suitable for the CMB satellite pro...
Optimal search and ambush for a hider who can escape the search region
Alpern, Steve; Fokkink, Robbert; Simanjuntak, Martin
2016-01-01
Search games for a mobile or immobile hider traditionally have the hider permanently confined to a compact ‘search region’ making eventual capture inevitable. Hence the payoff can be taken as time until capture. However in many real life search problems it is possible for the hider to escape an area in which he was known to be located (e.g. Bin Laden from Tora Bora) or for a prey animal to escape a predator’s hunting territory. We model and solve such continuous time problems with escape wher...
SHERLOCK: A quasi-model-independent new physics search strategy.
Knuteson, Bruce
2000-04-01
We develop a quasi-model-independent prescription for searching for physics responsible for the electroweak symmetry breaking in the Standard Model, and show a preliminary version of what we find when this prescription is applied to the DZero data.
Search Strategies for Top Partners in Composite Higgs models
Gripaios, Ben; Parker, M A; Sutherland, Dave
2014-01-01
We consider how best to search for top partners in generic composite Higgs models. We begin by classifying the possible group representations carried by top partners in models with and without a custodial $SU(2)\\times SU(2) \\rtimes \\mathbb{Z}_2$ symmetry protecting the rate for $Z \\rightarrow b\\overline{b}$ decays. We identify a number of minimal models whose top partners only have electric charges of $\\frac{1}{3}, \\frac{2}{3},$ or $\\frac{4}{3}$ and thus decay to top or bottom quarks via a single Higgs or electroweak gauge boson. We develop an inclusive search for these based on a top veto, which we find to be more effective than existing searches. Less minimal models feature light states that can be sought in final states with like-sign leptons and so we find that 2 straightforward LHC searches give a reasonable coverage of the gamut of composite Higgs models.
Strategy in Educational Leadership: In Search of Unity
Eacott, Scott
2008-01-01
Purpose: The purpose of this paper is to examine knowledge of strategy within the field of educational administration. It is intended to be the basis for future empirical research and inquiry into strategy in education by suggesting alternate ways of defining and researching strategy. Design/methodology/approach: The study examines the…
Search Trees with Relaxed Balance and Near-Optimal Height
DEFF Research Database (Denmark)
Fagerberg, Rolf; Larsen, Kim Skak; Jensen, Rune E.
2001-01-01
We introduce a relaxed k-tree, a search tree with relaxed balance and a height bound, when in balance, of (1+epsilon)log_2 n + 1, for any epsilon > 0. The number of nodes involved in rebalancing is O(1/epsilon) per update in the amortized sense, and O(log n/epsilon) in the worst case sense. This ...... constant rebalancing, which is an improvement over the current definition. World Wide Web search engines are possible applications for this line of work.......We introduce a relaxed k-tree, a search tree with relaxed balance and a height bound, when in balance, of (1+epsilon)log_2 n + 1, for any epsilon > 0. The number of nodes involved in rebalancing is O(1/epsilon) per update in the amortized sense, and O(log n/epsilon) in the worst case sense....... This is the first binary search tree with relaxed balance having a height bound better than c log_2 n for a fixed constant c. In all previous proposals, the constant is at least 1/log_2 phi>1.44, where phi is the golden ratio. As a consequence, we can also define a standard (non-relaxed) k-tree with amortized...
Optimal Finger Search Trees in the Pointer Machine
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Lagogiannis, George; Makris, Christos
2003-01-01
We develop a new finger search tree with worst-case constant update time in the Pointer Machine (PM) model of computation. This was a major problem in the field of Data Structures and was tantalizingly open for over twenty years while many attempts by researchers were made to solve it. The result...
Strategy Effects on Word Searching in Japanese Letter Fluency Tests: Evidence from the NIRS Findings
Hatta, Takeshi; Kanari, Ayano; Mase, Mitsuhito; Nagano, Yuko; Shirataki, Tatsuaki; Hibino, Shinji
2009-01-01
Strategy effects on word searching in the Japanese letter fluency test were investigated using the Near-infrared Spectroscopy (NIRS). Participants were given a Japanese letter fluency test and they were classified into two types of strategy users, based on analysis of their recorded verbal responses. One group, AIUEO-order strategy users, employed…
Deterministic oscillatory search: a new meta-heuristic optimization algorithm
Indian Academy of Sciences (India)
N ARCHANA; R VIDHYAPRIYA; ANTONY BENEDICT; KARTHIK CHANDRAN
2017-06-01
The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. Theresults show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.
Search for the optimality signature of river network development.
Paik, Kyungrock
2012-10-01
Whether the evolution of natural river networks pursues a certain optimal state has been a most intriguing and fundamental question. There have been many optimality hypotheses proposed but it has yet to be proved which of these best serves as a quantitative signature of river network development. Here, this fundamental question is investigated for the five hypotheses of "minimum total energy expenditure," "minimum total energy dissipation rate," "minimum total stream power," "minimum global energy expenditure rate," and "minimum topological energy." Using simple example landscapes, I examined whether any of these hypotheses pursues both the treelike river network formation and the concave stream longitudinal profile, the two characteristic patterns of natural landscapes. It is found that none of these hypotheses captures both patterns under the steady-state condition where the balance between tectonic uplift and sediment loss is satisfied. These findings are further verified through simulations of landscapes that satisfy given optimality criteria using an optimization method.
Optimization of Transformation Coefficients Using Direct Search and Swarm Intelligence
Directory of Open Access Journals (Sweden)
Manusov V.Z.
2017-04-01
Full Text Available This research considers optimization of tap position of transformers in power systems to reduce power losses. Now, methods based on heuristic rules and fuzzy logic, or methods that optimize parts of the whole system separately, are applied to this problem. The first approach requires expert knowledge about processes in the network. The second methods are not able to consider all the interrelations of system’s parts, while changes in segment affect the entire system. Both approaches are tough to implement and require adjustment to the tasks solved. It needs to implement algorithms that can take into account complex interrelations of optimized variables and self-adapt to optimization task. It is advisable to use algorithms given complex interrelations of optimized variables and independently adapting from optimization tasks. Such algorithms include Swarm Intelligence algorithms. Their main features are self-organization, which allows them to automatically adapt to conditions of tasks, and the ability to efficiently exit from local extremes. Thus, they do not require specialized knowledge of the system, in contrast to fuzzy logic. In addition, they can efficiently find quasi-optimal solutions converging to the global optimum. This research applies Particle Swarm Optimization algorithm (PSO. The model of Tajik power system used in experiments. It was found out that PSO is much more efficient than greedy heuristics and more flexible and easier to use than fuzzy logic. PSO allows reducing active power losses from 48.01 to 45.83 MW (4.5%. With al, the effect of using greedy heuristics or fuzzy logic is two times smaller (2.3%.
Immune clonal selection optimization method with combining mutation strategies
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.
Optimal search in interacting populations:Gaussian jumps vs Levy flights
Martínez-García, Ricardo; Calabrese, Justin; López, Cristóbal
2014-01-01
We investigated the relationships between search efficiency, movement strategy, and nonlocal communication in the biological context of animal foraging. We considered situations where the members of a population of foragers perform either Gaussian jumps or Lévy flights, and show that the search time is minimized when communication among individuals occurs at intermediate ranges, independently of the type of movement. Additionally, while Brownian strategies are more strongly influenced by the ...
Optimal switching strategies for stochastic geocentric/egocentric navigation
Peleg, O
2015-01-01
Animals use a combination of egocentric navigation driven by the internal integration of environmental cues, interspersed with geocentric course correction and reorientation, often with uncertainty in sensory acquisition of information, planning and execution. Inspired directly by observations of dung beetle navigational strategies that show switching between geocentric and egocentric strategies, we consider the question of optimal strategies for the navigation of an agent along a preferred direction in the presence of multiple sources of noise. We address this using a model that takes the form of a correlated random walk at short time scales that is interspersed with reorientation events that yields a biased random walks at long time scales. We identify optimal alternation schemes and characterize their robustness in the context of noisy sensory acquisition, and performance errors linked with variations in environmental conditions and agent-environment interactions.
Abedini, M. J.; Nasseri, M.; Burn, D. H.
2012-04-01
In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.
PEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid Vehicle
Directory of Open Access Journals (Sweden)
Tinton Dwi Atmaja
2012-02-01
Full Text Available Page HeaderOpen Journal SystemsJournal HelpUser You are logged in as...aulia My Journals My Profile Log Out Log Out as UserNotifications View (27 new ManageJournal Content SearchBrowse By Issue By Author By Title Other JournalsFont SizeMake font size smaller Make font size default Make font size largerInformation For Readers For Authors For LibrariansKeywords CBPNN Displacement FLC LQG/LTR Mixed PMA Ventilation bottom shear stress direct multiple shooting effective fuzzy logic geoelectrical method hourly irregular wave missile trajectory panoramic image predator-prey systems seawater intrusion segmentation structure development pattern terminal bunt manoeuvre Home About User Home Search Current Archives ##Editorial Board##Home > Vol 23, No 1 (2012 > AtmajaPEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid VehicleTinton Dwi Atmaja, Amin AminAbstractone of the present-day implementation of fuel cell is acting as main power source in Fuel Cell Hybrid Vehicle (FCHV. This paper proposes some strategies to optimize the performance of Polymer Electrolyte Membrane Fuel Cell (PEMFC implanted with auxiliary power source to construct a proper FCHV hybridization. The strategies consist of the most updated optimization method determined from three point of view i.e. Energy Storage System (ESS, hybridization topology and control system analysis. The goal of these strategies is to achieve an optimum hybridization with long lifetime, low cost, high efficiency, and hydrogen consumption rate improvement. The energy storage system strategy considers battery, supercapacitor, and high-speed flywheel as the most promising alternative auxiliary power source. The hybridization topology strategy analyzes the using of multiple storage devices injected with electronic components to bear a higher fuel economy and cost saving. The control system strategy employs nonlinear control system to optimize the ripple factor of the voltage and the current
Solution of Chemical Dynamic Optimization Using the Simultaneous Strategies
Institute of Scientific and Technical Information of China (English)
LIU Xinggao; CHEN Long; HU Yunqing
2013-01-01
An approach of simultaneous strategies with two novel techniques is proposed to improve the solution accuracy of chemical dynamic optimization problems.The first technique is to handle constraints on control variables based on the finite-element collocation so as to control the approximation error for discrete optimal problems,where a set of control constraints at element knots are integrated with the procedure for optimization leading to a significant gain in the accuracy of the simultaneous strategies.The second technique is to make the mesh refinement more feasible and reliable by introducing length constraints and guideline in designing appropriate element length boundaries,so that the proposed approach becomes more efficient in adjusting elements to track optimal control profile breakpoints and ensure accurate state and control profiles.Four classic benchmarks of dynamic optimization problems are used as illustrations,and the proposed approach is compared with literature reports.The research results reveal that the proposed approach is preferable in improving the solution accuracy of chemical dynamic optimization problem.
Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei
2014-04-01
Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.
Optimal HP configurations of proteins by combining local search with elastic net algorithm.
Guo, Yu-Zhen; Feng, En-Min; Wang, Yong
2007-04-10
The prediction of protein conformation from its amino-acid sequence is one of the most prominent problems in computational biology. But it is NP-hard. Here, we focus on an abstraction widely studied of this problem, the two-dimensional hydrophobic-polar protein folding problem (2D HP PFP). Mathematical optimal model of free energy of protein is established. Native conformations are often sought using stochastic sampling methods, but which are slow. The elastic net (EN) algorithm is one of fast deterministic methods as travelling salesman problem (TSP) strategies. However, it cannot be applied directly to protein folding problem, because of fundamental differences in the two types of problems. In this paper, how the 2D HP protein folding problem can be framed in terms of TSP is shown. Combination of the modified elastic net algorithm and novel local search method is adopted to solve this problem. To our knowledge, this is the first application of EN algorithm to 2D HP model. The results indicate that our approach can find more optimal conformations and is simple to implement, computationally efficient and fast.
Fischer, Steve; Aurrecoechea, Cristina; Brunk, Brian P; Gao, Xin; Harb, Omar S; Kraemer, Eileen T; Pennington, Cary; Treatman, Charles; Kissinger, Jessica C; Roos, David S; Stoeckert, Christian J
2011-01-01
Web sites associated with the Eukaryotic Pathogen Bioinformatics Resource Center (EuPathDB.org) have recently introduced a graphical user interface, the Strategies WDK, intended to make advanced searching and set and interval operations easy and accessible to all users. With a design guided by usability studies, the system helps motivate researchers to perform dynamic computational experiments and explore relationships across data sets. For example, PlasmoDB users seeking novel therapeutic targets may wish to locate putative enzymes that distinguish pathogens from their hosts, and that are expressed during appropriate developmental stages. When a researcher runs one of the approximately 100 searches available on the site, the search is presented as a first step in a strategy. The strategy is extended by running additional searches, which are combined with set operators (union, intersect or minus), or genomic interval operators (overlap, contains). A graphical display uses Venn diagrams to make the strategy's flow obvious. The interface facilitates interactive adjustment of the component searches with changes propagating forward through the strategy. Users may save their strategies, creating protocols that can be shared with colleagues. The strategy system has now been deployed on all EuPathDB databases, and successfully deployed by other projects. The Strategies WDK uses a configurable MVC architecture that is compatible with most genomics and biological warehouse databases, and is available for download at code.google.com/p/strategies-wdk. Database URL: www.eupathdb.org.
Weerathunga, Thilina Shihan
2017-08-01
Gravitational waves are a fundamental prediction of Einstein's General Theory of Relativity. The first experimental proof of their existence was provided by the Nobel Prize winning discovery by Taylor and Hulse of orbital decay in a binary pulsar system. The first detection of gravitational waves incident on earth from an astrophysical source was announced in 2016 by the LIGO Scientific Collaboration, launching the new era of gravitational wave (GW) astronomy. The signal detected was from the merger of two black holes, which is an example of sources called Compact Binary Coalescences (CBCs). Data analysis strategies used in the search for CBC signals are derivatives of the Maximum-Likelihood (ML) method. The ML method applied to data from a network of geographically distributed GW detectors--called fully coherent network analysis--is currently the best approach for estimating source location and GW polarization waveforms. However, in the case of CBCs, especially for lower mass systems (O(1M solar masses)) such as double neutron star binaries, fully coherent network analysis is computationally expensive. The ML method requires locating the global maximum of the likelihood function over a nine dimensional parameter space, where the computation of the likelihood at each point requires correlations involving O(104) to O(106) samples between the data and the corresponding candidate signal waveform template. Approximations, such as semi-coherent coincidence searches, are currently used to circumvent the computational barrier but incur a concomitant loss in sensitivity. We explored the effectiveness of Particle Swarm Optimization (PSO), a well-known algorithm in the field of swarm intelligence, in addressing the fully coherent network analysis problem. As an example, we used a four-detector network consisting of the two LIGO detectors at Hanford and Livingston, Virgo and Kagra, all having initial LIGO noise power spectral densities, and show that PSO can locate the global
Optimization of a genetic algorithm for searching molecular conformer space
Brain, Zoe E.; Addicoat, Matthew A.
2011-11-01
We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA - a meta-genetic algorithm - was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.
Online consumer search strategies for smoking-cessation information.
Cobb, Nathan K
2010-03-01
For many Americans, the Internet has become a primary mechanism for locating information on healthcare and treatment options, including tobacco addiction. Detailed information on this behavior could inform design decisions for next-generation cessation interventions, but very little is known about how consumers search or what resources they locate. A subset of a publicly available, anonymized record of the search behavior of 650,000 individuals over 3 months in 2006 was analyzed. Smoking cessation-related queries were extracted and coded via manual identification of terms and by back-identifying terms by matching them to the websites ultimately visited. Destination sites were coded as to whether or not they originated from a professional source based on the literature and known healthcare organizations. A total of 628 individuals (0.10%) made 1106 cessation-related searches during the observation period. Of these, 76% resulted in the individual reaching a website; professional sites were reached by only 34% of searchers. Complementary or alternative therapies were popular, with 10% of individuals searching for "laser" therapy. A concerning disconnect exists between consumer demand (as demonstrated by search behavior) and the sites produced by researchers and health professionals. This "demand gap" may contribute to low overall participation rates and hamper the potential impact of such systems. Further research is needed to link online consumer preferences to intervention design decisions. 2010 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
Galvan-Sosa, M.; Portilla, J.; Hernandez-Rueda, J.; Siegel, J.; Moreno, L.; Solis, J.
2014-09-01
In this work, we have developed and implemented a powerful search strategy for optimization of nonlinear optical effects by means of femtosecond pulse shaping, based on topological concepts derived from quantum control theory. Our algorithm [Multiple One-Dimensional Search (MODS)] is based on deterministic optimization of a single solution rather than pseudo-random optimization of entire populations as done by commonly used evolutionary algorithms. We have tested MODS against a genetic algorithm in a nontrivial problem consisting in optimizing the Kerr gating signal (self-interaction) of a shaped laser pulse in a detuned Michelson interferometer configuration. The obtained results show that our search method (MODS) strongly outperforms the genetic algorithm in terms of both convergence speed and quality of the solution. These findings demonstrate the applicability of concepts of quantum control theory to nonlinear laser-matter interaction problems, even in the presence of significant experimental noise.
The Optimal Taxation of UnskilIed Labor with Job Search and Social Assistance
Boone, J.; Bovenberg, A.L.
2002-01-01
In order to explore the optimal taxation of low-skilled labor, we extend the standard model of optimal non-linear income taxation in the presence of quasi-linear preferences in leisure by allowing for involuntary unemployment, job search, an exogenous welfare benefit, and a non-utilitarian social we
An unconstrained optimization method using nonmonotone second order Goldstein's line search
Institute of Scientific and Technical Information of China (English)
Wen-yu; SUN; Qun-yan; ZHOU
2007-01-01
In this paper, an unconstrained optimization method using the nonmonotone second order Goldstein's line search is proposed. By using the negative curvature information from the Hessian,the sequence generated is shown to converge to a stationary point with the second order optimality conditions. Numerical tests on a set of standard test problems confirm the efficiency of our new method.
A Wave Implementation of the Optimal Database Search Algorithm
Patel, A
2004-01-01
Grover's database search algorithm, although discovered in the context of quantum computation, can be implemented using any system that allows superposition of states. A physical realization of this algorithm is described using coupled simple harmonic oscillators. Such classical wave implementations are far more stable against decoherence compared to their quantum counterparts. In addition to providing convenient demonstration models, they may have a role in diverse practical situations, such as catalysis and structure of genetic languages.
Adaptation of Decoy Fusion Strategy for Existing Multi-Stage Search Workflows
Ivanov, Mark V.; Levitsky, Lev I.; Gorshkov, Mikhail V.
2016-09-01
A number of proteomic database search engines implement multi-stage strategies aiming at increasing the sensitivity of proteome analysis. These approaches often employ a subset of the original database for the secondary stage of analysis. However, if target-decoy approach (TDA) is used for false discovery rate (FDR) estimation, the multi-stage strategies may violate the underlying assumption of TDA that false matches are distributed uniformly across the target and decoy databases. This violation occurs if the numbers of target and decoy proteins selected for the second search are not equal. Here, we propose a method of decoy database generation based on the previously reported decoy fusion strategy. This method allows unbiased TDA-based FDR estimation in multi-stage searches and can be easily integrated into existing workflows utilizing popular search engines and post-search algorithms.
Metagenomic search strategies for interactions among plants and multiple microbes
Directory of Open Access Journals (Sweden)
Ulrich Karl Melcher
2014-06-01
Full Text Available Plants harbor multiple microbes. Metagenomics can facilitate understanding of the significance, for the plant, of the microbes and of the interactions among them. However, current approaches to metagenomic analysis of plants are computationally time-consuming. Efforts to speed the discovery process include improvement of computational speed, condensing the sequencing reads into smaller datasets before BLAST searches, simplifying the target database of BLAST searches, and flipping the roles of metagenomic and reference datasets. The latter is exemplified by the E-probe diagnostic nucleic acid analysis (EDNA approach originally devised for improving analysis during plant quarantine.
Fish School Search Algorithm for Solving Optimal Reactive Power Dispatch Problem
Directory of Open Access Journals (Sweden)
K. Lenin
2013-01-01
Full Text Available This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. This paper presents fish school search a novel method of swarm intelligence for solving above problem. Fish school search Algorithm, which was inspired by the natural schooling behaviours of fish, a powerful stochastic optimization technique has been utilised to solve the reactive power optimization problem.
On the robust optimization to the uncertain vaccination strategy problem
Energy Technology Data Exchange (ETDEWEB)
Chaerani, D., E-mail: d.chaerani@unpad.ac.id; Anggriani, N., E-mail: d.chaerani@unpad.ac.id; Firdaniza, E-mail: d.chaerani@unpad.ac.id [Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Padjadjaran Indonesia, Jalan Raya Bandung Sumedang KM 21 Jatinangor Sumedang 45363 (Indonesia)
2014-02-21
In order to prevent an epidemic of infectious diseases, the vaccination coverage needs to be minimized and also the basic reproduction number needs to be maintained below 1. This means that as we get the vaccination coverage as minimum as possible, thus we need to prevent the epidemic to a small number of people who already get infected. In this paper, we discuss the case of vaccination strategy in term of minimizing vaccination coverage, when the basic reproduction number is assumed as an uncertain parameter that lies between 0 and 1. We refer to the linear optimization model for vaccination strategy that propose by Becker and Starrzak (see [2]). Assuming that there is parameter uncertainty involved, we can see Tanner et al (see [9]) who propose the optimal solution of the problem using stochastic programming. In this paper we discuss an alternative way of optimizing the uncertain vaccination strategy using Robust Optimization (see [3]). In this approach we assume that the parameter uncertainty lies within an ellipsoidal uncertainty set such that we can claim that the obtained result will be achieved in a polynomial time algorithm (as it is guaranteed by the RO methodology). The robust counterpart model is presented.
Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search
Directory of Open Access Journals (Sweden)
Zong Woo Geem
2015-07-01
Full Text Available Thus far, various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping, ant colony optimization, harmony search, cross entropy, scatter search, and honey-bee mating, have been proposed to optimally design the water distribution networks with respect to design cost. However, flow velocity constraint, which is critical for structural robustness against water hammer or flow circulation against substance sedimentation, was seldom considered in the optimization formulation because of computational complexity. Thus, this study proposes a novel fuzzy-based velocity reliability index, which is to be maximized while the design cost is simultaneously minimized. The velocity reliability index is included in the existing cost optimization formulation and this extended multiobjective formulation is applied to two bench-mark problems. Results show that the model successfully found a Pareto set of multiobjective design solutions in terms of cost minimization and reliability maximization.
A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.
Optimal Polygonal Approximation of Digital Planar Curves Using Genetic Algorithm and Tabu Search
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Three heuristic algorithms for optimal polygonal approximation of digital planar curves is presented.With Genetic Algorithm (GA), improved Genetic Algorithm (IGA) based on Pareto optimal solution and Tabu Search (TS), a near optimal polygonal approximation was obtained.Compared to the famous Teh-chin algorithm, our algorithms have obtained the approximated polygons with less number of vertices and less approximation error.Compared to the dynamic programming algorithm, the processing time of our algorithms are much less expensive.
Kanagaraj, G.; Ponnambalam, S. G.; Jawahar, N.; Mukund Nilakantan, J.
2014-10-01
This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.
An optimal routing strategy on scale-free networks
Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Qi, Zhaohui; Zhao, Yongbin
Traffic is one of the most fundamental dynamical processes in networked systems. With the traditional shortest path routing (SPR) protocol, traffic congestion is likely to occur on the hub nodes on scale-free networks. In this paper, we propose an improved optimal routing (IOR) strategy which is based on the betweenness centrality and the degree centrality of nodes in the scale-free networks. With the proposed strategy, the routing paths can accurately bypass hub nodes in the network to enhance the transport efficiency. Simulation results show that the traffic capacity as well as some other indexes reflecting transportation efficiency are further improved with the IOR strategy. Owing to the significantly improved traffic performance, this study is helpful to design more efficient routing strategies in communication or transportation systems.
Optimal scan strategies for future CMB satellite experiments
Wallis, Christopher G. R.; Brown, Michael L.; Battye, Richard A.; Delabrouille, Jacques
2017-04-01
The B-mode polarization power spectrum in the cosmic microwave background (CMB) is about four orders of magnitude fainter than the CMB temperature power spectrum. Any instrumental imperfections that couple temperature fluctuations to B-mode polarization must therefore be carefully controlled and/or removed. We investigate the role that a scan strategy can have in mitigating certain common systematics by averaging systematic errors down with many crossing angles. We present approximate analytic forms for the error on the recovered B-mode power spectrum that would result from differential gain, differential pointing and differential ellipticity for the case where two detector pairs are used in a polarization experiment. We use these analytic predictions to search the parameter space of common satellite scan strategies in order to identify those features of a scan strategy that have most impact in mitigating systematic effects. As an example, we go on to identify a scan strategy suitable for the CMB satellite proposed for the European Space Agency M5 call, considering the practical considerations of fuel requirement, data rate and the relative orientation of the telescope to the earth. Having chosen a scan strategy we then go on to investigate the suitability of the scan strategy.
Alumni Job Search Strategies, Class of 2011. GMAC[R] Data-to-Go Series
Graduate Management Admission Council, 2012
2012-01-01
Examining the job search strategies and employment outcomes for Class of 2011 graduate business school alumni sheds light on current job market trends and the effort required to secure a first job after earning a graduate business degree. This fact sheet highlights the job search methods used by Class of 2011 business school graduates as reported…
An exploratory study of user goals and strategies in podcast search
Besser, J.; Hofmann, K.; Larson, M.; Mandl, T.; Fuhr, N.; Henrich, A.
2008-01-01
We report on an exploratory, qualitative user study designed to identify users’ goals underlying podcast search, the strategies used to gain access to podcasts, and how currently available tools influence podcast search. We employed a multi-method approach. First, we conducted an online survey to
Hsu, Chung-Yuan; Tsai, Meng-Jung; Hou, Huei-Tse; Tsai, Chin-Chung
2014-01-01
Online information searching tasks are usually implemented in a technology-enhanced science curriculum or merged in an inquiry-based science curriculum. The purpose of this study was to examine the role students' different levels of scientific epistemic beliefs (SEBs) play in their online information searching strategies and behaviors. Based on…
Talk as a Metacognitive Strategy during the Information Search Process of Adolescents
Bowler, Leanne
2010-01-01
Introduction: This paper describes a metacognitive strategy related to the social dimension of the information search process of adolescents. Method: A case study that used naturalistic methods to explore the metacognitive thinking nd associated emotions of ten adolescents. The study was framed by Kuhlthau's Information Search Process model and…
An exploratory study of user goals and strategies in podcast search
Besser, J.; Hofmann, K.; Larson, M.; Mandl, T.; Fuhr, N.; Henrich, A.
2008-01-01
We report on an exploratory, qualitative user study designed to identify users’ goals underlying podcast search, the strategies used to gain access to podcasts, and how currently available tools influence podcast search. We employed a multi-method approach. First, we conducted an online survey to ob
Pesquisas na web: estratégias de busca Searching on the web: search strategies p. 53-66
Directory of Open Access Journals (Sweden)
Elias Estevão Goulart
2007-01-01
Full Text Available A World Wide Web tem sido utilizada amplamente para a busca e seleção de informações, resultando em um de seus principais empregos como suporte para atividades acadêmicas e profissionais. Este trabalho apresenta um estudo sobre as estratégias de busca de informações na World Wide Web, visando analisar e comparar os resultados de uma pesquisa exploratória com estudo similar realizado na Universidade de Telaviv. Apresenta-se nove formas possíveis de buscas e como elas foram utilizadas nos estudos comparados. Como resultado, são apresentadas as mais efetivas e sugere-se melhor treinamento dos usuários para o conhecimento das técnicas apresentadas. Palavras-chave Estratégias de busca; Internet; World wide web Abstract The World Wide Web has been largely used for searching and selecting information, and is one of the most important tools to support academic and professional activities. This work presents a study about information search strategies on the world wide web, seeking to analyze and compare the results of a similar exploratory research implemented at Telaviv University. It presents nine possible ways of information search and how they were compared in both studies. As a result, the most effective of the strategies are presented and users training are suggested as the best way to make them aware of the discussed techniques. Key words Search strategies; Internet; World wide web
Proximity search heuristics for wind farm optimal layout
DEFF Research Database (Denmark)
Fischetti, Martina; Monaci, Michele
2016-01-01
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-...
The Database Dilemma: Online Search Strategies in Nursing.
Fried, Ava K.; And Others
1989-01-01
Describes a study that compared the coverage of the nursing profession, subject heading specificity, and ease of retrieval of the MEDLINE and Nursing & Allied Health (CINAHL) online databases. The strengths and weaknesses of each database are discussed and hints for searching on both databases are provided. (four references) (CLB)
KAISA HENTTONEN; PAAVO RITALA; TIINA JAUHIAINEN
2011-01-01
Given Chesbrough's idea of open innovation, it could be said that external knowledge is an important element in the optimisation of in-house innovation. External knowledge is distributed among various actors and is accessible through many channels. However, we still do not know much about the search strategies that affect innovation performance. Our study therefore explores the relationship between open knowledge search strategies and company-level innovative performance. This study examines ...
Research of stochastic weight strategy for extended particle swarm optimizer
Institute of Scientific and Technical Information of China (English)
XU Jun-jie; YUE Xin; XIN Zhan-hong
2008-01-01
To improve the performance of extended particle swarm optimizer, a novel means of stochastic weight deployment is proposed for the iterative equation of velocity updation. In this scheme, one of the weights is specified to a random number within the range of [0, 1] and the other two remain constant configurations. The simulations show that this weight strategy outperforms the previous deterministic approach with respect to success rate and convergence speed. The experi- ments also reveal that if the weight for global best neighbor is specified to a stochastic number, extended particle swarm optimizer achieves high and robust performance on the given multi-modal function.
Energy Technology Data Exchange (ETDEWEB)
Ji, Aimin; Yin, Xu; Yuan, Minghai [Hohai University, Changzhou (China)
2015-09-15
There are two problems in Collaborative optimization (CO): (1) the local optima arising from the selection of an inappropriate initial point; (2) the low efficiency and accuracy root in inappropriate relaxation factors. To solve these problems, we first develop the Latin hypercube design (LHD) to determine an initial point of optimization, and then use the non-linear programming by quadratic Lagrangian (NLPQL) to search for the global solution. The effectiveness of the initial point selection strategy is verified by three benchmark functions with some dimensions and different complexities. Then we propose the Adaptive relaxation collaborative optimization (ARCO) algorithm to solve the inconsistency between the system level and the disciplines level, and in this method, the relaxation factors are determined according to the three separated stages of CO respectively. The performance of the ARCO algorithm is compared with the standard collaborative algorithm and the constant relaxation collaborative algorithm with a typical numerical example, which indicates that the ARCO algorithm is more efficient and accurate. Finally, we propose a Hybrid collaborative optimization (HCO) approach, which integrates the selection strategy of initial point with the ARCO algorithm. The results show that HCO can achieve the global optimal solution without the initial value and it also has advantages in convergence, accuracy and robustness. Therefore, the proposed HCO approach can solve the CO problems with applications in the spindle and the speed reducer.
Automatic CT simulation optimization for radiation therapy: A general strategy
Energy Technology Data Exchange (ETDEWEB)
Li, Hua, E-mail: huli@radonc.wustl.edu; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M.; Mutic, Sasa [Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110 (United States); Yu, Lifeng [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States); Anastasio, Mark A. [Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63110 (United States); Low, Daniel A. [Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095 (United States)
2014-03-15
Purpose: In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. Methods: The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Results: Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube
Transitions in optimal adaptive strategies for populations in fluctuating environments
Mayer, Andreas; Mora, Thierry; Rivoire, Olivier; Walczak, Aleksandra M.
2017-09-01
Biological populations are subject to fluctuating environmental conditions. Different adaptive strategies can allow them to cope with these fluctuations: specialization to one particular environmental condition, adoption of a generalist phenotype that compromises between conditions, or population-wise diversification (bet hedging). Which strategy provides the largest selective advantage in the long run depends on the range of accessible phenotypes and the statistics of the environmental fluctuations. Here, we analyze this problem in a simple mathematical model of population growth. First, we review and extend a graphical method to identify the nature of the optimal strategy when the environmental fluctuations are uncorrelated. Temporal correlations in environmental fluctuations open up new strategies that rely on memory but are mathematically challenging to study: We present analytical results to address this challenge. We illustrate our general approach by analyzing optimal adaptive strategies in the presence of trade-offs that constrain the range of accessible phenotypes. Our results extend several previous studies and have applications to a variety of biological phenomena, from antibiotic resistance in bacteria to immune responses in vertebrates.
Survey of E-Commerce Modeling and Optimization Strategies
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Electronic commerce is impacting almost all commercial activities. The resulting emerging commercial activities bring with them many new modeling and optimization problems. This survey reviews pioneering works in this new area, covering topics in advertising strategy, web page design, automatic pricing, auction methods, brokerage strategy, and customer behavior analysis. Mathematical models for problems in these areas and their solution algorithms are discussed. In addition to presenting and commenting on these works, we also discuss possible extensions and related problems. The objective of this survey is to encourage more researchers to pay attention to this emerging area.
Acceleration of quantum optimal control theory algorithms with mixing strategies.
Castro, Alberto; Gross, E K U
2009-05-01
We propose the use of mixing strategies to accelerate the convergence of the common iterative algorithms utilized in quantum optimal control theory (QOCT). We show how the nonlinear equations of QOCT can be viewed as a "fixed-point" nonlinear problem. The iterative algorithms for this class of problems may benefit from mixing strategies, as it happens, e.g., in the quest for the ground-state density in Kohn-Sham density-functional theory. We demonstrate, with some numerical examples, how the same mixing schemes utilized in this latter nonlinear problem may significantly accelerate the QOCT iterative procedures.
Optimal Scheme for Search State Space and Scheduling on Multiprocessor Systems
Youness, Hassan A.; Sakanushi, Keishi; Takeuchi, Yoshinori; Salem, Ashraf; Wahdan, Abdel-Moneim; Imai, Masaharu
A scheduling algorithm aims to minimize the overall execution time of the program by properly allocating and arranging the execution order of the tasks on the core processors such that the precedence constraints among the tasks are preserved. In this paper, we present a new scheduling algorithm by using geometry analysis of the Task Precedence Graph (TPG) based on A* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity and pruning techniques to produce an optimal solution for the allocation/scheduling problem of a parallel application to parallel and multiprocessor architecture. The main goal of this work is to significantly reduce the search space and achieve the optimality or near optimal solution. We implemented the algorithm on general task graph problems that are processed on most of related search work and obtain the optimal scheduling with a small number of states. The proposed algorithm reduced the exhaustive search by at least 50% of search space. The viability and potential of the proposed algorithm is demonstrated by an illustrative example.
Using string invariants for prediction searching for optimal parameters
Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard
2016-02-01
We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.
Enrich the E-publishing Community Website with Search Engine Optimization Technique
Directory of Open Access Journals (Sweden)
Vadivel Rangasamy
2011-09-01
Full Text Available Internet has played vital role in the online business. Every business peoples are needed to show their information clients or end user. In search engines have million indexed pages. A search engine optimization technique has to implement both web applications static and dynamic. There is no issue for create search engine optimization contents to static (web contents does not change until that web site is re host web application and keep up the search engine optimization regulations and state of affairs. A few significant challenges to dynamic content poses. To overcome these challenges to have a fully functional dynamic site that is optimized as much as a static site can be optimized. Whatever user search and they can get information their information quickly. In that circumstance we are using few search engine optimization dynamic web application methods such as User Friendly URL's, URL Redirector and HTML Generic. Both internal and external elements of the site affect the way it's ranked in any given search engine, so all of these elements should be taken into consideration. Implement these concepts to E-publishing Community Website that web site have large amount of dynamic fields with dynamic validations with the help of XML, XSL Java script. A database plays a major role to accomplish this functionality. We can use 3D (static, dynamic and Meta database structures. One of the advantages of the XML/XSLT combination is the ability to separate content from presentation. A data source can return an XML document, then by using an XSLT, the data can be transformed into whatever HTML is needed, based on the data in the XML document. The flexibility of XML/XLST can be combined with the power of ASP.NET server/client controls by using an XSLT to generate the server/client controls dynamically, thus leveraging the best of both worlds.
Spatial Search by Quantum Walk is Optimal for Almost all Graphs.
Chakraborty, Shantanav; Novo, Leonardo; Ambainis, Andris; Omar, Yasser
2016-03-11
The problem of finding a marked node in a graph can be solved by the spatial search algorithm based on continuous-time quantum walks (CTQW). However, this algorithm is known to run in optimal time only for a handful of graphs. In this work, we prove that for Erdös-Renyi random graphs, i.e., graphs of n vertices where each edge exists with probability p, search by CTQW is almost surely optimal as long as p≥log^{3/2}(n)/n. Consequently, we show that quantum spatial search is in fact optimal for almost all graphs, meaning that the fraction of graphs of n vertices for which this optimality holds tends to one in the asymptotic limit. We obtain this result by proving that search is optimal on graphs where the ratio between the second largest and the largest eigenvalue is bounded by a constant smaller than 1. Finally, we show that we can extend our results on search to establish high fidelity quantum communication between two arbitrary nodes of a random network of interacting qubits, namely, to perform quantum state transfer, as well as entanglement generation. Our work shows that quantum information tasks typically designed for structured systems retain performance in very disordered structures.
Optimal intervention strategies for cholera outbreak by education and chlorination
Bakhtiar, Toni
2016-01-01
This paper discusses the control of infectious diseases in the framework of optimal control approach. A case study on cholera control was studied by considering two control strategies, namely education and chlorination. We distinct the former control into one regarding person-to-person behaviour and another one concerning person-to-environment conduct. Model are divided into two interacted populations: human population which follows an SIR model and pathogen population. Pontryagin maximum principle was applied in deriving a set of differential equations which consists of dynamical and adjoin systems as optimality conditions. Then, the fourth order Runge-Kutta method was exploited to numerically solve the equation system. An illustrative example was provided to assess the effectiveness of the control strategies toward a set of control scenarios.
A NEW STOCHASTIC OPTIMAL CONTROL STRATEGY FOR HYSTERETIC MR DAMPERS
Institute of Scientific and Technical Information of China (English)
YingZuguang; NiYiqing; KoJanming
2004-01-01
A new stochastic optimal control strategy for randomly excited quasi-integrable Hamiltonian systems using magneto-theological (MR) dampers is proposed. The dynamic behavior of an MR damper is characterized by the Bouc-Wen hysteretic model. The control force produced by the MR damper is separated into a passive part incorporated in the uncontrolled system and a semi-active part to be determined. The system combining the Bouc-Wen hysteretic force is converted into an equivalent non-hysteretic nonlinear stochastic control system. Then Ito stochastic differential equations are derived from the equivalent system by using the stochastic averaging method. A dynamical programming equation for the controlled diffusion processes is established based on the stochastic dynamical programming principle. The non-clipping nonlinear optimal control law is obtained for a certain performance index by minimizing the dynamical programming equation. Finally, an example is given to illustrate the application and effectiveness of the proposed control strategy.
Using Cotton Model Simulations to Estimate Optimally Profitable Irrigation Strategies
Mauget, S. A.; Leiker, G.; Sapkota, P.; Johnson, J.; Maas, S.
2011-12-01
In recent decades irrigation pumping from the Ogallala Aquifer has led to declines in saturated thickness that have not been compensated for by natural recharge, which has led to questions about the long-term viability of agriculture in the cotton producing areas of west Texas. Adopting irrigation management strategies that optimize profitability while reducing irrigation waste is one way of conserving the aquifer's water resource. Here, a database of modeled cotton yields generated under drip and center pivot irrigated and dryland production scenarios is used in a stochastic dominance analysis that identifies such strategies under varying commodity price and pumping cost conditions. This database and analysis approach will serve as the foundation for a web-based decision support tool that will help producers identify optimal irrigation treatments under specified cotton price, electricity cost, and depth to water table conditions.
Strategies for the search of life in the universe
Schneider, Jean
1996-01-01
The discovery of an increasing number of Jupiter-like planets in orbit around other stars (or extra-solar planets) is a promising first step toward the search for Life in the Universe. We review all aspects of the question: - definition of Life - definition and characterization of the `habitable zone' around a star - overview of detection methods of planets, with special attention to habitable planets - present fingings - future projects.
A Harmony Search Algorithm approach for optimizing traffic signal timings
Directory of Open Access Journals (Sweden)
Mauro Dell'Orco
2013-07-01
Full Text Available In this study, a bi-level formulation is presented for solving the Equilibrium Network Design Problem (ENDP. The optimisation of the signal timing has been carried out at the upper-level using the Harmony Search Algorithm (HSA, whilst the traffic assignment has been carried out through the Path Flow Estimator (PFE at the lower level. The results of HSA have been first compared with those obtained using the Genetic Algorithm, and the Hill Climbing on a two-junction network for a fixed set of link flows. Secondly, the HSA with PFE has been applied to the medium-sized network to show the applicability of the proposed algorithm in solving the ENDP. Additionally, in order to test the sensitivity of perceived travel time error, we have used the HSA with PFE with various level of perceived travel time. The results showed that the proposed method is quite simple and efficient in solving the ENDP.
Optimization of reliability allocation strategies through use of genetic algorithms
Energy Technology Data Exchange (ETDEWEB)
Campbell, J.E.; Painton, L.A.
1996-08-01
This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.
Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search
Asmuth, John
2012-01-01
Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act near-optimally in Markov Decision Processes (MDPs) with very large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is equivalent to optimal behavior in the known belief-space MDP, although the size of this belief-space MDP grows exponentially with the amount of history retained, and is potentially infinite. We show how an agent can use one particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an efficient way to act nearly Bayes-optimally for all but a polynomial number of steps, assuming that FSSS can be used to act efficiently in any possible underlying MDP.
Jochmann-Mannak, Hanna Ewoudia
2014-01-01
Children experience all kinds of problems using search interfaces for adults such as Google. The research reported in this dissertation is about the design of informational interfaces for children between 8 and 12 years old. The goal of the research was to learn more about interfaces that ‘work’ for children and interfaces that children ‘like’. The first step in the research was a corpus study to identify design conventions of children’s search interfaces. The design conventions showed that d...
Optimal sampling strategies for detecting zoonotic disease epidemics.
Ferguson, Jake M; Langebrake, Jessica B; Cannataro, Vincent L; Garcia, Andres J; Hamman, Elizabeth A; Martcheva, Maia; Osenberg, Craig W
2014-06-01
The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.
A UAV routing and sensor control optimization algorithm for target search
Collins, Gaemus E.; Riehl, James R.; Vegdahl, Philip S.
2007-04-01
An important problem in unmanned air vehicle (UAV) and UAV-mounted sensor control is the target search problem: locating target(s) in minimum time. Current methods solve the optimization of UAV routing control and sensor management independently. While this decoupled approach makes the target search problem computationally tractable, it is suboptimal. In this paper, we explore the target search and classification problems by formulating and solving a joint UAV routing and sensor control optimization problem. The routing problem is solved on a graph using receding horizon optimal control. The graph is dynamically adjusted based on the target probability distribution function (PDF). The objective function for the routing optimization is the solution of a sensor control optimization problem. An optimal sensor schedule (in the sense of maximizing the viewed target probability mass) is constructed for each candidate flight path in the routing control problem. The PDF of the target state is represented with a particle filter and an "occupancy map" for any undiscovered targets. The tradeoff between searching for undiscovered targets and locating tracks is handled automatically and dynamically by the use of an appropriate objective function. In particular, the objective function is based on the expected amount of target probability mass to be viewed.
Energy Technology Data Exchange (ETDEWEB)
Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S. [Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)
2011-02-15
Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems. (author)
Win the game of Googleopoly unlocking the secret strategy of search engines
Bradley, Sean V
2015-01-01
Rank higher in search results with this guide to SEO and content building supremacy Google is not only the number one search engine in the world, it is also the number one website in the world. Only 5 percent of site visitors search past the first page of Google, so if you're not in those top ten results, you are essentially invisible. Winning the Game of Googleopoly is the ultimate roadmap to Page One Domination. The POD strategy is what gets you on that super-critical first page of Google results by increasing your page views. You'll learn how to shape your online presence for Search Engine
Incorporate Energy Strategy into Particle Swarm Optimizer Algorithm
Institute of Scientific and Technical Information of China (English)
ZHANG Lun; DONG De-cun; LU Yan; CHEN Lan
2008-01-01
The issue of optimizing the dynamic parameters in Particle Swarm Optimizer (PSO) is addressed in this paper.An algorithm is designed which makes all particles originally endowed with a certain level energy, what here we define as EPSO (Energy Strategy PSO).During the iterative process of PSO algorithm, the Inertia Weight is updated according to the calculation of the particle's energy.The portion ratio of the current residual energy to the initial endowed energy is used as the parameter Inertia Weight which aims to update the particles' velocity efficiently.By the simulation in a graph theoritical and a functional optimization problem respectively, it could be easily found that the rate of convergence in EPSO is obviously increased.
Infomax strategies for an optimal balance between exploration and exploitation
Reddy, Gautam; Vergassola, Massimo
2016-01-01
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific applications are successful, the validity of information as a proxy for reward remains unclear. Here, we consider the multi-armed bandit decision problem, which features arms (slot-machines) of unknown probabilities of success and a player trying to maximize cumulative payoff by choosing the sequence of arms to play. We show that an Infomax strategy (Info-p) which optimally gathers information on the highest mean reward among the arms saturates known optimal bounds and compares favorably to existing policies. The highest mean reward considered by Info-p is not the quantity actually needed for the choice of the arm to play, yet it allows for optimal tradeoffs between exploration and exploitation.
Optimal Constrained Resource Allocation Strategies under Low Risk Circumstances
Andreica, Mugurel Ionut; Visan, Costel
2009-01-01
In this paper we consider multiple constrained resource allocation problems, where the constraints can be specified by formulating activity dependency restrictions or by using game-theoretic models. All the problems are focused on generic resources, with a few exceptions which consider financial resources in particular. The problems consider low-risk circumstances and the values of the uncertain variables which are used by the algorithms are the expected values of the variables. For each of the considered problems we propose novel algorithmic solutions for computing optimal resource allocation strategies. The presented solutions are optimal or near-optimal from the perspective of their time complexity. The considered problems have applications in a broad range of domains, like workflow scheduling in industry (e.g. in the mining and metallurgical industry) or the financial sector, motion planning, facility location and data transfer or job scheduling and resource management in Grids, clouds or other distribute...
Developing efficient search strategies to identify reports of adverse effects in MEDLINE and EMBASE.
Golder, Su; McIntosh, Heather M; Duffy, Steve; Glanville, Julie
2006-03-01
This study aimed to assess the performance, in terms of sensitivity and precision, of different approaches to searching MEDLINE and EMBASE to identify studies of adverse effects. Five approaches to searching for adverse effects evidence were identified: approach 1, using specified adverse effects; approach 2, using subheadings/qualifiers; approach 3, using text words; approach 4, using indexing terms; approach 5, searching for specific study designs. The sensitivity and precision of these five approaches, and combinations of these approaches, were compared in a case study using a systematic review of the adverse effects of seven anti-epileptic drugs. The most sensitive search strategy in MEDLINE (97.0%) required a combination of terms for specified adverse effects, floating subheadings, and text words for 'adverse effects'. In EMBASE, a combination of terms for specified adverse effects and text words for 'adverse effects' provided the most sensitive search strategy (98.6%). Both these search strategies yielded low precision (2.8%). A highly sensitive search in either database requires a combination of approaches, and has low precision. This suggests that better reporting and indexing of adverse effects is required and that an effective generic search filter may not yet be feasible.
Search Engine Optimization Techniques Practiced in Organizations: A Study of Four Organizations
Akram, Muhammad; Hayat, Sikandar; Shafi, M Imran; Saeed, Umer
2010-01-01
Web spammers used Search Engine Optimization (SEO) techniques to increase search-ranking of web sites. In this paper we have study the essentials SEO techniques, such as; directory submission, keyword generation and link exchanges. The impact of SEO techniques can be applied as marketing technique and to get top listing in major search engines like Google, Yahoo, and MSN. Our study focuses on these techniques from four different companies' perspectives of United Kingdom and Pakistan. According to the these companies, these techniques are low cost and high impacts in profit, because mostly customers focus on major search engine to find different products on internet, so SEO technique provides best opportunity to grow their business. This paper also describes the pros and cons of using these searh engine optimization techniques in above four companies. We have concluded that these techniques are essential to increase their business profit and minimize their marketing cost.
Optimal information transmission in organizations: search and congestion
Energy Technology Data Exchange (ETDEWEB)
Arenas, A.; Cabrales, A.; Danon, L.; Diaz-Guilera, A.; Guimera, R.; Vega-Redondo, F.
2008-01-01
We propose a stylized model of a problem-solving organization whose internal communication structure is given by a fixed network. Problems arrive randomly anywhere in this network and must find their way to their respective specialized solvers by relying on local information alone. The organization handles multiple problems simultaneously. For this reason, the process may be subject to congestion. We provide a characterization of the threshold of collapse of the network and of the stock of floating problems (or average delay) that prevails below that threshold. We build upon this characterization to address a design problem: the determination of what kind of network architecture optimizes performance for any given problem arrival rate. We conclude that, for low arrival rates, the optimal network is very polarized (i.e. star-like or centralized), whereas it is largely homogeneous (or decentralized) for high arrival rates. These observations are in line with a common transformation experienced by information-intensive organizations as their work flow has risen in recent years.
Optimizing selection of decentralized stormwater management strategies in urbanized regions
Yu, Z.; Montalto, F.
2011-12-01
A variety of decentralized stormwater options are available for implementation in urbanized regions. These strategies, which include bio-retention, porous pavement, green roof etc., vary in terms of cost, ability to reduce runoff, and site applicability. This paper explores the tradeoffs between different types of stormwater control meastures that could be applied in a typical urban study area. A nested optimization strategy first identifies the most cost-effective (e.g. runoff reduction / life cycle cost invested ) options for individual land parcel typologies, and then scales up the results with detailed attention paid to uncertainty in adoption rates, life cycle costs, and hydrologic performance. The study is performed with a custom built stochastic rainfall-runoff model (Monte Carlo techniques are used to quantify uncertainties associated with phased implementation of different strategies and different land parcel typologies under synthetic precipitation ensembles). The results are presented as a comparison of cost-effectiveness over the time span of 30 years, and state an optimized strategy on the cumulative cost-effectiveness over the period.
How Interface Design and Search Strategy Influence Children’s Search Performance and Evaluation
Jochmann-Mannak, Hanna; Lentz, Leo; Huibers, Theo; Sanders, Ted
2014-01-01
This chapter presents an experiment with 158 children, aged 10 to 12, in which search performance and attitudes towards an informational Website are investigated. The same Website was designed in 3 different types of interface design varying in playfulness of navigation structure and in playfulness
Jochmann-Mannak, Hanna Ewoudia
2014-01-01
Children experience all kinds of problems using search interfaces for adults such as Google. The research reported in this dissertation is about the design of informational interfaces for children between 8 and 12 years old. The goal of the research was to learn more about interfaces that ‘work’ for
VI International Workshop on Nature Inspired Cooperative Strategies for Optimization
Otero, Fernando; Masegosa, Antonio
2014-01-01
Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation. This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm In...
Critical Assessment of Search Strategies in Systematic Reviews in Endodontics.
Yaylali, Ibrahim Ethem; Alaçam, Tayfun
2016-06-01
The aim of this study was to perform an overview of literature search strategies in systematic reviews (SRs) published in 2 endodontic journals, Journal of Endodontics and International Endodontic Journal. A search was done by using the MEDLINE (PubMed interface) database to retrieve the articles published between January 1, 2000 and December 31, 2015. The last search was on January 10, 2016. All the SRs published in the 2 journals were retrieved and screened. Eligible SRs were assessed by using 11 questions about search strategies in the SRs that were adapted from 2 guidelines (ie, AMSTAR checklist and the Cochrane Handbook). A total of 83 SRs were retrieved by electronic search. Of these, 55 were from the Journal of Endodontics, and 28 were from the International Endodontic Journal. After screening, 2 SRs were excluded, and 81 SRs were included in the study. Some issues, such as search of grey literature and contact with study authors, were not fully reported (30% and 25%, respectively). On the other hand, some issues, such as the use of index terms and key words and search in at least 2 databases, were reported in most of the SRs (97% and 95%, respectively). The overall quality of the search strategy in both journals was 61%. No significant difference was found between the 2 journals in terms of evaluation criteria (P > .05). There exist areas for improving the quality of reporting of search strategies in SRs; for example, grey literature should be searched for unpublished studies, no language limitation should be applied to databases, and authors should make an attempt to contact the authors of included studies to obtain further relevant information. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.
Research on Search Engine Optimization%搜索引擎优化技术研究
Institute of Scientific and Technical Information of China (English)
殷存举
2014-01-01
Search engine optimization technique is one of the effective methods to enterprise site network promotion,it has been paid attention more and more websites.The paper analysis of the impact of various factors of search engine optimization and puts forward search engine optimization method.%搜索引擎优化技术作为企业网站网络推广的有效方法之一，已被越来越多的网站重视和使用。对制约搜索引擎优化的原因进行了详细探讨，提出了对搜索引擎优化的策略。
Golder, Su; Loke, Yoon K; Zorzela, Liliane
2014-06-01
Research indicates that the methods used to identify data for systematic reviews of adverse effects may need to differ from other systematic reviews. To compare search methods in systematic reviews of adverse effects with other reviews. The search methodologies in 849 systematic reviews of adverse effects were compared with other reviews. Poor reporting of search strategies is apparent in both systematic reviews of adverse effects and other types of systematic reviews. Systematic reviews of adverse effects are less likely to restrict their searches to MEDLINE or include only randomised controlled trials (RCTs). The use of other databases is largely dependent on the topic area and the year the review was conducted, with more databases searched in more recent reviews. Adverse effects search terms are used by 72% of reviews and despite recommendations only two reviews report using floating subheadings. The poor reporting of search strategies in systematic reviews is universal, as is the dominance of searching MEDLINE. However, reviews of adverse effects are more likely to include a range of study designs (not just RCTs) and search beyond MEDLINE. © 2014 Crown Copyright.
ZULAZEZE SAHRI
2016-01-01
Abstract—It is inevitable to accept that today’s business trends focus on selling products online using the latest web application system and website marketing tools. This phenomenon creates a high competition on search engine ranking among website owners in gaining business leads, visitor traffics and acquisitions. This paper proposed a model on improving e-business traffics acquisitions in terms of the number of new visitors (traffics) and returning visitors using Search Engine Optimization...
Ji, H F; Huang, M Y; Xu, S Y; Wang, N; Wang, S
2016-01-01
The Robust Conjugate Direction Search (RCDS) method is used to optimize the collimation system for Rapid Cycling Synchrotron (RCS) of the Chinese Spallation Neutron Source (CSNS). The parameters of secondary collimators are optimized for a better performance of the collimation system. To improve the efficiency of the optimization, the Objective Ring Beam Injection and Tracking (ORBIT) parallel module combined with MATLAB parallel computing is used, which can run multiple ORBIT instances simultaneously. This study presents a way to figure out an optimal parameter combination of the secondary collimators for a machine model in preparation for CSNS/RCS commissioning.
Optimal strategies for electric energy contract decision making
Song, Haili
2000-10-01
The power industry restructuring in various countries in recent years has created an environment where trading of electric energy is conducted in a market environment. In such an environment, electric power companies compete for the market share through spot and bilateral markets. Being profit driven, electric power companies need to make decisions on spot market bidding, contract evaluation, and risk management. New methods and software tools are required to meet these upcoming needs. In this research, bidding strategy and contract pricing are studied from a market participant's viewpoint; new methods are developed to guide a market participant in spot and bilateral market operation. A supplier's spot market bidding decision is studied. Stochastic optimization is formulated to calculate a supplier's optimal bids in a single time period. This decision making problem is also formulated as a Markov Decision Process. All the competitors are represented by their bidding parameters with corresponding probabilities. A systematic method is developed to calculate transition probabilities and rewards. The optimal strategy is calculated to maximize the expected reward over a planning horizon. Besides the spot market, a power producer can also trade in the bilateral markets. Bidding strategies in a bilateral market are studied with game theory techniques. Necessary and sufficient conditions of Nash Equilibrium (NE) bidding strategy are derived based on the generators' cost and the loads' willingness to pay. The study shows that in any NE, market efficiency is achieved. Furthermore, all Nash equilibria are revenue equivalent for the generators. The pricing of "Flexible" contracts, which allow delivery flexibility over a period of time with a fixed total amount of electricity to be delivered, is analyzed based on the no-arbitrage pricing principle. The proposed algorithm calculates the price based on the optimality condition of the stochastic optimization formulation
An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS
Directory of Open Access Journals (Sweden)
Yang Jiao
2017-01-01
Full Text Available To solve problems such as the high cost of microgrids (MGs, balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS and harmony search algorithm (HSA are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA can effectively improve the stability and economy of MG operation with an ESS.
Directory of Open Access Journals (Sweden)
Dr.B.Subramanyam
2013-02-01
Full Text Available In this paper, Particle Swarm optimization(PSO and Artificial Bee Colony (ABC algorithms are used to determine the optimal bidding strategy in competitive auction market implementation. The deregulated power industry meets the challenges of increase their profits and also minimize the associadted risks of the system. Themarket includes generating companies(Gencos and large Consumers. The demand prediction of the system has been determined by the neural network, which is trained by using the previous day demand dataset, the training process is achieved by the back propagation algorithm. The fitness of the system compared with PSO and ABC technique, the maximized fitness is the optimal bidding strategy of the system . The results for two techniques will be analyzed in this paper. The implementation of the two techniques could be implemented in theMATLAB Platform.
Selva, Anna; Solà, Ivan; Zhang, Yuan; Pardo-Hernandez, Hector; Haynes, R Brian; Martínez García, Laura; Navarro, Tamara; Schünemann, Holger; Alonso-Coello, Pablo
2017-08-30
Identifying scientific literature addressing patients' views and preferences is complex due to the wide range of studies that can be informative and the poor indexing of this evidence. Given the lack of guidance we developed a search strategy to retrieve this type of evidence. We assembled an initial list of terms from several sources, including the revision of the terms and indexing of topic-related studies and, methods research literature, and other relevant projects and systematic reviews. We used the relative recall approach, evaluating the capacity of the designed search strategy for retrieving studies included in relevant systematic reviews for the topic. We implemented in practice the final version of the search strategy for conducting systematic reviews and guidelines, and calculated search's precision and the number of references needed to read (NNR). We assembled an initial version of the search strategy, which had a relative recall of 87.4% (yield of 132/out of 151 studies). We then added some additional terms from the studies not initially identified, and re-tested this improved version against the studies included in a new set of systematic reviews, reaching a relative recall of 85.8% (151/out of 176 studies, 95% CI 79.9 to 90.2). This final version of the strategy includes two sets of terms related with two domains: "Patient Preferences and Decision Making" and "Health State Utilities Values". When we used the search strategy for the development of systematic reviews and clinical guidelines we obtained low precision values (ranging from 2% to 5%), and the NNR from 20 to 50. This search strategy fills an important research gap in this field. It will help systematic reviewers, clinical guideline developers, and policy-makers to retrieve published research on patients' views and preferences. In turn, this will facilitate the inclusion of this critical aspect when formulating heath care decisions, including recommendations.
Is Memory Search Governed by Universal Principles or Idiosyncratic Strategies?
Healey, M. Karl; Kahana, Michael J.
2013-01-01
Laboratory paradigms have provided an empirical foundation for much of psychological science. Some have argued, however, that such paradigms are highly susceptible to idiosyncratic strategies and that rather than reflecting fundamental cognitive principles, many findings are artifacts of averaging across participants who employ different strategies. We develop a set of techniques to rigorously test the extent to which average data are distorted by such strategy differences and apply these techniques to free recall data from the Penn Electrophysiology of Encoding and Retrieval Study (PEERS). Recall initiation showed evidence of subgroups: the majority of participants initiate recall from the last item in the list, but one subgroup show elevated initiation probabilities for items 2–4 back from the end of the list and another showed elevated probabilities for the beginning of the list. By contrast, serial position curves and temporal and semantic clustering functions were remarkably consistent, with almost every participant exhibiting a recognizable version of the average function, suggesting that these functions reflect fundamental principles of the memory system. The approach taken here can serve as a model for evaluating the extent to which other laboratory paradigms are influenced by individual differences in strategy use. PMID:23957279
PcapDB: Search Optimized Packet Capture, Version 0.1.0.0
Energy Technology Data Exchange (ETDEWEB)
2016-11-04
PcapDB is a packet capture system designed to optimize the captured data for fast search in the typical (network incident response) use case. The technology involved in this software has been submitted via the IDEAS system and has been filed as a provisional patent. It includes the following primary components: capture: The capture component utilizes existing capture libraries to retrieve packets from network interfaces. Once retrieved the packets are passed to additional threads for sorting into flows and indexing. The sorted flows and indexes are passed to other threads so that they can be written to disk. These components are written in the C programming language. search: The search components provide a means to find relevant flows and the associated packets. A search query is parsed and represented as a search tree. Various search commands, written in C, are then used resolve this tree into a set of search results. The tree generation and search execution management components are written in python. interface: The PcapDB web interface is written in Python on the Django framework. It provides a series of pages, API's, and asynchronous tasks that allow the user to manage the capture system, perform searches, and retrieve results. Web page components are written in HTML,CSS and Javascript.
A two-phase tabu search approach to scheduling optimization in container terminals
Institute of Scientific and Technical Information of China (English)
ZENG Qing-cheng; YANG Zhong-zhen
2007-01-01
An optimization model for scheduling of quay cranes (QCs) and yard trailers was proposed to improve the overall efficiency of container terminals. To implement this model, a two-phase tabu search algorithra was designed. In the QCs scheduling phase of the algorithm, a search was performed to determine a good QC unloading operation order. For each QC unloading operation order generated during the QC's scheduling phase, another search was run to obtain a good yard trailer routing for the given QC's unloading order. Using this information, the time required for the operation was estimated,then the time of return to availability of the units was fed back to the QC scheduler. Numerical tests show that the two-phase Tabu Search algorithm searches the solution space efficiently, decreases the empty distance yard trailers must travel, decreases the number of trailers needed, and thereby reduces time and costs and improves the integration and reliability of container terminal operation systems.
Optimizing Online Suicide Prevention: A Search Engine-Based Tailored Approach.
Arendt, Florian; Scherr, Sebastian
2016-10-14
Search engines are increasingly used to seek suicide-related information online, which can serve both harmful and helpful purposes. Google acknowledges this fact and presents a suicide-prevention result for particular search terms. Unfortunately, the result is only presented to a limited number of visitors. Hence, Google is missing the opportunity to provide help to vulnerable people. We propose a two-step approach to a tailored optimization: First, research will identify the risk factors. Second, search engines will reweight algorithms according to the risk factors. In this study, we show that the query share of the search term "poisoning" on Google shows substantial peaks corresponding to peaks in actual suicidal behavior. Accordingly, thresholds for showing the suicide-prevention result should be set to the lowest levels during the spring, on Sundays and Mondays, on New Year's Day, and on Saturdays following Thanksgiving. Search engines can help to save lives globally by utilizing a more tailored approach to suicide prevention.
Directory of Open Access Journals (Sweden)
Prof. Nar Singh
2016-05-01
Full Text Available In area of video compression, Motion Estimation is one of the most important modules and play an important role to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to searching of a candidate block in the search window of the reference frame. Various block matching methods have been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less expensive block matching methods, which is widely acceptable for better Motion Estimation in video data processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search method for improvement in motion estimation.
The optimal time-frequency atom search based on a modified ant colony algorithm
Institute of Scientific and Technical Information of China (English)
GUO Jun-feng; LI Yan-jun; YU Rui-xing; ZHANG Ke
2008-01-01
In this paper,a new optimal time-frequency atom search method based on a modified ant colony algorithm is proposed to improve the precision of the traditional methods.First,the discretization formula of finite length time-frequency atom is inferred at length.Second; a modified ant colony algorithm in continuous space is proposed.Finally,the optimal timefrequency atom search algorithm based on the modified ant colony algorithm is described in detail and the simulation experiment is carried on.The result indicates that the developed algorithm is valid and stable,and the precision of the method is higher than that of the traditional method.
Protopopescu, V; Barhen, J
2003-01-01
A constant-time solution of the continuous global optimization problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Brueschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed. (letter to the editor)
Search Engine Optimization and Its Importance for Business Visibility and Branding
Vo, Tuan
2016-01-01
In the era of Information age, it is common for a business to have an online presence on the Internet. However, presence is not enough, the business has to be clearly visible on the Internet whenever people search for the product, service or resource provided by that business in order to survive and thrive in an increasingly competitive market. As a result, search engine marketing (SEM) in general and search engine optimization (SEO) in particular is an essential tool that can be applied to d...
An evolutionary strategy based on partial imitation for solving optimization problems
Javarone, Marco Alberto
2016-12-01
In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem whose search space grows exponentially, increasing the number of cities, up to becoming NP-hard. The solutions of the TSP can be codified by arrays of cities, and can be evaluated by fitness, computed according to a cost function (e.g. the length of a path). Our method is based on the evolution of an agent population by means of an imitative mechanism, we define 'partial imitation'. In particular, agents receive a random solution and then, interacting among themselves, may imitate the solutions of agents with a higher fitness. Since the imitation mechanism is only partial, agents copy only one entry (randomly chosen) of another array (i.e. solution). In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. driven towards an ordered phase. We highlight that the adopted 'partial imitation' mechanism allows the population to generate solutions over time, before reaching the final equilibrium. Results of numerical simulations show that our method is able to find, in a finite time, both optimal and suboptimal solutions, depending on the size of the considered search space.
Directory of Open Access Journals (Sweden)
Weitian Lin
2014-01-01
Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.
New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization
Directory of Open Access Journals (Sweden)
Alejandro MURRIETA-MENDOZA
2016-06-01
Full Text Available Burning the fuel required to sustain a given flight releases pollution such as carbon dioxide and nitrogen oxides, and the amount of fuel consumed is also a significant expense for airlines. It is desirable to reduce fuel consumption to reduce both pollution and flight costs. To increase fuel savings in a given flight, one option is to compute the most economical vertical reference trajectory (or flight plan. A deterministic algorithm was developed using a numerical aircraft performance model to determine the most economical vertical flight profile considering take-off weight, flight distance, step climb and weather conditions. This algorithm is based on linear interpolations of the performance model using the Lagrange interpolation method. The algorithm downloads the latest available forecast from Environment Canada according to the departure date and flight coordinates, and calculates the optimal trajectory taking into account the effects of wind and temperature. Techniques to avoid unnecessary calculations are implemented to reduce the computation time. The costs of the reference trajectories proposed by the algorithm are compared with the costs of the reference trajectories proposed by a commercial flight management system using the fuel consumption estimated by the FlightSim® simulator made by Presagis®.
Energy Optimal Control Strategy of PHEV Based on PMP Algorithm
Directory of Open Access Journals (Sweden)
Tiezhou Wu
2017-01-01
Full Text Available Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV, from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC, which is hybrid energy storage system (Li-SC HESS, working together with internal combustion engine (ICE to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.
Cost Effectiveness Analysis of Optimal Malaria Control Strategies in Kenya
Directory of Open Access Journals (Sweden)
Gabriel Otieno
2016-03-01
Full Text Available Malaria remains a leading cause of mortality and morbidity among the children under five and pregnant women in sub-Saharan Africa, but it is preventable and controllable provided current recommended interventions are properly implemented. Better utilization of malaria intervention strategies will ensure the gain for the value for money and producing health improvements in the most cost effective way. The purpose of the value for money drive is to develop a better understanding (and better articulation of costs and results so that more informed, evidence-based choices could be made. Cost effectiveness analysis is carried out to inform decision makers on how to determine where to allocate resources for malaria interventions. This study carries out cost effective analysis of one or all possible combinations of the optimal malaria control strategies (Insecticide Treated Bednets—ITNs, Treatment, Indoor Residual Spray—IRS and Intermittent Preventive Treatment for Pregnant Women—IPTp for the four different transmission settings in order to assess the extent to which the intervention strategies are beneficial and cost effective. For the four different transmission settings in Kenya the optimal solution for the 15 strategies and their associated effectiveness are computed. Cost-effective analysis using Incremental Cost Effectiveness Ratio (ICER was done after ranking the strategies in order of the increasing effectiveness (total infections averted. The findings shows that for the endemic regions the combination of ITNs, IRS, and IPTp was the most cost-effective of all the combined strategies developed in this study for malaria disease control and prevention; for the epidemic prone areas is the combination of the treatment and IRS; for seasonal areas is the use of ITNs plus treatment; and for the low risk areas is the use of treatment only. Malaria transmission in Kenya can be minimized through tailor-made intervention strategies for malaria control
Hemoglobin optimization and transfusion strategies in patients undergoing cardiac surgery
Institute of Scientific and Technical Information of China (English)
Mahdi; Najafi; David; Faraoni
2015-01-01
Although red blood cells(RBCs) transfusion is sometimes associated with adverse reactions,anemia could also lead to increased morbidity and mortality in highrisk patients. For these reasons,the definition of perioperative strategies that aims to detect and treat preoperative anemia,prevent excessive blood loss,and define "optimal" transfusion algorithms is crucial. Although the treatment with preoperative iron and erythropoietin has been recommended in some specific conditions,several controversies exist regarding the benefit-to-risk balance associated with these treatments. Further studies are needed to better define the indications,dosage,and route of administration for preoperative iron with or without erythropoietin supplementation. Although restrictive transfusion strategies in patients undergoing cardiac surgery have been shown to effectively reduce the incidence and the amount of RBCs transfusion without increase in side effects,some high-risk patients(e.g.,symptomatic acute coronary syndrome) could benefit from higher hemoglobin concentrations. Despite all efforts made last decade,a significant amount of work remains to be done to improve hemoglobin optimization and transfusion strategies in patients undergoing cardiac surgery.