Meta-heuristic cuckoo search algorithm for the correction of faulty array antenna

International Nuclear Information System (INIS)

Khan, S.U.; Qureshi, I.M.

2015-01-01

In this article, we introduce a CSA (Cuckoo Search Algorithm) for compensation of faulty array antenna. It is assumed that the faulty elemental location is also known. When the sensor fails, it disturbs the power pattern, owing to which its SLL (Sidelobe Level) raises and nulls are shifted from their required positions. In this approach, the CSA optimizes the weights of the active elements for the reduction of SLL and null position in the desired direction. The meta-heuristic CSA is used for the control of SLL and steering of nulls at their required positions. The CSA is based on the necessitated kids bloodsucking behavior of cuckoo sort in arrangement with the Levy flight manners. The fitness function is used to reduce the error between the preferred and probable pattern along with null constraints. Imitational consequences for various scenarios are given to exhibit the validity and presentation of the proposed method. (author)

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

Science.gov (United States)

Nouri, Houssem Eddine; Belkahla Driss, Olfa; Ghédira, Khaled

2018-03-01

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

A well-scalable metaheuristic for the fleet size and mix vehicle routing problem with time windows

NARCIS (Netherlands)

Bräysy, Olli; Porkka, Pasi P.; Dullaert, Wout; Repoussis, Panagiotis P.; Tarantilis, Christos D.

This paper presents an efficient and well-scalable metaheuristic for fleet size and mix vehicle routing with time windows. The suggested solution method combines the strengths of well-known threshold accepting and guided local search metaheuristics to guide a set of four local search heuristics. The

Hybridisations of Variable Neighbourhood Search and Modified Simplex Elements to Harmony Search and Shuffled Frog Leaping Algorithms for Process Optimisations

Science.gov (United States)

Aungkulanon, P.; Luangpaiboon, P.

2010-10-01

Nowadays, the engineering problem systems are large and complicated. An effective finite sequence of instructions for solving these problems can be categorised into optimisation and meta-heuristic algorithms. Though the best decision variable levels from some sets of available alternatives cannot be done, meta-heuristics is an alternative for experience-based techniques that rapidly help in problem solving, learning and discovery in the hope of obtaining a more efficient or more robust procedure. All meta-heuristics provide auxiliary procedures in terms of their own tooled box functions. It has been shown that the effectiveness of all meta-heuristics depends almost exclusively on these auxiliary functions. In fact, the auxiliary procedure from one can be implemented into other meta-heuristics. Well-known meta-heuristics of harmony search (HSA) and shuffled frog-leaping algorithms (SFLA) are compared with their hybridisations. HSA is used to produce a near optimal solution under a consideration of the perfect state of harmony of the improvisation process of musicians. A meta-heuristic of the SFLA, based on a population, is a cooperative search metaphor inspired by natural memetics. It includes elements of local search and global information exchange. This study presents solution procedures via constrained and unconstrained problems with different natures of single and multi peak surfaces including a curved ridge surface. Both meta-heuristics are modified via variable neighbourhood search method (VNSM) philosophy including a modified simplex method (MSM). The basic idea is the change of neighbourhoods during searching for a better solution. The hybridisations proceed by a descent method to a local minimum exploring then, systematically or at random, increasingly distant neighbourhoods of this local solution. The results show that the variant of HSA with VNSM and MSM seems to be better in terms of the mean and variance of design points and yields.

Comparison of metaheuristic techniques to determine optimal placement of biomass power plants

International Nuclear Information System (INIS)

Reche-Lopez, P.; Ruiz-Reyes, N.; Garcia Galan, S.; Jurado, F.

2009-01-01

This paper deals with the application and comparison of several metaheuristic techniques to optimize the placement and supply area of biomass-fueled power plants. Both, trajectory and population-based methods are applied for our goal. In particular, two well-known trajectory method, such as Simulated Annealing (SA) and Tabu Search (TS), and two commonly used population-based methods, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are hereby considered. In addition, a new binary PSO algorithm has been proposed, which incorporates an inertia weight factor, like the classical continuous approach. The fitness function for the metaheuristics is the profitability index, defined as the ratio between the net present value and the initial investment. In this work, forest residues are considered as biomass source, and the problem constraints are: the generation system must be located inside the supply area, and its maximum electric power is 5 MW. The comparative results obtained by all considered metaheuristics are discussed. Random walk has also been assessed for the problem we deal with.

Comparison of metaheuristic techniques to determine optimal placement of biomass power plants

Energy Technology Data Exchange (ETDEWEB)

Reche-Lopez, P.; Ruiz-Reyes, N.; Garcia Galan, S. [Telecommunication Engineering Department, University of Jaen Polytechnic School, C/ Alfonso X el Sabio 28, 23700 Linares, Jaen (Spain); Jurado, F. [Electrical Engineering Department, University of Jaen Polytechnic School, C/ Alfonso X el Sabio 28, 23700 Linares, Jaen (Spain)

2009-08-15

This paper deals with the application and comparison of several metaheuristic techniques to optimize the placement and supply area of biomass-fueled power plants. Both, trajectory and population-based methods are applied for our goal. In particular, two well-known trajectory method, such as Simulated Annealing (SA) and Tabu Search (TS), and two commonly used population-based methods, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are hereby considered. In addition, a new binary PSO algorithm has been proposed, which incorporates an inertia weight factor, like the classical continuous approach. The fitness function for the metaheuristics is the profitability index, defined as the ratio between the net present value and the initial investment. In this work, forest residues are considered as biomass source, and the problem constraints are: the generation system must be located inside the supply area, and its maximum electric power is 5 MW. The comparative results obtained by all considered metaheuristics are discussed. Random walk has also been assessed for the problem we deal with. (author)

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

Directory of Open Access Journals (Sweden)

Tashkova Katerina

2011-10-01

Full Text Available Abstract Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA, particle-swarm optimization (PSO, and differential evolution (DE, as well as a local-search derivative-based algorithm 717 (A717 to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE clearly and significantly outperform the local derivative-based method (A717. Among the three meta-heuristics, differential evolution (DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

Science.gov (United States)

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and

Data classification using metaheuristic Cuckoo Search technique for Levenberg Marquardt back propagation (CSLM) algorithm

Science.gov (United States)

Nawi, Nazri Mohd.; Khan, Abdullah; Rehman, M. Z.

2015-05-01

A nature inspired behavior metaheuristic techniques which provide derivative-free solutions to solve complex problems. One of the latest additions to the group of nature inspired optimization procedure is Cuckoo Search (CS) algorithm. Artificial Neural Network (ANN) training is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms have some limitation such as getting trapped in local minima and slow convergence rate. This study proposed a new technique CSLM by combining the best features of two known algorithms back-propagation (BP) and Levenberg Marquardt algorithm (LM) for improving the convergence speed of ANN training and avoiding local minima problem by training this network. Some selected benchmark classification datasets are used for simulation. The experiment result show that the proposed cuckoo search with Levenberg Marquardt algorithm has better performance than other algorithm used in this study.

Multi-objective optimization in computer networks using metaheuristics

CERN Document Server

Donoso, Yezid

2007-01-01

Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

A Novel Quad Harmony Search Algorithm for Grid-Based Path Finding

Directory of Open Access Journals (Sweden)

Saso Koceski

2014-09-01

Full Text Available A novel approach to the problem of grid-based path finding has been introduced. The method is a block-based search algorithm, founded on the bases of two algorithms, namely the quad-tree algorithm, which offered a great opportunity for decreasing the time needed to compute the solution, and the harmony search (HS algorithm, a meta-heuristic algorithm used to obtain the optimal solution. This quad HS algorithm uses the quad-tree decomposition of free space in the grid to mark the free areas and treat them as a single node, which greatly improves the execution. The results of the quad HS algorithm have been compared to other meta-heuristic algorithms, i.e., ant colony, genetic algorithm, particle swarm optimization and simulated annealing, and it was proved to obtain the best results in terms of time and giving the optimal path.

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

Directory of Open Access Journals (Sweden)

Nazia Anwar

2018-03-01

Full Text Available Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO. The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT, in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs. The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.

Integer programming formulation and variable neighborhood search metaheuristic for the multiproduct pipeline scheduling problem

Energy Technology Data Exchange (ETDEWEB)

Souza Filho, Erito M.; Bahiense, Laura; Ferreira Filho, Virgilio J.M. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE); Lima, Leonardo [Centro Federal de Educacao Tecnologica Celso Sukow da Fonseca (CEFET-RJ), Rio de Janeiro, RJ (Brazil)

2008-07-01

Pipeline are known as the most reliable and economical mode of transportation for petroleum and its derivatives, especially when large amounts of products have to be pumped for large distances. In this work we address the short-term schedule of a pipeline system comprising the distribution of several petroleum derivatives from a single oil refinery to several depots, connected to local consumer markets, through a single multi-product pipeline. We propose an integer linear programming formulation and a variable neighborhood search meta-heuristic in order to compare the performances of the exact and heuristic approaches to the problem. Computational tests in C language and MOSEL/XPRESS-MP language are performed over a real Brazilian pipeline system. (author)

Metaheuristics for Engineering and Architectural Design of Hospitals

DEFF Research Database (Denmark)

Holst, Malene Kirstine Østergaard; Kirkegaard, Poul Henning

2014-01-01

This paper presents an approach for optimized hospital layout design based on metaheuristics. Through the use of metaheuristics the hospital functionalities are decomposed into geometric units. The units define the baseline for the design of the hospital, as the units are based on correlations of...

A comparative analysis of meta-heuristic methods for power management of a dual energy storage system for electric vehicles

International Nuclear Information System (INIS)

Trovão, João P.; Antunes, Carlos Henggeler

2015-01-01

Highlights: • Two meta-heuristic approaches are evaluated for multi-ESS management in electric vehicles. • An online global energy management strategy with two different layers is studied. • Meta-heuristic techniques are used to define optimized energy sharing mechanisms. • A comparative analysis for ARTEMIS driving cycle is addressed. • The effectiveness of the double-layer management with meta-heuristic is presented. - Abstract: This work is focused on the performance evaluation of two meta-heuristic approaches, simulated annealing and particle swarm optimization, to deal with power management of a dual energy storage system for electric vehicles. The proposed strategy is based on a global energy management system with two layers: long-term (energy) and short-term (power) management. A rule-based system deals with the long-term (strategic) layer and for the short-term (action) layer meta-heuristic techniques are developed to define optimized online energy sharing mechanisms. Simulations have been made for several driving cycles to validate the proposed strategy. A comparative analysis for ARTEMIS driving cycle is presented evaluating three performance indicators (computation time, final value of battery state of charge, and minimum value of supercapacitors state of charge) as a function of input parameters. The results show the effectiveness of an implementation based on a double-layer management system using meta-heuristic methods for online power management supported by a rule set that restricts the search space

Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm

Science.gov (United States)

Ayvaz, M. Tamer

2007-11-01

This study proposes an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads. In the zone structure identification problem fuzzy c-means ( FCM) clustering method is used. The association of the zone structure with the transmissivity distribution is accomplished through an optimization model. The meta-heuristic harmony search ( HS) algorithm, which is conceptualized using the musical process of searching for a perfect state of harmony, is used as an optimization technique. The optimum parameter zone structure is identified based on three criteria which are the residual error, parameter uncertainty, and structure discrimination. A numerical example given in the literature is solved to demonstrate the performance of the proposed algorithm. Also, a sensitivity analysis is performed to test the performance of the HS algorithm for different sets of solution parameters. Results indicate that the proposed solution algorithm is an effective way in the simultaneous identification of aquifer parameters and their corresponding zone structures.

Metaheuristics for bi-level optimization

CERN Document Server

2013-01-01

This book provides a complete background on metaheuristics to solve complex bi-level optimization problems (continuous/discrete, mono-objective/multi-objective) in a diverse range of application domains. Readers learn to solve large scale bi-level optimization problems by efficiently combining metaheuristics with complementary metaheuristics and mathematical programming approaches. Numerous real-world examples of problems demonstrate how metaheuristics are applied in such fields as networks, logistics and transportation, engineering design, finance and security.

Metaheuristic approaches to order sequencing on a unidirectional picking line

Directory of Open Access Journals (Sweden)

AP de Villiers

2013-06-01

Full Text Available In this paper the sequencing of orders on a unidirectional picking line is considered. The aim of the order sequencing is to minimise the number of cycles travelled by a picker within the picking line to complete all orders. A tabu search, simulated annealing, genetic algorithm, generalised extremal optimisation and a random local search are presented as possible solution approaches. Computational results based on real life data instances are presented for these metaheuristics and compared to the performance of a lower bound and the solutions used in practise. The random local search exhibits the best overall solution quality, however, the generalised extremal optimisation approach delivers comparable results in considerably shorter computational times.

The use of meta-heuristics for airport gate assignment

DEFF Research Database (Denmark)

Cheng, Chun-Hung; Ho, Sin C.; Kwan, Cheuk-Lam

2012-01-01

proposed to generate good solutions within a reasonable timeframe. In this work, we attempt to assess the performance of three meta-heuristics, namely, genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and a hybrid approach based on SA and TS. Flight data from Incheon International Airport...... are collected to carry out the computational comparison. Although the literature has documented these algorithms, this work may be a first attempt to evaluate their performance using a set of realistic flight data....

Handbook of metaheuristics

CERN Document Server

Kochenberger, Gary

2003-01-01

Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. Over time, these methods have also come to include any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. The degree to which neighborhoods are exploited varies according to the type of procedure. In the case of certain population-based procedures, such as genetic al- rithms, neighborhoods are implicitly (and somewhat restrictively) defined by reference to replacing components of one solution with those of another, by variously chosen rules of exchange p...

A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems

Directory of Open Access Journals (Sweden)

Angel A. Juan

2015-12-01

Full Text Available Many combinatorial optimization problems (COPs encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.

Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms

Directory of Open Access Journals (Sweden)

Eduardo Batista de Moraes Barbosa

2017-01-01

Full Text Available Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA, which explores a search space of parameters looking for candidate configurations close to a promising alternative. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA and Genetic Algorithm (GA, in order to solve the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach.

A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.

Science.gov (United States)

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Handbook of metaheuristics

CERN Document Server

Potvin, Jean-Yves

2010-01-01

“… an excellent book if you want to learn about a number of individual metaheuristics." (U. Aickelin, Journal of the Operational Research Society, Issue 56, 2005, on the First Edition) The first edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous developments observed in the field of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. When Glover and Kochenberger were unable to prepare this second edition, they suggested that Michel Gendreau and Jean-Yves Potvin should take over the editorship, and so this important new edition is now available. Through its 21 chapters, this second edition is designed to provide a broad coverage of the concepts, implementations and applications in this important field of optimization. Original contributors either revised or updated their work, or provided entirely new chapters. The Handbook now includes updated chapters on the b...

A class-based search for the in-core fuel management optimization of a pressurized water reactor

International Nuclear Information System (INIS)

Alvarenga de Moura Meneses, Anderson; Rancoita, Paola; Schirru, Roberto; Gambardella, Luca Maria

2010-01-01

The In-Core Fuel Management Optimization (ICFMO) is a prominent problem in nuclear engineering, with high complexity and studied for more than 40 years. Besides manual optimization and knowledge-based methods, optimization metaheuristics such as Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization have yielded outstanding results for the ICFMO. In the present article, the Class-Based Search (CBS) is presented for application to the ICFMO. It is a novel metaheuristic approach that performs the search based on the main nuclear characteristics of the fuel assemblies, such as reactivity. The CBS is then compared to the one of the state-of-art algorithms applied to the ICFMO, the Particle Swarm Optimization. Experiments were performed for the optimization of Angra 1 Nuclear Power Plant, located at the Southeast of Brazil. The CBS presented noticeable performance, providing Loading Patterns that yield a higher average of Effective Full Power Days in the simulation of Angra 1 NPP operation, according to our methodology.

A class-based search for the in-core fuel management optimization of a pressurized water reactor

Energy Technology Data Exchange (ETDEWEB)

Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@lmp.ufrj.b [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); Rancoita, Paola [IDSIA (Dalle Molle Institute for Artificial Intelligence), Galleria 2, 6982 Manno-Lugano, TI (Switzerland); Mathematics Department, Universita degli Studi di Milano (Italy); Schirru, Roberto [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); Gambardella, Luca Maria [IDSIA (Dalle Molle Institute for Artificial Intelligence), Galleria 2, 6982 Manno-Lugano, TI (Switzerland)

2010-11-15

The In-Core Fuel Management Optimization (ICFMO) is a prominent problem in nuclear engineering, with high complexity and studied for more than 40 years. Besides manual optimization and knowledge-based methods, optimization metaheuristics such as Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization have yielded outstanding results for the ICFMO. In the present article, the Class-Based Search (CBS) is presented for application to the ICFMO. It is a novel metaheuristic approach that performs the search based on the main nuclear characteristics of the fuel assemblies, such as reactivity. The CBS is then compared to the one of the state-of-art algorithms applied to the ICFMO, the Particle Swarm Optimization. Experiments were performed for the optimization of Angra 1 Nuclear Power Plant, located at the Southeast of Brazil. The CBS presented noticeable performance, providing Loading Patterns that yield a higher average of Effective Full Power Days in the simulation of Angra 1 NPP operation, according to our methodology.

Metaheuristics-Assisted Combinatorial Screening of Eu2+-Doped Ca-Sr-Ba-Li-Mg-Al-Si-Ge-N Compositional Space in Search of a Narrow-Band Green Emitting Phosphor and Density Functional Theory Calculations.

Science.gov (United States)

Lee, Jin-Woong; Singh, Satendra Pal; Kim, Minseuk; Hong, Sung Un; Park, Woon Bae; Sohn, Kee-Sun

2017-08-21

A metaheuristics-based design would be of great help in relieving the enormous experimental burdens faced during the combinatorial screening of a huge, multidimensional search space, while providing the same effect as total enumeration. In order to tackle the high-throughput powder processing complications and to secure practical phosphors, metaheuristics, an elitism-reinforced nondominated sorting genetic algorithm (NSGA-II), was employed in this study. The NSGA-II iteration targeted two objective functions. The first was to search for a higher emission efficacy. The second was to search for narrow-band green color emissions. The NSGA-II iteration finally converged on BaLi 2 Al 2 Si 2 N 6 :Eu 2+ phosphors in the Eu 2+ -doped Ca-Sr-Ba-Li-Mg-Al-Si-Ge-N compositional search space. The BaLi 2 Al 2 Si 2 N 6 :Eu 2+ phosphor, which was synthesized with no human intervention via the assistance of NSGA-II, was a clear single phase and gave an acceptable luminescence. The BaLi 2 Al 2 Si 2 N 6 :Eu 2+ phosphor as well as all other phosphors that appeared during the NSGA-II iterations were examined in detail by employing powder X-ray diffraction-based Rietveld refinement, X-ray absorption near edge structure, density functional theory calculation, and time-resolved photoluminescence. The thermodynamic stability and the band structure plausibility were confirmed, and more importantly a novel approach to the energy transfer analysis was also introduced for BaLi 2 Al 2 Si 2 N 6 :Eu 2+ phosphors.

Metaheuristics for medicine and biology

CERN Document Server

Talbi, El-Ghazali

2017-01-01

This book highlights recent research on metaheuristics for biomedical engineering, addressing both theoretical and applications aspects. Given the multidisciplinary nature of bio-medical image analysis, it has now become one of the most central topics in computer science, computer engineering and electrical and electronic engineering, and attracted the interest of many researchers. To deal with these problems, many traditional and recent methods, algorithms and techniques have been proposed. Among them, metaheuristics is the most common choice. This book provides essential content for senior and young researchers interested in methodologies for implementing metaheuristics to help solve biomedical engineering problems.

A metaheuristic for a numerical approximation to the mass transfer problem

Directory of Open Access Journals (Sweden)

Avendaño-Garrido Martha L.

2016-12-01

Full Text Available This work presents an improvement of the approximation scheme for the Monge-Kantorovich (MK mass transfer problem on compact spaces, which is studied by Gabriel et al. (2010, whose scheme discretizes the MK problem, reduced to solve a sequence of finite transport problems. The improvement presented in this work uses a metaheuristic algorithm inspired by scatter search in order to reduce the dimensionality of each transport problem. The new scheme solves a sequence of linear programming problems similar to the transport ones but with a lower dimension. The proposed metaheuristic is supported by a convergence theorem. Finally, examples with an exact solution are used to illustrate the performance of our proposal.

Advanced metaheuristic algorithms for laser optimization

International Nuclear Information System (INIS)

Tomizawa, H.

2010-01-01

A laser is one of the most important experimental tools. In synchrotron radiation field, lasers are widely used for experiments with Pump-Probe techniques. Especially for Xray-FELs, a laser has important roles as a seed light source or photo-cathode-illuminating light source to generate a high brightness electron bunch. The controls of laser pulse characteristics are required for many kinds of experiments. However, the laser should be tuned and customized for each requirement by laser experts. The automatic tuning of laser is required to realize with some sophisticated algorithms. The metaheuristic algorithm is one of the useful candidates to find one of the best solutions as acceptable as possible. The metaheuristic laser tuning system is expected to save our human resources and time for the laser preparations. I have shown successful results on a metaheuristic algorithm based on a genetic algorithm to optimize spatial (transverse) laser profiles and a hill climbing method extended with a fuzzy set theory to choose one of the best laser alignments automatically for each experimental requirement. (author)

METAHEURISTICS EVALUATION: A PROPOSAL FOR A MULTICRITERIA METHODOLOGY

Directory of Open Access Journals (Sweden)

Valdir Agustinho de Melo

2015-12-01

Full Text Available ABSTRACT In this work we propose a multicriteria evaluation scheme for heuristic algorithms based on the classic Condorcet ranking technique. Weights are associated to the ranking of an algorithm among a set being object of comparison. We used five criteria and a function on the set of natural numbers to create a ranking. The discussed comparison involves three well-known problems of combinatorial optimization - Traveling Salesperson Problem (TSP, Capacitated Vehicle Routing Problem (CVRP and Quadratic Assignment Problem (QAP. The tested instances came from public libraries. Each algorithm was used with essentially the same structure, the same local search was applied and the initial solutions were similarly built. It is important to note that the work does not make proposals involving algorithms: the results for the three problems are shown only to illustrate the operation of the evaluation technique. Four metaheuristics - GRASP, Tabu Search, ILS and VNS - are therefore only used for the comparisons.

A hybrid approach for efficient anomaly detection using metaheuristic methods

Directory of Open Access Journals (Sweden)

Tamer F. Ghanem

2015-07-01

Full Text Available Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

Metaheuristics in water, geotechnical and transport engineering

CERN Document Server

Yang, Xin-She; Talatahari, Siamak; Alavi, Amir Hossein

2013-01-01

Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines the la

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

Science.gov (United States)

Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan

2015-03-01

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.

Meta-Heuristics for Dynamic Lot Sizing: a review and comparison of solution approaches

NARCIS (Netherlands)

R.F. Jans (Raf); Z. Degraeve (Zeger)

2004-01-01

textabstractProofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search,

A metaheuristic optimization framework for informative gene selection

Directory of Open Access Journals (Sweden)

Kaberi Das

Full Text Available This paper presents a metaheuristic framework using Harmony Search (HS with Genetic Algorithm (GA for gene selection. The internal architecture of the proposed model broadly works in two phases, in the first phase, the model allows the hybridization of HS with GA to compute and evaluate the fitness of the randomly selected solutions of binary strings and then HS ranks the solutions in descending order of their fitness. In the second phase, the offsprings are generated using crossover and mutation operations of GA and finally, those offsprings were selected for the next generation whose fitness value is more than their parents evaluated by SVM classifier. The accuracy of the final gene subsets obtained from this model has been evaluated using SVM classifiers. The merit of this approach is analyzed by experimental results on five benchmark datasets and the results showed an impressive accuracy over existing feature selection approaches. The occurrence of gene subsets selected from this model have also been computed and the most often selected gene subsets with the probability of [0.1–0.9] have been chosen as optimal sets of informative genes. Finally, the performance of those selected informative gene subsets have been measured and established through probabilistic measures. Keywords: Gene Selection, Metaheuristic, Harmony Search Algorithm, Genetic Algorithm, SVM

Metaheuristics progress as real problem solvers

CERN Document Server

Nonobe, Koji; Yagiura, Mutsunori

2005-01-01

Metaheuristics: Progress as Real Problem Solvers is a peer-reviewed volume of eighteen current, cutting-edge papers by leading researchers in the field. Included are an invited paper by F. Glover and G. Kochenberger, which discusses the concept of Metaheuristic agent processes, and a tutorial paper by M.G.C. Resende and C.C. Ribeiro discussing GRASP with path-relinking. Other papers discuss problem-solving approaches to timetabling, automated planograms, elevators, space allocation, shift design, cutting stock, flexible shop scheduling, colorectal cancer and cartography. A final group of methodology papers clarify various aspects of Metaheuristics from the computational view point.

Water distribution systems design optimisation using metaheuristics ...

African Journals Online (AJOL)

The topic of multi-objective water distribution systems (WDS) design optimisation using metaheuristics is investigated, comparing numerous modern metaheuristics, including several multi-objective evolutionary algorithms, an estimation of distribution algorithm and a recent hyperheuristic named AMALGAM (an evolutionary ...

Adaptive Large Neighbourhood Search

DEFF Research Database (Denmark)

Røpke, Stefan

Large neighborhood search is a metaheuristic that has gained popularity in recent years. The heuristic repeatedly moves from solution to solution by first partially destroying the solution and then repairing it. The best solution observed during this search is presented as the final solution....... This tutorial introduces the large neighborhood search metaheuristic and the variant adaptive large neighborhood search that dynamically tunes parameters of the heuristic while it is running. Both heuristics belong to a broader class of heuristics that are searching a solution space using very large...... neighborhoods. The tutorial also present applications of the adaptive large neighborhood search, mostly related to vehicle routing problems for which the heuristic has been extremely successful. We discuss how the heuristic can be parallelized and thereby take advantage of modern desktop computers...

A Hybrid Metaheuristic-Based Approach for the Aerodynamic Optimization of Small Hybrid Wind Turbine Rotors

DEFF Research Database (Denmark)

Herbert-Acero, José F.; Martínez-Lauranchet, Jaime; Probst, Oliver

2014-01-01

of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize...

Metaheuristic Algorithms Applied to Bioenergy Supply Chain Problems: Theory, Review, Challenges, and Future

Directory of Open Access Journals (Sweden)

Krystel K. Castillo-Villar

2014-11-01

Full Text Available Bioenergy is a new source of energy that accounts for a substantial portion of the renewable energy production in many countries. The production of bioenergy is expected to increase due to its unique advantages, such as no harmful emissions and abundance. Supply-related problems are the main obstacles precluding the increase of use of biomass (which is bulky and has low energy density to produce bioenergy. To overcome this challenge, large-scale optimization models are needed to be solved to enable decision makers to plan, design, and manage bioenergy supply chains. Therefore, the use of effective optimization approaches is of great importance. The traditional mathematical methods (such as linear, integer, and mixed-integer programming frequently fail to find optimal solutions for non-convex and/or large-scale models whereas metaheuristics are efficient approaches for finding near-optimal solutions that use less computational resources. This paper presents a comprehensive review by studying and analyzing the application of metaheuristics to solve bioenergy supply chain models as well as the exclusive challenges of the mathematical problems applied in the bioenergy supply chain field. The reviewed metaheuristics include: (1 population approaches, such as ant colony optimization (ACO, the genetic algorithm (GA, particle swarm optimization (PSO, and bee colony algorithm (BCA; and (2 trajectory approaches, such as the tabu search (TS and simulated annealing (SA. Based on the outcomes of this literature review, the integrated design and planning of bioenergy supply chains problem has been solved primarily by implementing the GA. The production process optimization was addressed primarily by using both the GA and PSO. The supply chain network design problem was treated by utilizing the GA and ACO. The truck and task scheduling problem was solved using the SA and the TS, where the trajectory-based methods proved to outperform the population-based

Waste Load Allocation Based on Total Maximum Daily Load Approach Using the Charged System Search (CSS Algorithm

Directory of Open Access Journals (Sweden)

Elham Faraji

2016-03-01

Full Text Available In this research, the capability of a charged system search algorithm (CSS in handling water management optimization problems is investigated. First, two complex mathematical problems are solved by CSS and the results are compared with those obtained from other metaheuristic algorithms. In the last step, the optimization model developed by the CSS algorithm is applied to the waste load allocation in rivers based on the total maximum daily load (TMDL concept. The results are presented in Tables and Figures for easy comparison. The study indicates the superiority of the CSS algorithm in terms of its speed and performance over the other metaheuristic algorithms while its precision in water management optimization problems is verified.

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space

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Shaeen Kalathil

2015-11-01

Full Text Available This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB using canonic signed digit (CSD coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB.

A nuclear heuristic for application to metaheuristics in-core fuel management optimization

Energy Technology Data Exchange (ETDEWEB)

Meneses, Anderson Alvarenga de Moura, E-mail: ameneses@lmp.ufrj.b [COPPE/Federal University of Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program; Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, TI (Switzerland); Gambardella, Luca Maria, E-mail: luca@idsia.c [Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, TI (Switzerland); Schirru, Roberto, E-mail: schirru@lmp.ufrj.b [COPPE/Federal University of Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program

2009-07-01

The In-Core Fuel Management Optimization (ICFMO) is a well-known problem of nuclear engineering whose features are complexity, high number of feasible solutions, and a complex evaluation process with high computational cost, thus it is prohibitive to have a great number of evaluations during an optimization process. Heuristics are criteria or principles for deciding which among several alternative courses of action are more effective with respect to some goal. In this paper, we propose a new approach for the use of relational heuristics for the search in the ICFMO. The Heuristic is based on the reactivity of the fuel assemblies and their position into the reactor core. It was applied to random search, resulting in less computational effort concerning the number of evaluations of loading patterns during the search. The experiments demonstrate that it is possible to achieve results comparable to results in the literature, for future application to metaheuristics in the ICFMO. (author)

A nuclear heuristic for application to metaheuristics in-core fuel management optimization

International Nuclear Information System (INIS)

Meneses, Anderson Alvarenga de Moura; Gambardella, Luca Maria; Schirru, Roberto

2009-01-01

The In-Core Fuel Management Optimization (ICFMO) is a well-known problem of nuclear engineering whose features are complexity, high number of feasible solutions, and a complex evaluation process with high computational cost, thus it is prohibitive to have a great number of evaluations during an optimization process. Heuristics are criteria or principles for deciding which among several alternative courses of action are more effective with respect to some goal. In this paper, we propose a new approach for the use of relational heuristics for the search in the ICFMO. The Heuristic is based on the reactivity of the fuel assemblies and their position into the reactor core. It was applied to random search, resulting in less computational effort concerning the number of evaluations of loading patterns during the search. The experiments demonstrate that it is possible to achieve results comparable to results in the literature, for future application to metaheuristics in the ICFMO. (author)

Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

Directory of Open Access Journals (Sweden)

Longquan Yong

2012-01-01

Full Text Available Harmony search (HS method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

Population-based metaheuristic optimization in neutron optics and shielding design

Energy Technology Data Exchange (ETDEWEB)

DiJulio, D.D., E-mail: Douglas.DiJulio@esss.se [European Spallation Source ERIC, P.O. Box 176, SE-221 00 Lund (Sweden); Division of Nuclear Physics, Lund University, SE-221 00 Lund (Sweden); Björgvinsdóttir, H. [European Spallation Source ERIC, P.O. Box 176, SE-221 00 Lund (Sweden); Department of Physics and Astronomy, Uppsala University, SE-751 20 Uppsala (Sweden); Zendler, C. [European Spallation Source ERIC, P.O. Box 176, SE-221 00 Lund (Sweden); Bentley, P.M. [European Spallation Source ERIC, P.O. Box 176, SE-221 00 Lund (Sweden); Department of Physics and Astronomy, Uppsala University, SE-751 20 Uppsala (Sweden)

2016-11-01

Population-based metaheuristic algorithms are powerful tools in the design of neutron scattering instruments and the use of these types of algorithms for this purpose is becoming more and more commonplace. Today there exists a wide range of algorithms to choose from when designing an instrument and it is not always initially clear which may provide the best performance. Furthermore, due to the nature of these types of algorithms, the final solution found for a specific design scenario cannot always be guaranteed to be the global optimum. Therefore, to explore the potential benefits and differences between the varieties of these algorithms available, when applied to such design scenarios, we have carried out a detailed study of some commonly used algorithms. For this purpose, we have developed a new general optimization software package which combines a number of common metaheuristic algorithms within a single user interface and is designed specifically with neutronic calculations in mind. The algorithms included in the software are implementations of Particle-Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and a Genetic Algorithm (GA). The software has been used to optimize the design of several problems in neutron optics and shielding, coupled with Monte-Carlo simulations, in order to evaluate the performance of the various algorithms. Generally, the performance of the algorithms depended on the specific scenarios, however it was found that DE provided the best average solutions in all scenarios investigated in this work.

Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.

Science.gov (United States)

Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P

2013-04-09

Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.

A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems

Directory of Open Access Journals (Sweden)

Nader Ghaffari-Nasab

2010-07-01

Full Text Available During the past two decades, there have been increasing interests on permutation flow shop with different types of objective functions such as minimizing the makespan, the weighted mean flow-time etc. The permutation flow shop is formulated as a mixed integer programming and it is classified as NP-Hard problem. Therefore, a direct solution is not available and meta-heuristic approaches need to be used to find the near-optimal solutions. In this paper, we present a new discrete firefly meta-heuristic to minimize the makespan for the permutation flow shop scheduling problem. The results of implementation of the proposed method are compared with other existing ant colony optimization technique. The preliminary results indicate that the new proposed method performs better than the ant colony for some well known benchmark problems.

FC-TLBO: fully constrained meta-heuristic algorithm for abundance ...

Indian Academy of Sciences (India)

Omprakash Tembhurne

hyperspectral unmixing; meta-heuristic approach; teaching-learning-based optimisation (TLBO). 1. ... area of research due to its real-time applications. Satellite .... describes the detailed methodology of proposed FC-TLBO. Section 4 contains ...

A Comparison between Different Meta-Heuristic Techniques in Power Allocation for Physical Layer Security

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N. Okati

2017-12-01

Full Text Available Node cooperation can protect wireless networks from eavesdropping by using the physical characteristics of wireless channels rather than cryptographic methods. Allocating the proper amount of power to cooperative nodes is a challenging task. In this paper, we use three cooperative nodes, one as relay to increase throughput at the destination and two friendly jammers to degrade eavesdropper’s link. For this scenario, the secrecy rate function is a non-linear non-convex problem. So, in this case, exact optimization methods can only achieve suboptimal solution. In this paper, we applied different meta-heuristic optimization techniques, like Genetic Algorithm (GA, Partial Swarm Optimization (PSO, Bee Algorithm (BA, Tabu Search (TS, Simulated Annealing (SA and Teaching-Learning-Based Optimization (TLBO. They are compared with each other to obtain solution for power allocation in a wiretap wireless network. Although all these techniques find suboptimal solutions, but they appear superlative to exact optimization methods. Finally, we define a Figure of Merit (FOM as a rule of thumb to determine the best meta-heuristic algorithm. This FOM considers quality of solution, number of required iterations to converge, and CPU time.

Metaheuristics progress in complex systems optimization

CERN Document Server

Doerner, Karl F; Greistorfer, Peter; Gutjahr, Walter; Hartl, Richard F; Reimann, Marc

2007-01-01

The aim of ""Metaheuristics: Progress in Complex Systems Optimization"" is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.

Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm

OpenAIRE

Savsani, Vimal; Patel, Vivek; Gadhvi, Bhargav; Tawhid, Mohamed

2017-01-01

Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS) algorithm, which is based on the search technique of heat transfer search (HTS) algorithm. MOHTS employs the elitist nondominated sorting and crowding dis...

A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem

Energy Technology Data Exchange (ETDEWEB)

Fesanghary, M. [Department of Mechanical Engineering, Louisiana State University, 2508 Patrick Taylor Hall, Baton Rouge, LA 70808 (United States); Ardehali, M.M. [Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424-Hafez Avenue, 15875-4413 Tehran (Iran)

2009-06-15

The increasing costs of fuel and operation of thermal power generating units warrant development of optimization methodologies for economic dispatch (ED) problems. Optimization methodologies that are based on meta-heuristic procedures could assist power generation policy analysts to achieve the goal of minimizing the generation costs. In this context, the objective of this study is to present a novel approach based on harmony search (HS) algorithm for solving ED problems, aiming to provide a practical alternative for conventional methods. To demonstrate the efficiency and applicability of the proposed method and for the purposes of comparison, various types of ED problems are examined. The results of this study show that the new proposed approach is able to find more economical loads than those determined by other methods. (author)

Water distribution systems design optimisation using metaheuristics and hyperheuristics

Directory of Open Access Journals (Sweden)

DN Raad

2011-06-01

Full Text Available The topic of multi-objective water distribution systems (WDS design optimisation using metaheuristics is investigated, comparing numerous modern metaheuristics, including sev- eral multi-objective evolutionary algorithms, an estimation of distribution algorithm and a recent hyperheuristic named AMALGAM (an evolutionary framework for the simultaneous incorporation of multiple metaheuristics, in order to determine which approach is most capa- ble with respect to WDS design optimisation. Novel metaheuristics and variants of existing algorithms are developed, for a total of twenty-three algorithms examined. Testing with re- spect to eight small-to-large-sized WDS benchmarks from the literature reveal that the four top-performing algorithms are mutually non-dominated with respect to the various perfor- mance metrics used. These algorithms are NSGA-II, TAMALGAMJndu , TAMALGAMndu and AMALGAMSndp (the last three being novel variants of AMALGAM. However, when these four algorithms are applied to the design of a very large real-world benchmark, the AMALGAM paradigm outperforms NSGA-II convincingly, with AMALGAMSndp exhibiting the best performance overall.

Iterated Local Search Algorithm with Strategic Oscillation for School Bus Routing Problem with Bus Stop Selection

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Mohammad Saied Fallah Niasar

2017-02-01

Full Text Available he school bus routing problem (SBRP represents a variant of the well-known vehicle routing problem. The main goal of this study is to pick up students allocated to some bus stops and generate routes, including the selected stops, in order to carry students to school. In this paper, we have proposed a simple but effective metaheuristic approach that employs two features: first, it utilizes large neighborhood structures for a deeper exploration of the search space; second, the proposed heuristic executes an efficient transition between the feasible and infeasible portions of the search space. Exploration of the infeasible area is controlled by a dynamic penalty function to convert the unfeasible solution into a feasible one. Two metaheuristics, called N-ILS (a variant of the Nearest Neighbourhood with Iterated Local Search algorithm and I-ILS (a variant of Insertion with Iterated Local Search algorithm are proposed to solve SBRP. Our experimental procedure is based on the two data sets. The results show that N-ILS is able to obtain better solutions in shorter computing times. Additionally, N-ILS appears to be very competitive in comparison with the best existing metaheuristics suggested for SBRP

Metaheuristics in the service industry

CERN Document Server

Geiger, Martin Josef; Sevaux, Marc; Sörensen, Kenneth

2009-01-01

This book presents novel methodological approaches and improved results of metaheuristics for modern services. It examines applications in the area of transportation and logistics, while other areas include production and financial services.

Neural model of gene regulatory network: a survey on supportive meta-heuristics.

Science.gov (United States)

Biswas, Surama; Acharyya, Sriyankar

2016-06-01

Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm

Directory of Open Access Journals (Sweden)

Ali Akbar Hasani

2016-11-01

Full Text Available In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

Ringed Seal Search for Global Optimization via a Sensitive Search Model.

Directory of Open Access Journals (Sweden)

Younes Saadi

Full Text Available The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive and exploitation (intensive of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

Directory of Open Access Journals (Sweden)

N. Rincon-Garcia

2017-01-01

Full Text Available This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’ search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%, travel distance (10.88% and travel time (12.00% compared to previous implementations in reasonable time.

An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

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.

Application of metaheuristics to Loading Pattern Optimization problems based on the IAEA-3D and BIBLIS-2D data

International Nuclear Information System (INIS)

Meneses, Anderson Alvarenga de Moura; Araujo, Lenilson Moreira; Nast, Fernando Nogueira; Da Silva, Patrick Vasconcelos; Schirru, Roberto

2018-01-01

Highlights: •Metaheuristics were applied to Loading Pattern Optimization problems and compared. •The problems are based on data of the benchmarks IAEA and BIBLIS. •The metaheuristics compared were PSO, Cross-Entropy, PBIL and Artificial Bee Colony. •Angra 1 NPP data were also used for further comparison of the algorithms. -- Abstract: The Loading Pattern Optimization (LPO) of a Nuclear Power Plant (NPP), or in-core fuel management optimization, is a real-world and prominent problem in Nuclear Engineering with the goal of finding an optimal (or near-optimal) Loading Pattern (LP), in terms of energy production, within adequate safety margins. Most of the reactor models used in the LPO problem are particular cases, such as research or power reactors with technical data that cannot be made available for several reasons, which makes the reproducibility of tests unattainable. In the present article we report the results of LPO of problems based upon reactor physics benchmarks. Since such data are well-known and widely available in the literature, it is possible to reproduce tests for comparison of techniques. We performed the LPO with the data of the benchmarks IAEA-3D and BIBLIS-2D. The Reactor Physics code RECNOD, which was used in previous works for the optimization of Angra 1 NPP in Brazil, was also used for further comparison. Four Optimization Metaheuristics (OMHs) were applied to those problems: Particle Swarm Optimization (PSO), Cross-Entropy algorithm (CE), Artificial Bee Colony (ABC) and Population-Based Incremental Learning (PBIL). For IAEA-3D, the best algorithm was the ABC. For BIBLIS-2D, PBIL was the best OMH. For Angra 1 / RECNOD optimization problem, PBIL, ABC and CE were the best OMHs.

Modelling and Metaheuristic for Gantry Crane Scheduling and Storage Space Allocation Problem in Railway Container Terminals

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Ming Zeng

2017-01-01

Full Text Available The gantry crane scheduling and storage space allocation problem in the main containers yard of railway container terminal is studied. A mixed integer programming model which comprehensively considers the handling procedures, noncrossing constraints, the safety margin and traveling time of gantry cranes, and the storage modes in the main area is formulated. A metaheuristic named backtracking search algorithm (BSA is then improved to solve this intractable problem. A series of computational experiments are carried out to evaluate the performance of the proposed algorithm under some randomly generated cases based on the practical operation conditions. The results show that the proposed algorithm can gain the near-optimal solutions within a reasonable computation time.

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

Science.gov (United States)

Li, Zixiang; Janardhanan, Mukund Nilakantan; Tang, Qiuhua; Nielsen, Peter

2018-05-01

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.

Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

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Simon Fong

2013-01-01

Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

A cellular automata based FPGA realization of a new metaheuristic bat-inspired algorithm

Science.gov (United States)

Progias, Pavlos; Amanatiadis, Angelos A.; Spataro, William; Trunfio, Giuseppe A.; Sirakoulis, Georgios Ch.

2016-10-01

Optimization algorithms are often inspired by processes occuring in nature, such as animal behavioral patterns. The main concern with implementing such algorithms in software is the large amounts of processing power they require. In contrast to software code, that can only perform calculations in a serial manner, an implementation in hardware, exploiting the inherent parallelism of single-purpose processors, can prove to be much more efficient both in speed and energy consumption. Furthermore, the use of Cellular Automata (CA) in such an implementation would be efficient both as a model for natural processes, as well as a computational paradigm implemented well on hardware. In this paper, we propose a VHDL implementation of a metaheuristic algorithm inspired by the echolocation behavior of bats. More specifically, the CA model is inspired by the metaheuristic algorithm proposed earlier in the literature, which could be considered at least as efficient than other existing optimization algorithms. The function of the FPGA implementation of our algorithm is explained in full detail and results of our simulations are also demonstrated.

CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION

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Radomir Radiša

2017-12-01

Full Text Available This paper presents the use of metaheuristic optimization techniques to support the improvement of casting process. Genetic algorithm (GA, Ant Colony Optimization (ACO, Simulated annealing (SA and Particle Swarm Optimization (PSO have been considered as optimization tools to define the geometry of the casting part’s feeder. The proposed methodology has been demonstrated in the design of the feeder for casting Pelton turbine bucket. The results of the optimization are dimensional characteristics of the feeder, and the best result from all the implemented optimization processes has been adopted. Numerical simulation has been used to verify the validity of the presented design methodology and the feeding system optimization in the casting system of the Pelton turbine bucket.

Theory and principled methods for the design of metaheuristics

CERN Document Server

Borenstein, Yossi

2013-01-01

Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.  In this book the editors establish a bridge between theo

PALNS - A software framework for parallel large neighborhood search

DEFF Research Database (Denmark)

Røpke, Stefan

2009-01-01

This paper propose a simple, parallel, portable software framework for the metaheuristic named large neighborhood search (LNS). The aim is to provide a framework where the user has to set up a few data structures and implement a few functions and then the framework provides a metaheuristic where ...... parallelization "comes for free". We apply the parallel LNS heuristic to two different problems: the traveling salesman problem with pickup and delivery (TSPPD) and the capacitated vehicle routing problem (CVRP)....

Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

International Nuclear Information System (INIS)

Chou, Jui-Sheng; Ngo, Ngoc-Tri

2016-01-01

Highlights: • This study develops a novel time-series sliding window forecast system. • The system integrates metaheuristics, machine learning and time-series models. • Site experiment of smart grid infrastructure is installed to retrieve real-time data. • The proposed system accurately predicts energy consumption in residential buildings. • The forecasting system can help users minimize their electricity usage. - Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kW h. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially

An approach involving dynamic group search optimization for allocating resources in OFDM-based cognitive radio system

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Sameer Suresh Nanivadekar

2018-03-01

Full Text Available Allocation of channel resources in a cognitive radio system for achieving minimized transmission energy at an increased transmission rate is a challenging research. This paper proposes a resource allocation algorithm based on the meta-heuristic search principle. The proposed algorithm is an improved version of the Group Search Optimizer (GSO, which is a currently developed optimization algorithm that works through imitating the searching behaviour of the animals. The improvement is accomplished through introducing dynamics in the maximum pursuit angle of the GSO members. A cognitive radio system, relying on Orthogonal Frequency Division Multiplexing (OFDM for its operation, is simulated and the experimentations are carried out for sub-channel allocation. The proposed algorithm is experimentally compared with five renowned optimization algorithms, namely, conventional GSO, Particle Swarm Optimization, Genetic Algorithm, Firefly Algorithm and Artificial Bee Colony algorithm. The obtained results assert the competing performance of the proposed algorithm over the other algorithms. Keywords: Cognitive radio, OFDM, Resource, Allocation, Optimization, GSO

Hybrid and Cooperative Strategies Using Harmony Search and Artificial Immune Systems for Solving the Nurse Rostering Problem

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Suk Ho Jin

2017-06-01

Full Text Available The nurse rostering problem is an important search problem that features many constraints. In a nurse rostering problem, these constraints are defined by processes such as maintaining work regulations, assigning nurse shifts, and considering nurse preferences. A number of approaches to address these constraints, such as penalty function methods, have been investigated in the literature. We propose two types of hybrid metaheuristic approaches for solving the nurse rostering problem, which are based on combining harmony search techniques and artificial immune systems to balance local and global searches and prevent slow convergence speeds and prematurity. The proposed algorithms are evaluated against a benchmarking dataset of nurse rostering problems; the results show that they identify better or best known solutions compared to those identified in other studies for most instances. The results also show that the combination of harmony search and artificial immune systems is better suited than using single metaheuristic or other hybridization methods for finding upper-bound solutions for nurse rostering problems and discrete optimization problems.

METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS

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V. Panteleev Andrei

2017-01-01

Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and

Tabu search for target-radar assignment

DEFF Research Database (Denmark)

Hindsberger, Magnus; Vidal, Rene Victor Valqui

2000-01-01

In the paper the problem of assigning air-defense illumination radars to enemy targets is presented. A tabu search metaheuristic solution is described and the results achieved are compared to those of other heuristic approaches, implementation and experimental aspects are discussed. It is argued ...

Applications of metaheuristic optimization algorithms in civil engineering

CERN Document Server

Kaveh, A

2017-01-01

The book presents recently developed efficient metaheuristic optimization algorithms and their applications for solving various optimization problems in civil engineering. The concepts can also be used for optimizing problems in mechanical and electrical engineering.

A review of metaheuristic scheduling techniques in cloud computing

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Mala Kalra

2015-11-01

Full Text Available Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO, Genetic Algorithm (GA and Particle Swarm Optimization (PSO, and two novel techniques: League Championship Algorithm (LCA and BAT algorithm.

Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem

International Nuclear Information System (INIS)

Athayde Costa e Silva, Marsil de; Klein, Carlos Eduardo; Mariani, Viviana Cocco; Santos Coelho, Leandro dos

2013-01-01

The environmental/economic dispatch (EED) is an important daily optimization task in the operation of many power systems. It involves the simultaneous optimization of fuel cost and emission objectives which are conflicting ones. The EED problem can be formulated as a large-scale highly constrained nonlinear multiobjective optimization problem. In recent years, many metaheuristic optimization approaches have been reported in the literature to solve the multiobjective EED. In terms of metaheuristics, recently, scatter search approaches are receiving increasing attention, because of their potential to effectively explore a wide range of complex optimization problems. This paper proposes an improved scatter search (ISS) to deal with multiobjective EED problems based on concepts of Pareto dominance and crowding distance and a new scheme for the combination method. In this paper, we have considered the standard IEEE (Institute of Electrical and Electronics Engineers) 30-bus system with 6-generators and the results obtained by proposed ISS algorithm are compared with the other recently reported results in the literature. Simulation results demonstrate that the proposed ISS algorithm is a capable candidate in solving the multiobjective EED problems. - Highlights: ► Economic dispatch. ► We solve the environmental/economic economic power dispatch problem with scatter search. ► Multiobjective scatter search can effectively improve the global search ability

A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures

Science.gov (United States)

Kaveh, A.; Ilchi Ghazaan, M.

2018-02-01

In this article a hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is presented for global optimization of large-scale dome truss structures. The new algorithm is called MDVC-UVPS in which the VPS algorithm acts as the main engine of the algorithm. The VPS algorithm is one of the most recent multi-agent meta-heuristic algorithms mimicking the mechanisms of damped free vibration of single degree of freedom systems. In order to handle a large number of variables, cascade sizing optimization utilizing a series of DVCs is used. Moreover, the UBS is utilized to reduce the computational time. Various dome truss examples are studied to demonstrate the effectiveness and robustness of the proposed method, as compared to some existing structural optimization techniques. The results indicate that the MDVC-UVPS technique is a powerful search and optimization method for optimizing structural engineering problems.

Harmony Search Method: Theory and Applications

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X. Z. Gao

2015-01-01

Full Text Available The Harmony Search (HS method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem.

A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics

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Bharat Singh

2014-11-01

Full Text Available A high-dimensional feature selection having a very large number of features with an optimal feature subset is an NP-complete problem. Because conventional optimization techniques are unable to tackle large-scale feature selection problems, meta-heuristic algorithms are widely used. In this paper, we propose a particle swarm optimization technique while utilizing regression techniques for feature selection. We then use the selected features to classify the data. Classification accuracy is used as a criterion to evaluate classifier performance, and classification is accomplished through the use of k-nearest neighbour (KNN and Bayesian techniques. Various high dimensional data sets are used to evaluate the usefulness of the proposed approach. Results show that our approach gives better results when compared with other conventional feature selection algorithms.

A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.

Science.gov (United States)

Zhang, Aizhu; Sun, Genyun; Ren, Jinchang; Li, Xiaodong; Wang, Zhenjie; Jia, Xiuping

2018-01-01

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.

Modified harmony search

Science.gov (United States)

Mohamed, Najihah; Lutfi Amri Ramli, Ahmad; Majid, Ahmad Abd; Piah, Abd Rahni Mt

2017-09-01

A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in many areas. HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. Propose in this paper Modified Harmony Search for solving optimization problems, which employs a concept from genetic algorithm method and particle swarm optimization for generating new solution vectors that enhances the performance of HS algorithm. The performances of MHS and HS are investigated on ten benchmark optimization problems in order to make a comparison to reflect the efficiency of the MHS in terms of final accuracy, convergence speed and robustness.

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

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Igor Stojanović

2017-01-01

Full Text Available The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

Ant colony system (ACS with hybrid local search to solve vehicle routing problems

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Suphan Sodsoon

2016-02-01

Full Text Available This research applied an Ant Colony System algorithm with a Hybrid Local Search to solve Vehicle Routing Problems (VRP from a single depot when the customers’ requirements are known. VRP is an NP-hard optimization problem and has usually been successfully solved optimum by heuristics. A fleet of vehicles of a specific capacity are used to serve a number of customers at minimum cost, without violating the constraints of vehicle capacity. There are meta-heuristic approaches to solve these problems, such as Simulated Annealing, Genetic Algorithm, Tabu Search and the Ant Colony System algorithm. In this case a hybrid local search was used (Cross-Exchange, Or-Opt and 2-Opt algorithm with an Ant Colony System algorithm. The Experimental Design was tested on 7 various problems from the data set online in the OR-Library. There are five different problems in which customers are randomly distributed with the depot in an approximately central location. The customers were grouped into clusters. The results are evaluated in terms of optimal routes using optimal distances. The experimental results are compared with those obtained from meta-heuristics and they show that the proposed method outperforms six meta-heuristics in the literature.

A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem

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Antonio Costa

2014-07-01

Full Text Available Production processes in Cellular Manufacturing Systems (CMS often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs and Biased Random Sampling (BRS search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation.

A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery

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Maryam Ashouri

2017-07-01

Full Text Available Vehicle routing problem (VRP is a Nondeterministic Polynomial Hard combinatorial optimization problem to serve the consumers from central depots and returned back to the originated depots with given vehicles. Furthermore, two of the most important extensions of the VRPs are the open vehicle routing problem (OVRP and VRP with simultaneous pickup and delivery (VRPSPD. In OVRP, the vehicles have not return to the depot after last visit and in VRPSPD, customers require simultaneous delivery and pick-up service. The aim of this paper is to present a combined effective ant colony optimization (CEACO which includes sweep and several local search algorithms which is different with common ant colony optimization (ACO. An extensive numerical experiment is performed on benchmark problem instances addressed in the literature. The computational result shows that suggested CEACO approach not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms in the literature for solving VRP, OVRP and VRPSPD problems. Keywords: Meta-heuristic algorithms, Vehicle Routing Problem, Open Vehicle Routing Problem, Simultaneously Pickup and Delivery, Ant Colony Optimization.

Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

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S. Molla-Alizadeh-Zavardehi

2014-01-01

Full Text Available This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA, variable neighborhood search (VNS, and simulated annealing (SA frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms.

Iterated local search and record-to-record travel applied to the fixed charge transportation problem

DEFF Research Database (Denmark)

Andersen, Jeanne; Klose, Andreas

The fixed charge transportation problem (FCTP) is a well-known and difficult optimization problem with lots of applications in logistics. It consists in finding a minimum cost network flow from a set of suppliers to a set of customers. Beside costs proportional to quantities transported......, transportation costs do, however, include a fixed charge. Iterated local search and record-to-record travel are both simple local search based meta-heuristics that, to our knowledge, not yet have been applied to the FCTP. In this paper, we apply both types of search strategies and combine them into a single...

Comparison of multiobjective harmony search, cuckoo search and bat-inspired algorithms for renewable distributed generation placement

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John E. Candelo-Becerra

2015-07-01

Full Text Available Electric power losses have a significant impact on the total costs of distribution networks. The use of renewable energy sources is a major alternative to improve power losses and costs, although other important issues are also enhanced such as voltage magnitudes and network congestion. However, determining the best location and size of renewable energy generators can be sometimes a challenging task due to a large number of possible combinations in the search space. Furthermore, the multiobjective functions increase the complexity of the problem and metaheuristics are preferred to find solutions in a relatively short time. This paper evaluates the performance of the cuckoo search (CS, harmony search (HS, and bat-inspired (BA algorithms for the location and size of renewable distributed generation (RDG in radial distribution networks using a multiobjective function defined as minimizing the energy losses and the RDG costs. The metaheuristic algorithms were programmed in Matlab and tested using the 33-node radial distribution network. The three algorithms obtained similar results for the two objectives evaluated, finding points close to the best solutions in the Pareto front. Comparisons showed that the CS obtained the minimum results for most points evaluated, but the BA and the HS were close to the best solution.

Metaheuristic and Machine Learning Models for TFE-731-2, PW4056, and JT8D-9 Cruise Thrust

Science.gov (United States)

Baklacioglu, Tolga

2017-08-01

The requirement for an accurate engine thrust model has a major antecedence in airline fuel saving programs, assessment of environmental effects of fuel consumption, emissions reduction studies, and air traffic management applications. In this study, utilizing engine manufacturers' real data, a metaheuristic model based on genetic algorithms (GAs) and a machine learning model based on neural networks (NNs) trained with Levenberg-Marquardt (LM), delta-bar-delta (DBD), and conjugate gradient (CG) algorithms were accomplished to incorporate the effect of both flight altitude and Mach number in the estimation of thrust. For the GA model, the analysis of population size impact on the model's accuracy and effect of number of data on model coefficients were also performed. For the NN model, design of optimum topology was searched for one- and two-hidden-layer networks. Predicted thrust values presented a close agreement with real thrust data for both models, among which LM trained NNs gave the best accuracies.

Optimization by GRASP greedy randomized adaptive search procedures

CERN Document Server

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...

The Air Traffic Controller Work-Shift Scheduling Problem in Spain from a Multiobjective Perspective: A Metaheuristic and Regular Expression-Based Approach

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Faustino Tello

2018-01-01

Full Text Available We address an air traffic control operator (ATCo work-shift scheduling problem. We consider a multiple objective perspective where the number of ATCos is fixed in advance and a set of ATCo labor conditions have to be satisfied. The objectives deal with the ATCo work and rest periods and positions, the structure of the solution, the number of control center changes, or the distribution of the ATCo workloads. We propose a three-phase problem-solving methodology. In the first phase, a heuristic is used to derive infeasible initial solutions on the basis of templates. Then, a multiple independent run of the simulated annealing metaheuristic is conducted aimed at reaching feasible solutions in the second phase. Finally, a multiple independent simulated annealing run is again conducted from the initial feasible solutions to optimize the objective functions. To do this, we transform the multiple to single optimization problem by using the rank-order centroid function. In the search processes in phases 2 and 3, we use regular expressions to check the ATCo labor conditions in the visited solutions. This provides high testing speed. The proposed approach is illustrated using a real example, and the optimal solution which is reached outperforms an existing template-based reference solution.

Comparing the performance of different meta-heuristics for unweighted parallel machine scheduling

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Adamu, Mumuni Osumah

2015-08-01

Full Text Available This article considers the due window scheduling problem to minimise the number of early and tardy jobs on identical parallel machines. This problem is known to be NP complete and thus finding an optimal solution is unlikely. Three meta-heuristics and their hybrids are proposed and extensive computational experiments are conducted. The purpose of this paper is to compare the performance of these meta-heuristics and their hybrids and to determine the best among them. Detailed comparative tests have also been conducted to analyse the different heuristics with the simulated annealing hybrid giving the best result.

Improving the security of power systems with the use of metaheuristics; Melhorando a seguranca de sistemas de potencia com a utilizacao de metaheuristicas

Energy Technology Data Exchange (ETDEWEB)

Silva Neto, C.A. da [Universidade Federal Fluminense (UFF), Niteroi, RJ (Brazil). Inst. de Computacao], e-mail: cneto@ic.uff.br; Schilling, M.T. [Universidade Federal Fluminense (UFF), Niteroi, RJ (Brazil)], E-mail: schilling@ic.uff.br; Souza, J.C.S. [Universidade Federal Fluminense (UFF), Niteroi, RJ (Brazil). Programa de Pos-Graduacao em Computacao], E-mail: julio@ic.uff.br

2009-07-01

The paper presents aspects leading the combined use of electromechanical simulations complete and metaheuristics in order to increase the safe operation of electric power systems. The index that measuring the level of security and, consequently, the ability to each candidate solution is the level of damping of oscillations voltage. The complete electromechanics simulations allow a more accurate representation of the elements of the grid resulting in a more reliable diagnosis. Metaheuristics possess a high degree of generalization enabling its application in highly complex optimization problems such as the maximization of the attenuation level of voltage oscillations, which occur in a power system, due to a defect in the net. Due to the unprecedented nature of this methodology will be investigated two different metaheuristics, one based on a evolutionary algorithm and the other in particle swarm.

Applying Stochastic Metaheuristics to the Problem of Data Management in a Multi-Tenant Database Cluster

Directory of Open Access Journals (Sweden)

E. A. Boytsov

2014-01-01

Full Text Available A multi-tenant database cluster is a concept of a data-storage subsystem for cloud applications with the multi-tenant architecture. The cluster is a set of relational database servers with the single entry point, combined into one unit with a cluster controller. This system is aimed to be used by applications developed according to Software as a Service (SaaS paradigm and allows to place tenants at database servers so that providing their isolation, data backup and the most effective usage of available computational power. One of the most important problems about such a system is an effective distribution of data into servers, which affects the degree of individual cluster nodes load and faulttolerance. This paper considers the data-management approach, based on the usage of a load-balancing quality measure function. This function is used during initial placement of new tenants and also during placement optimization steps. Standard schemes of metaheuristic optimization such as simulated annealing and tabu search are used to find a better tenant placement.

Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods

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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.

Application of genetic programming in shape optimization of concrete gravity dams by metaheuristics

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Abdolhossein Baghlani

2014-12-01

Full Text Available A gravity dam maintains its stability against the external loads by its massive size. Hence, minimization of the weight of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with a computationally efficient approach is introduced. Genetic programming (GP in conjunction with metaheuristics is used for this purpose. As a case study, shape optimization of the Bluestone dam is presented. Pseudo-dynamic analysis is carried out on a total number of 322 models in order to establish a database of the results. This database is then used to find appropriate relations based on GP for design criteria of the dam. This procedure eliminates the necessity of the time-consuming process of structural analyses in evolutionary optimization methods. The method is hybridized with three different metaheuristics, including particle swarm optimization, firefly algorithm (FA, and teaching–learning-based optimization, and a comparison is made. The results show that although all algorithms are very suitable, FA is slightly superior to other two algorithms in finding a lighter structure in less number of iterations. The proposed method reduces the weight of dam up to 14.6% with very low computational effort.

A Hybrid Metaheuristic-Based Approach for the Aerodynamic Optimization of Small Hybrid Wind Turbine Rotors

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José F. Herbert-Acero

2014-01-01

Full Text Available This work presents a novel framework for the aerodynamic design and optimization of blades for small horizontal axis wind turbines (WT. The framework is based on a state-of-the-art blade element momentum model, which is complemented with the XFOIL 6.96 software in order to provide an estimate of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize the aerodynamic efficiency of small WT (SWT rotors for a wide range of operational conditions. The design variables are (1 the airfoil shape at the different blade span positions and the radial variation of the geometrical variables of (2 chord length, (3 twist angle, and (4 thickness along the blade span. A wind tunnel validation study of optimized rotors based on the NACA 4-digit airfoil series is presented. Based on the experimental data, improvements in terms of the aerodynamic efficiency, the cut-in wind speed, and the amount of material used during the manufacturing process were achieved. Recommendations for the aerodynamic design of SWT rotors are provided based on field experience.

An Automatic Multilevel Image Thresholding Using Relative Entropy and Meta-Heuristic Algorithms

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Josue R. Cuevas

2013-06-01

Full Text Available Multilevel thresholding has been long considered as one of the most popular techniques for image segmentation. Multilevel thresholding outputs a gray scale image in which more details from the original picture can be kept, while binary thresholding can only analyze the image in two colors, usually black and white. However, two major existing problems with the multilevel thresholding technique are: it is a time consuming approach, i.e., finding appropriate threshold values could take an exceptionally long computation time; and defining a proper number of thresholds or levels that will keep most of the relevant details from the original image is a difficult task. In this study a new evaluation function based on the Kullback-Leibler information distance, also known as relative entropy, is proposed. The property of this new function can help determine the number of thresholds automatically. To offset the expensive computational effort by traditional exhaustive search methods, this study establishes a procedure that combines the relative entropy and meta-heuristics. From the experiments performed in this study, the proposed procedure not only provides good segmentation results when compared with a well known technique such as Otsu’s method, but also constitutes a very efficient approach.

An improved harmony search algorithm for power economic load dispatch

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, PPGEPS, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil)], E-mail: leandro.coelho@pucpr.br; Mariani, Viviana Cocco [Pontifical Catholic University of Parana, PUCPR, Department of Mechanical Engineering, PPGEM, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil)], E-mail: viviana.mariani@pucpr.br

2009-10-15

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature.

An improved harmony search algorithm for power economic load dispatch

Energy Technology Data Exchange (ETDEWEB)

Coelho, Leandro dos Santos [Pontifical Catholic Univ. of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, PPGEPS, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil); Mariani, Viviana Cocco [Pontifical Catholic Univ. of Parana, PUCPR, Dept. of Mechanical Engineering, PPGEM, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil)

2009-10-15

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature. (author)

An improved harmony search algorithm for power economic load dispatch

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos; Mariani, Viviana Cocco

2009-01-01

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature.

Metaheuristic simulation optimisation for the stochastic multi-retailer supply chain

Science.gov (United States)

Omar, Marina; Mustaffa, Noorfa Haszlinna H.; Othman, Siti Norsyahida

2013-04-01

Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. A simulation optimization approach has been widely used in research nowadays on finding the best solution for decision-making process in Supply Chain Management (SCM) that generally faced a complexity with large sources of uncertainty and various decision factors. Metahueristic method is the most popular simulation optimization approach. However, very few researches have applied this approach in optimizing the simulation model for supply chains. Thus, this paper interested in evaluating the performance of metahueristic method for stochastic supply chains in determining the best flexible inventory replenishment parameters that minimize the total operating cost. The simulation optimization model is proposed based on the Bees algorithm (BA) which has been widely applied in engineering application such as training neural networks for pattern recognition. BA is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. This model considers an outbound centralised distribution system consisting of one supplier and 3 identical retailers and is assumed to be independent and identically distributed with unlimited supply capacity at supplier.

Protein structure prediction using bee colony optimization metaheuristic

DEFF Research Database (Denmark)

Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

2010-01-01

of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally ¿nds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem....

Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism

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Dawid Połap

2017-09-01

Full Text Available In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO. The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space.

A Metaheuristic Scheduler for Time Division Multiplexed Network-on-Chip

DEFF Research Database (Denmark)

Sørensen, Rasmus Bo; Sparsø, Jens; Pedersen, Mark Ruvald

that this is possible with only negligible impact on the schedule period. We evaluate the scheduler with seven different applications from the MCSL NOC benchmark suite. We observe that the metaheuristics perform better than the greedy solution. In the special case of all-to-all communication with equal bandwidths...

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.

Science.gov (United States)

Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

2016-01-01

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

Optimal calibration of variable biofuel blend dual-injection engines using sparse Bayesian extreme learning machine and metaheuristic optimization

International Nuclear Information System (INIS)

Wong, Ka In; Wong, Pak Kin

2017-01-01

Highlights: • A new calibration method is proposed for dual-injection engines under biofuel blends. • Sparse Bayesian extreme learning machine and flower pollination algorithm are employed in the proposed method. • An SI engine is retrofitted for operating under dual-injection strategy. • The proposed method is verified experimentally under the two idle speed conditions. • Comparison with other machine learning methods and optimization algorithms is conducted. - Abstract: Although many combinations of biofuel blends are available in the market, it is more beneficial to vary the ratio of biofuel blends at different engine operating conditions for optimal engine performance. Dual-injection engines have the potential to implement such function. However, while optimal engine calibration is critical for achieving high performance, the use of two injection systems, together with other modern engine technologies, leads the calibration of the dual-injection engines to a very complicated task. Traditional trial-and-error-based calibration approach can no longer be adopted as it would be time-, fuel- and labor-consuming. Therefore, a new and fast calibration method based on sparse Bayesian extreme learning machine (SBELM) and metaheuristic optimization is proposed to optimize the dual-injection engines operating with biofuels. A dual-injection spark-ignition engine fueled with ethanol and gasoline is employed for demonstration purpose. The engine response for various parameters is firstly acquired, and an engine model is then constructed using SBELM. With the engine model, the optimal engine settings are determined based on recently proposed metaheuristic optimization methods. Experimental results validate the optimal settings obtained with the proposed methodology, indicating that the use of machine learning and metaheuristic optimization for dual-injection engine calibration is effective and promising.

An Improved Crow Search Algorithm Applied to Energy Problems

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Primitivo Díaz

2018-03-01

Full Text Available The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I the awareness probability (AP and (II the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.

Car painting process scheduling with harmony search algorithm

Science.gov (United States)

Syahputra, M. F.; Maiyasya, A.; Purnamawati, S.; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Automotive painting program in the process of painting the car body by using robot power, making efficiency in the production system. Production system will be more efficient if pay attention to scheduling of car order which will be done by considering painting body shape of car. Flow shop scheduling is a scheduling model in which the job-job to be processed entirely flows in the same product direction / path. Scheduling problems often arise if there are n jobs to be processed on the machine, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. Harmony Search Algorithm is a metaheuristic optimization algorithm based on music. The algorithm is inspired by observations that lead to music in search of perfect harmony. This musical harmony is in line to find optimal in the optimization process. Based on the tests that have been done, obtained the optimal car sequence with minimum makespan value.

Distance selection based on relevance feedback in the context of CBIR using the SFS meta-heuristic with one round

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Mawloud Mosbah

2017-03-01

Full Text Available In this paper, we address the selection in the context of Content Based-Image Retrieval (CBIR. Instead of addressing features’ selection issue, we deal here with distance selection as a novel paradigm poorly addressed within CBIR field. Whereas distance concept is a very precise and sharp mathematical tool, we extend the study to weak distances: Similarity, quasi-distance, and divergence. Therefore, as many as eighteen (18 such measures as considered: distances: {Euclidian, …}, similarities{Ruzika, …}, quasi-distances: {Neyman-X2, …} and divergences: {Jeffrey, …}. We specifically propose a hybrid system based on the Sequential Forward Selector (SFS meta-heuristic with one round and relevance feedback. The experiments conducted on the Wang database (Corel-1K using color moments as a signature show that our system yields promising results in terms of effectiveness.

Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm

International Nuclear Information System (INIS)

Liang, Y.-C.; Chen, Y.-C.

2007-01-01

This paper presents a meta-heuristic algorithm, variable neighborhood search (VNS), to the redundancy allocation problem (RAP). The RAP, an NP-hard problem, has attracted the attention of much prior research, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. Authors' previously published work has shown promise for the variable neighborhood descent (VND) method, the simplest version among VNS variations, on RAP. The variable neighborhood search method itself has not been used in reliability design, yet it is a method that fits those combinatorial problems with potential neighborhood structures, as in the case of the RAP. Therefore, authors further extended their work to develop a VNS algorithm for the RAP and tested a set of well-known benchmark problems from the literature. Results on 33 test instances ranging from less to severely constrained conditions show that the variable neighborhood search method improves the performance of VND and provides a competitive solution quality at economically computational expense in comparison with the best-known heuristics including ant colony optimization, genetic algorithm, and tabu search

Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm

Energy Technology Data Exchange (ETDEWEB)

Liang, Y.-C. [Department of Industrial Engineering and Management, Yuan Ze University, No 135 Yuan-Tung Road, Chung-Li, Taoyuan County, Taiwan 320 (China)]. E-mail: ycliang@saturn.yzu.edu.tw; Chen, Y.-C. [Department of Industrial Engineering and Management, Yuan Ze University, No 135 Yuan-Tung Road, Chung-Li, Taoyuan County, Taiwan 320 (China)]. E-mail: s927523@mail.yzu.edu.tw

2007-03-15

This paper presents a meta-heuristic algorithm, variable neighborhood search (VNS), to the redundancy allocation problem (RAP). The RAP, an NP-hard problem, has attracted the attention of much prior research, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. Authors' previously published work has shown promise for the variable neighborhood descent (VND) method, the simplest version among VNS variations, on RAP. The variable neighborhood search method itself has not been used in reliability design, yet it is a method that fits those combinatorial problems with potential neighborhood structures, as in the case of the RAP. Therefore, authors further extended their work to develop a VNS algorithm for the RAP and tested a set of well-known benchmark problems from the literature. Results on 33 test instances ranging from less to severely constrained conditions show that the variable neighborhood search method improves the performance of VND and provides a competitive solution quality at economically computational expense in comparison with the best-known heuristics including ant colony optimization, genetic algorithm, and tabu search.

Partial Transmit Sequence Optimization Using Improved Harmony Search Algorithm for PAPR Reduction in OFDM

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Mangal Singh

2017-12-01

Full Text Available This paper considers the use of the Partial Transmit Sequence (PTS technique to reduce the Peak‐to‐Average Power Ratio (PAPR of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search‐based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

Voltage stability index based optimal placement of static VAR compensator and sizing using Cuckoo search algorithm

Science.gov (United States)

Venkateswara Rao, B.; Kumar, G. V. Nagesh; Chowdary, D. Deepak; Bharathi, M. Aruna; Patra, Stutee

2017-07-01

This paper furnish the new Metaheuristic algorithm called Cuckoo Search Algorithm (CSA) for solving optimal power flow (OPF) problem with minimization of real power generation cost. The CSA is found to be the most efficient algorithm for solving single objective optimal power flow problems. The CSA performance is tested on IEEE 57 bus test system with real power generation cost minimization as objective function. Static VAR Compensator (SVC) is one of the best shunt connected device in the Flexible Alternating Current Transmission System (FACTS) family. It has capable of controlling the voltage magnitudes of buses by injecting the reactive power to system. In this paper SVC is integrated in CSA based Optimal Power Flow to optimize the real power generation cost. SVC is used to improve the voltage profile of the system. CSA gives better results as compared to genetic algorithm (GA) in both without and with SVC conditions.

Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

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Haizhou Wu

2016-01-01

Full Text Available Symbiotic organisms search (SOS is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs. In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

A Novel Self-Adaptive Harmony Search Algorithm

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Kaiping Luo

2013-01-01

Full Text Available The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS, improved harmony search (IHS, and other self-adaptive variants for large-scale optimization problems and constrained problems.

An improved version of Inverse Distance Weighting metamodel assisted Harmony Search algorithm for truss design optimization

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Y. Gholipour

Full Text Available This paper focuses on a metamodel-based design optimization algorithm. The intention is to improve its computational cost and convergence rate. Metamodel-based optimization method introduced here, provides the necessary means to reduce the computational cost and convergence rate of the optimization through a surrogate. This algorithm is a combination of a high quality approximation technique called Inverse Distance Weighting and a meta-heuristic algorithm called Harmony Search. The outcome is then polished by a semi-tabu search algorithm. This algorithm adopts a filtering system and determines solution vectors where exact simulation should be applied. The performance of the algorithm is evaluated by standard truss design problems and there has been a significant decrease in the computational effort and improvement of convergence rate.

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

Science.gov (United States)

Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar

2017-08-01

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

Science.gov (United States)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

Science.gov (United States)

Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid

2017-10-01

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.

A Novel adaptative Discrete Cuckoo Search Algorithm for parameter optimization in computer vision

Directory of Open Access Journals (Sweden)

loubna benchikhi

2017-10-01

Full Text Available Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO, reinforcement learning (RL and ant colony optimization (ACO show the efficiency of this novel method.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

OpenAIRE

Sliwko, L.; Getov, Vladimir

2015-01-01

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded ...

Topology optimization based on the harmony search method

International Nuclear Information System (INIS)

Lee, Seung-Min; Han, Seog-Young

2017-01-01

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclu- sions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

Topology optimization based on the harmony search method

Energy Technology Data Exchange (ETDEWEB)

Lee, Seung-Min; Han, Seog-Young [Hanyang University, Seoul (Korea, Republic of)

2017-06-15

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclu- sions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

Optimization in engineering sciences approximate and metaheuristic methods

CERN Document Server

Stefanoiu, Dan; Popescu, Dumitru; Filip, Florin Gheorghe; El Kamel, Abdelkader

2014-01-01

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU's FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods o

Meta-heuristic algorithms for parallel identical machines scheduling problem with weighted late work criterion and common due date.

Science.gov (United States)

Xu, Zhenzhen; Zou, Yongxing; Kong, Xiangjie

2015-01-01

To our knowledge, this paper investigates the first application of meta-heuristic algorithms to tackle the parallel machines scheduling problem with weighted late work criterion and common due date ([Formula: see text]). Late work criterion is one of the performance measures of scheduling problems which considers the length of late parts of particular jobs when evaluating the quality of scheduling. Since this problem is known to be NP-hard, three meta-heuristic algorithms, namely ant colony system, genetic algorithm, and simulated annealing are designed and implemented, respectively. We also propose a novel algorithm named LDF (largest density first) which is improved from LPT (longest processing time first). The computational experiments compared these meta-heuristic algorithms with LDF, LPT and LS (list scheduling), and the experimental results show that SA performs the best in most cases. However, LDF is better than SA in some conditions, moreover, the running time of LDF is much shorter than SA.

A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization

International Nuclear Information System (INIS)

Mahmoudi, S.M.; Aghaie, M.; Bahonar, M.; Poursalehi, N.

2016-01-01

Highlights: • The Gravitational Search Algorithm (GSA) is introduced. • The advantage of GSA is verified in Shekel’s Foxholes. • Reload optimizing in WWER-1000 and WWER-440 cases are performed. • Maximizing K eff , minimizing PPFs and flattening power density is considered. - Abstract: In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. In recent decades several meta-heuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. This paper presents a new method of using Gravitational Search Algorithm (GSA) for in-core fuel management optimization. The GSA is constructed based on the law of gravity and the notion of mass interactions. It uses the theory of Newtonian physics and searcher agents are the collection of masses. In this work, at the first step, GSA method is compared with other meta-heuristic algorithms on Shekel’s Foxholes problem. In the second step for finding the best core, the GSA algorithm has been performed for three PWR test cases including WWER-1000 and WWER-440 reactors. In these cases, Multi objective optimizations with the following goals are considered, increment of multiplication factor (K eff ), decrement of power peaking factor (PPF) and power density flattening. It is notable that for neutronic calculation, PARCS (Purdue Advanced Reactor Core Simulator) code is used. The results demonstrate that GSA algorithm have promising performance and could be proposed for other optimization problems of nuclear engineering field.

A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

Directory of Open Access Journals (Sweden)

Bruno A. Angélico

2013-12-01

Full Text Available This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO, Genetic Algorithm (GA and an Ant Colony Optimization (ACO are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

Data fitting by G1 rational cubic Bézier curves using harmony search

Directory of Open Access Journals (Sweden)

Najihah Mohamed

2015-07-01

Full Text Available A metaheuristic algorithm, called Harmony Search (HS is implemented for data fitting by rational cubic Bézier curves. HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. HS is suitable for multivariate non-linear optimization problem. It is mainly achieved by data fitting using rational cubic Bézier curves with G1 continuity for every joint of segments of the whole data sets. This approach has significant contributions in making the technique automated. HS is used to optimize positions of middle points and values of the shape parameters. Test outline images and comparative experimental analysis are presented to show effectiveness and robustness of the proposed method. Statistical testing between HS and two other different metaheuristic algorithms is used in the analysis on several outline images. All of the algorithms improvised a near optimal solution but the result that is obtained by the HS is better than the results of the other two algorithms.

Metaheuristic applications to speech enhancement

CERN Document Server

Kunche, Prajna

2016-01-01

This book serves as a basic reference for those interested in the application of metaheuristics to speech enhancement. The major goal of the book is to explain the basic concepts of optimization methods and their use in heuristic optimization in speech enhancement to scientists, practicing engineers, and academic researchers in speech processing. The authors discuss why it has been a challenging problem for researchers to develop new enhancement algorithms that aid in the quality and intelligibility of degraded speech. They present powerful optimization methods to speech enhancement that can help to solve the noise reduction problems. Readers will be able to understand the fundamentals of speech processing as well as the optimization techniques, how the speech enhancement algorithms are implemented by utilizing optimization methods, and will be given the tools to develop new algorithms. The authors also provide a comprehensive literature survey regarding the topic.

A meta-heuristic cuckoo search and eigen permutation approach for ...

Indian Academy of Sciences (India)

Akhilesh Kumar Gupta

2018-04-17

Apr 17, 2018 ... system (HOS) into a simplified lower order model of rea- sonable accuracy by ..... dom walk whose flight step length is dependent on a power law formula often ..... In: IEEE International Conference on Electric. Power and Energy ... hybrid cuckoo search and genetic algorithm for reliability– redundancy ...

Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

Science.gov (United States)

Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar

2017-07-01

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.

Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

Directory of Open Access Journals (Sweden)

E. Osaba

2014-01-01

Full Text Available Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB. The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem and one combinatorial design problem (the one-dimensional bin packing problem have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

Science.gov (United States)

Osaba, E.; Diaz, F.; Carballedo, R.; Onieva, E.; Perallos, A.

2014-01-01

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results. PMID:25165742

Improving search for low energy protein structures with an iterative niche genetic algorithm

DEFF Research Database (Denmark)

Helles, Glennie

2010-01-01

In attempts to predict the tertiary structure of proteins we use almost exclusively metaheuristics. However, despite known differences in performance of metaheuristics for different problems, the effect of the choice of metaheuristic has received precious little attention in this field. Particula......In attempts to predict the tertiary structure of proteins we use almost exclusively metaheuristics. However, despite known differences in performance of metaheuristics for different problems, the effect of the choice of metaheuristic has received precious little attention in this field...

Tabu Search-based Synthesis of Digital Microfluidic Biochips with Dynamically Reconfigurable Non-rectangular Devices

DEFF Research Database (Denmark)

Maftei, Elena; Pop, Paul; Madsen, Jan

2010-01-01

they are highly reconfigurable and scalable. A digital biochip is composed of a two-dimensional array of cells, together with reservoirs for storing the samples and reagents. Several adjacent cells are dynamically grouped to form a virtual device, on which operations are performed. So far, researchers have...... assumed that throughout its execution, an operation is performed on a rectangular virtual device, whose position remains fixed. However, during the execution of an operation, the virtual device can be reconfigured to occupy a different group of cells on the array, forming any shape, not necessarily...... rectangular. In this paper, we present a Tabu Search metaheuristic for the synthesis of digital microfluidic biochips, which, starting from a biochemical application and a given biochip architecture, determines the allocation, resource binding, scheduling and placement of the operations in the application...

A tabu-search heuristic for solving the multi-depot vehicle scheduling problem

Directory of Open Access Journals (Sweden)

Gilmar D'Agostini Oliveira Casalinho

2014-08-01

Full Text Available Currently the logistical problems are relying quite significantly on Operational Research in order to achieve greater efficiency in their operations. Among the problems related to the vehicles scheduling in a logistics system, the Multiple Depot Vehicle Scheduling Problem (MDVSP has been addressed in several studies. The MDVSP presupposes the existence of depots that affect the planning of sequences to which travel must be performed. Often, exact methods cannot solve large instances encountered in practice and in order to take them into account, several heuristic approaches are being developed. The aim of this study was thus to solve the MDVSP using a meta-heuristic based on tabu-search method. The main motivation for this work came from the indication that only recently the use of meta-heuristics is being applied to MDVSP context (Pepin et al. 2008 and, also, the limitations listed by Rohde (2008 in his study, which used the branch-and-bound in one of the steps of the heuristic presented to solve the problem, which has increased the time resolution. The research method for solving this problem was based on adaptations of traditional techniques of Operational Research, and provided resolutions presenting very competitive results for the MDVSP such as the cost of the objective function, number of vehicles used and computational time.

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Directory of Open Access Journals (Sweden)

Kerim Guney

2015-01-01

Full Text Available An evolutionary method based on backtracking search optimization algorithm (BSA is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO, genetic algorithm (GA, modified touring ant colony algorithm (MTACO, quadratic programming method (QPM, bacterial foraging algorithm (BFA, bees algorithm (BA, clonal selection algorithm (CLONALG, plant growth simulation algorithm (PGSA, tabu search algorithm (TSA, memetic algorithm (MA, nondominated sorting GA-2 (NSGA-2, multiobjective differential evolution (MODE, decomposition with differential evolution (MOEA/D-DE, comprehensive learning PSO (CLPSO, harmony search algorithm (HSA, seeker optimization algorithm (SOA, and mean variance mapping optimization (MVMO. The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search.

Science.gov (United States)

Villagra, Andrea; Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

Location, Allocation and Routing of Temporary Health Centers in Rural Areas in Crisis, Solved by Improved Harmony Search Algorithm

Directory of Open Access Journals (Sweden)

Mahdi Alinaghian

2017-01-01

Full Text Available In this paper, an uncertain integrated model for simultaneously locating temporary health centers in the affected areas, allocating affected areas to these centers, and routing to transport their required good is considered. Health centers can be settled in one of the affected areas or in a place out of them; therefore, the proposed model offers the best relief operation policy when it is possible to supply the goods of affected areas (which are customers of goods directly or under coverage. Due to that the problem is NP-Hard, to solve the problem in large-scale, a meta-heuristic algorithm based on harmony search algorithm is presented and its performance has been compared with basic harmony search algorithm and neighborhood search algorithm in small and large scale test problems. The results show that the proposed harmony search algorithm has a suitable efficiency.

Combined heat and power economic dispatch by harmony search algorithm

Energy Technology Data Exchange (ETDEWEB)

Vasebi, A.; Bathaee, S.M.T. [Power System Research Laboratory, Department of Electrical and Electronic Engineering, K.N.Toosi University of Technology, 322-Mirdamad Avenue West, 19697 Tehran (Iran); Fesanghary, M. [Department of Mechanical Engineering, Amirkabir University of Technology, 424-Hafez Avenue, Tehran (Iran)

2007-12-15

The optimal utilization of multiple combined heat and power (CHP) systems is a complicated problem that needs powerful methods to solve. This paper presents a harmony search (HS) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The HS algorithm is a recently developed meta-heuristic algorithm, and has been very successful in a wide variety of optimization problems. The method is illustrated using a test case taken from the literature as well as a new one proposed by authors. Numerical results reveal that the proposed algorithm can find better solutions when compared to conventional methods and is an efficient search algorithm for CHPED problem. (author)

Two parameter-tuned metaheuristic algorithms for the multi-level lot sizing and scheduling problem

Directory of Open Access Journals (Sweden)

S.M.T. Fatemi Ghomi

2012-10-01

Full Text Available This paper addresses the problem of lot sizing and scheduling problem for n-products and m-machines in flow shop environment where setups among machines are sequence-dependent and can be carried over. Many products must be produced under capacity constraints and allowing backorders. Since lot sizing and scheduling problems are well-known strongly NP-hard, much attention has been given to heuristics and metaheuristics methods. This paper presents two metaheuristics algorithms namely, Genetic Algorithm (GA and Imperialist Competitive Algorithm (ICA. Moreover, Taguchi robust design methodology is employed to calibrate the parameters of the algorithms for different size problems. In addition, the parameter-tuned algorithms are compared against a presented lower bound on randomly generated problems. At the end, comprehensive numerical examples are presented to demonstrate the effectiveness of the proposed algorithms. The results showed that the performance of both GA and ICA are very promising and ICA outperforms GA statistically.

Metaheuristic optimization approaches to predict shear-wave velocity from conventional well logs in sandstone and carbonate case studies

Science.gov (United States)

Emami Niri, Mohammad; Amiri Kolajoobi, Rasool; Khodaiy Arbat, Mohammad; Shahbazi Raz, Mahdi

2018-06-01

Seismic wave velocities, along with petrophysical data, provide valuable information during the exploration and development stages of oil and gas fields. The compressional-wave velocity (VP ) is acquired using conventional acoustic logging tools in many drilled wells. But the shear-wave velocity (VS ) is recorded using advanced logging tools only in a limited number of wells, mainly because of the high operational costs. In addition, laboratory measurements of seismic velocities on core samples are expensive and time consuming. So, alternative methods are often used to estimate VS . Heretofore, several empirical correlations that predict VS by using well logging measurements and petrophysical data such as VP , porosity and density are proposed. However, these empirical relations can only be used in limited cases. The use of intelligent systems and optimization algorithms are inexpensive, fast and efficient approaches for predicting VS. In this study, in addition to the widely used Greenberg–Castagna empirical method, we implement three relatively recently developed metaheuristic algorithms to construct linear and nonlinear models for predicting VS : teaching–learning based optimization, imperialist competitive and artificial bee colony algorithms. We demonstrate the applicability and performance of these algorithms to predict Vs using conventional well logs in two field data examples, a sandstone formation from an offshore oil field and a carbonate formation from an onshore oil field. We compared the estimated VS using each of the employed metaheuristic approaches with observed VS and also with those predicted by Greenberg–Castagna relations. The results indicate that, for both sandstone and carbonate case studies, all three implemented metaheuristic algorithms are more efficient and reliable than the empirical correlation to predict VS . The results also demonstrate that in both sandstone and carbonate case studies, the performance of an artificial bee

Improved Multiobjective Harmony Search Algorithm with Application to Placement and Sizing of Distributed Generation

Directory of Open Access Journals (Sweden)

Wanxing Sheng

2014-01-01

Full Text Available To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS. The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application.

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports.

Science.gov (United States)

Schilde, M; Doerner, K F; Hartl, R F

2011-12-01

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances.

Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

Directory of Open Access Journals (Sweden)

Jeng-Fung Chen

2014-10-01

Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

3rd International Conference on Harmony Search Algorithm

CERN Document Server

2017-01-01

This book presents state-of-the-art technical contributions based around one of the most successful evolutionary optimization algorithms published to date: Harmony Search. Contributions span from novel technical derivations of this algorithm to applications in the broad fields of civil engineering, energy, transportation & mobility and health, among many others and focus not only on its cross-domain applicability, but also on its core evolutionary operators, including elements inspired from other meta-heuristics. The global scientific community is witnessing an upsurge in groundbreaking, new advances in all areas of computational intelligence, with a particular flurry of research focusing on evolutionary computation and bio-inspired optimization. Observed processes in nature and sociology have provided the basis for innovative algorithmic developments aimed at leveraging the inherent capability to adapt characterized by various animals, including ants, fireflies, wolves and humans. However, it is the beha...

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

Directory of Open Access Journals (Sweden)

Andrea Villagra

2016-01-01

Full Text Available This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS has shown encouraging results with regard to earlier applications of our methodology.

UN ALGORITMO METAHEURÍSTICO BASADO EN RECOCIDO SIMULADO CON ESPACIO DE BÚSQUEDA GRANULAR PARA EL PROBLEMA DE LOCALIZACIÓN Y RUTEO CON RESTRICCIONES DE CAPACIDAD A META-HEURISTIC ALGORITHM BASED ON THE SIMULATED ANNEALING WITH GRANULAR SEARCH SPACE FOR THE CAPACITATED LOCATION ROUTING PROBLEM

Directory of Open Access Journals (Sweden)

John Willmer Escobar

2012-12-01

Full Text Available Consideramos el problema de localización y ruteo con restricciones de capacidad (CLRP, en el cual la meta es determinar los depósitos a ser abiertos, los clientes a ser asignados a cada depósito abierto, y las rutas a ser construidas para satisfacer las demandas de los clientes. El objetivo es minimizar la suma de los costos de abrir depósitos, de los costos de los vehículos usados, y de los costos variables asociados con la distancia recorrida por las rutas. En este paper, proponemos una metaheurística basada en simulado y recocido con espacio de búsqueda granular para resolver el problema CLRP. Experimentos computacionales en instancias de benchmarking tomadas de la literatura muestran que el algoritmo propuesto es capaz de obtener, dentro de tiempos computacionales razonables, soluciones de alta calidad mostrando su eficacia.The article deals with the Capacitated Location Routing Problem (CLRP where the goal is to determine the depots to be opened, the customers to be assigned to each deposit opened, and the routes to be constructed for fulfilling customers' demands. The objective is to minimize the sum of costs resulting from opening depots, costs resulting from used vehicles, and variable costs associated to the distance across the routes. In this paper, a metaheuristic based on simulated annealing with granular search space for solving the CLRP is proposed. Computational experiments on benchmarking instances taken from literature show that the proposed algorithm is able to obtain high-quality solutions within reasonable computational times, thus showing its efficiency.

Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm

Directory of Open Access Journals (Sweden)

Vimal Savsani

2017-01-01

Full Text Available Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS algorithm, which is based on the search technique of heat transfer search (HTS algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA, and combined PSO-GA based MOEA.

Multiobjective Variable Neighborhood Search algorithm for scheduling independent jobs on computational grid

Directory of Open Access Journals (Sweden)

S. Selvi

2015-07-01

Full Text Available Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to super computers distributed around the world. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. In this paper, an investigation on implementing Multiobjective Variable Neighborhood Search (MVNS algorithm for scheduling independent jobs on computational grid is carried out. The performance of the proposed algorithm has been evaluated with Min–Min algorithm, Simulated Annealing (SA and Greedy Randomized Adaptive Search Procedure (GRASP algorithm. Simulation results show that MVNS algorithm generally performs better than other metaheuristics methods.

Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data

Science.gov (United States)

Alkan, Hilal; Balkaya, Çağlayan

2018-02-01

We present an efficient inversion tool for parameter estimation from horizontal loop electromagnetic (HLEM) data using Differential Search Algorithm (DSA) which is a swarm-intelligence-based metaheuristic proposed recently. The depth, dip, and origin of a thin subsurface conductor causing the anomaly are the parameters estimated by the HLEM method commonly known as Slingram. The applicability of the developed scheme was firstly tested on two synthetically generated anomalies with and without noise content. Two control parameters affecting the convergence characteristic to the solution of the algorithm were tuned for the so-called anomalies including one and two conductive bodies, respectively. Tuned control parameters yielded more successful statistical results compared to widely used parameter couples in DSA applications. Two field anomalies measured over a dipping graphitic shale from Northern Australia were then considered, and the algorithm provided the depth estimations being in good agreement with those of previous studies and drilling information. Furthermore, the efficiency and reliability of the results obtained were investigated via probability density function. Considering the results obtained, we can conclude that DSA characterized by the simple algorithmic structure is an efficient and promising metaheuristic for the other relatively low-dimensional geophysical inverse problems. Finally, the researchers after being familiar with the content of developed scheme displaying an easy to use and flexible characteristic can easily modify and expand it for their scientific optimization problems.

Evidence-based Medicine Search: a customizable federated search engine.

Science.gov (United States)

Bracke, Paul J; Howse, David K; Keim, Samuel M

2008-04-01

This paper reports on the development of a tool by the Arizona Health Sciences Library (AHSL) for searching clinical evidence that can be customized for different user groups. The AHSL provides services to the University of Arizona's (UA's) health sciences programs and to the University Medical Center. Librarians at AHSL collaborated with UA College of Medicine faculty to create an innovative search engine, Evidence-based Medicine (EBM) Search, that provides users with a simple search interface to EBM resources and presents results organized according to an evidence pyramid. EBM Search was developed with a web-based configuration component that allows the tool to be customized for different specialties. Informal and anecdotal feedback from physicians indicates that EBM Search is a useful tool with potential in teaching evidence-based decision making. While formal evaluation is still being planned, a tool such as EBM Search, which can be configured for specific user populations, may help lower barriers to information resources in an academic health sciences center.

Metaheuristic Approaches for Hydropower System Scheduling

Directory of Open Access Journals (Sweden)

Ieda G. Hidalgo

2015-01-01

Full Text Available This paper deals with the short-term scheduling problem of hydropower systems. The objective is to meet the daily energy demand in an economic and safe way. The individuality of the generating units and the nonlinearity of their efficiency curves are taken into account. The mathematical model is formulated as a dynamic, mixed integer, nonlinear, nonconvex, combinatorial, and multiobjective optimization problem. We propose two solution methods using metaheuristic approaches. They combine Genetic Algorithm with Strength Pareto Evolutionary Algorithm and Ant Colony Optimization. Both approaches are divided into two phases. In the first one, to maximize the plant’s net generation, the problem is solved for each hour of the day (static dispatch. In the second phase, to minimize the units’ switching on-off, the day is considered as a whole (dynamic dispatch. The proposed methodology is applied to two Brazilian hydroelectric plants, in cascade, that belong to the national interconnected system. The nondominated solutions from both approaches are presented. All of them meet demand respecting the physical, electrical, and hydraulic constraints.

METAHEURISTICS FOR OPTIMIZING SAFETY STOCK IN MULTI STAGE INVENTORY SYSTEM

Directory of Open Access Journals (Sweden)

Gordan Badurina

2013-02-01

Full Text Available Managing the right level of inventory is critical in order to achieve the targeted level of customer service, but it also carries significant cost in supply chain. In majority of cases companies define safety stock on the most downstream level, i.e. the finished product level, using different analytical methods. Safety stock on upstream level, however, usually covers only those problems which companies face on that particular level (uncertainty of delivery, issues in production, etc.. This paper looks into optimizing safety stock in a pharmaceutical supply considering the three stages inventory system. The problem is defined as a single criterion mixed integer programming problem. The objective is to minimize the inventory cost while the service level is predetermined. In order to coordinate inventories at all echelons, the variable representing the so-called service time is introduced. Because of the problem dimensions, metaheuristics based on genetic algorithm and simulated annealing are constructed and compared, using real data from a Croatian pharmaceutical company. The computational results are presented evidencing improvements in minimizing inventory costs.

An improved harmony search algorithm for synchronization of discrete-time chaotic systems

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos; Andrade Bernert, Diego Luis de

2009-01-01

The harmony search (HS) algorithm is a recently developed meta-heuristic algorithm, and has been very successful in a wide variety of optimization problems. HS was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. The HS algorithm does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. Furthermore, the HS algorithm is simple in concept, few in parameters, easy in implementation, imposes fewer mathematical requirements, and does not require initial value settings of the decision variables. In recent years, the investigation of synchronization and control problem for discrete chaotic systems has attracted much attention, and many possible applications. The tuning of a proportional-integral-derivative (PID) controller based on an improved HS (IHS) algorithm for synchronization of two identical discrete chaotic systems subject the different initial conditions is investigated in this paper. Simulation results of the IHS to determine the PID parameters to synchronization of two Henon chaotic systems are compared with other HS approaches including classical HS and global-best HS. Numerical results reveal that the proposed IHS method is a powerful search and controller design optimization tool for synchronization of chaotic systems.

Cuckoo Search with Lévy Flights for Weighted Bayesian Energy Functional Optimization in Global-Support Curve Data Fitting

Directory of Open Access Journals (Sweden)

Akemi Gálvez

2014-01-01

for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.

An Ad-Hoc Initial Solution Heuristic for Metaheuristic Optimization of Energy Market Participation Portfolios

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Ricardo Faia

2017-06-01

Full Text Available The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations.

Event based neutron activation spectroscopy and analysis algorithm using MLE and metaheuristics

Science.gov (United States)

Wallace, Barton

2014-03-01

Techniques used in neutron activation analysis are often dependent on the experimental setup. In the context of developing a portable and high efficiency detection array, good energy resolution and half-life discrimination are difficult to obtain with traditional methods [1] given the logistic and financial constraints. An approach different from that of spectrum addition and standard spectroscopy analysis [2] was needed. The use of multiple detectors prompts the need for a flexible storage of acquisition data to enable sophisticated post processing of information. Analogously to what is done in heavy ion physics, gamma detection counts are stored as two-dimensional events. This enables post-selection of energies and time frames without the need to modify the experimental setup. This method of storage also permits the use of more complex analysis tools. Given the nature of the problem at hand, a light and efficient analysis code had to be devised. A thorough understanding of the physical and statistical processes [3] involved was used to create a statistical model. Maximum likelihood estimation was combined with metaheuristics to produce a sophisticated curve-fitting algorithm. Simulated and experimental data were fed into the analysis code prompting positive results in terms of half-life discrimination, peak identification and noise reduction. The code was also adapted to other fields of research such as heavy ion identification of the quasi-target (QT) and quasi-particle (QP). The approach used seems to be able to translate well into other fields of research.

Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

Science.gov (United States)

Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

2016-09-01

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

An improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

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)

An Improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

International Nuclear Information System (INIS)

Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S.

2011-01-01

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.

Kinetic theory of nonequilibrium ensembles, irreversible thermodynamics, and generalized hydrodynamics

CERN Document Server

Eu, Byung Chan

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...

Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference.

Science.gov (United States)

Santander-Jiménez, Sergio; Vega-Rodríguez, Miguel A

2013-10-01

The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Content Based Searching for INIS

International Nuclear Information System (INIS)

Jain, V.; Jain, R.K.

2016-01-01

Full text: Whatever a user wants is available on the internet, but to retrieve the information efficiently, a multilingual and most-relevant document search engine is a must. Most current search engines are word based or pattern based. They do not consider the meaning of the query posed to them; purely based on the keywords of the query; no support of multilingual query and and dismissal of nonrelevant results. Current information-retrieval techniques either rely on an encoding process, using a certain perspective or classification scheme, to describe a given item, or perform a full-text analysis, searching for user-specified words. Neither case guarantees content matching because an encoded description might reflect only part of the content and the mere occurrence of a word does not necessarily reflect the document’s content. For general documents, there doesn’t yet seem to be a much better option than lazy full-text analysis, by manually going through those endless results pages. In contrast to this, new search engine should extract the meaning of the query and then perform the search based on this extracted meaning. New search engine should also employ Interlingua based machine translation technology to present information in the language of choice of the user. (author

Elephant swarm water search algorithm for global optimization

Indian Academy of Sciences (India)

S Mandal

2018-02-07

Feb 7, 2018 ... Evolutionary computation and metaheuristics based on swarm intelligence are .... pollen for reproduction or flowering of plants by different pollinators such as insects. Due to long-distance ...... nodes of the denote genes and regulatory interactions between genes are ..... ioral ecology, 3rd ed. Oxford, UK: ...

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

Science.gov (United States)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

Wastewater Treatment Optimization for Fish Migration Using Harmony Search

Directory of Open Access Journals (Sweden)

Zong Woo Geem

2014-01-01

Full Text Available Certain types of fish migrate between the sea and fresh water to spawn. In order for them to swim without any breathing problem, river should contain enough oxygen. If fish is passing along the river in municipal area, it needs sufficient dissolved oxygen level which is influenced by dumped amount of wastewater into the river. If existing treatment methods such as settling and biological oxidation are not enough, we have to consider additional treatment methods such as microscreening filtration and nitrification. This study constructed a wastewater treatment optimization model for migratory fish, which considers three costs (filtration cost, nitrification cost, and irrigation cost and two environmental constraints (minimal dissolved oxygen level and maximal nitrate-nitrogen concentration. Results show that the metaheuristic technique such as harmony search could find good solutions robustly while calculus-based technique such as generalized reduced gradient method was trapped in local optima or even divergent.

Event based neutron activation spectroscopy and analysis algorithm using MLE and meta-heuristics

International Nuclear Information System (INIS)

Wallace, B.

2014-01-01

Techniques used in neutron activation analysis are often dependent on the experimental setup. In the context of developing a portable and high efficiency detection array, good energy resolution and half-life discrimination are difficult to obtain with traditional methods given the logistic and financial constraints. An approach different from that of spectrum addition and standard spectroscopy analysis was needed. The use of multiple detectors prompts the need for a flexible storage of acquisition data to enable sophisticated post processing of information. Analogously to what is done in heavy ion physics, gamma detection counts are stored as two-dimensional events. This enables post-selection of energies and time frames without the need to modify the experimental setup. This method of storage also permits the use of more complex analysis tools. Given the nature of the problem at hand, a light and efficient analysis code had to be devised. A thorough understanding of the physical and statistical processes involved was used to create a statistical model. Maximum likelihood estimation was combined with meta-heuristics to produce a sophisticated curve-fitting algorithm. Simulated and experimental data were fed into the analysis code prompting positive results in terms of half-life discrimination, peak identification and noise reduction. The code was also adapted to other fields of research such as heavy ion identification of the quasi-target (QT) and quasi-particle (QP). The approach used seems to be able to translate well into other fields of research. (author)

Theoretical Investigation of Combined Use of PSO, Tabu Search and Lagrangian Relaxation methods to solve the Unit Commitment Problem

Directory of Open Access Journals (Sweden)

Sahbi Marrouchi

2018-02-01

Full Text Available Solving the Unit Commitment problem (UCP optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed a new strategy based on the combination three optimization methods: Tabu search, Particle swarm optimization and Lagrangian relaxation methods in order to develop a proper unit commitment scheduling of the production units while reducing the production cost during a definite period. The proposed strategy has been implemented on a the IEEE 9 bus test system containing 3 production unit and the results were promising compared to strategies based on meta-heuristic and deterministic methods.

A solution to energy and environmental problems of electric power system using hybrid harmony search-random search optimization algorithm

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Vikram Kumar Kamboj

2016-04-01

Full Text Available In recent years, global warming and carbon dioxide (CO2 emission reduction have become important issues in India, as CO2 emission levels are continuing to rise in accordance with the increased volume of Indian national energy consumption under the pressure of global warming, it is crucial for Indian government to impose the effective policy to promote CO2 emission reduction. Challenge of supplying the nation with high quality and reliable electrical energy at a reasonable cost, converted government policy into deregulation and restructuring environment. This research paper presents aims to presents an effective solution for energy and environmental problems of electric power using an efficient and powerful hybrid optimization algorithm: Hybrid Harmony search-random search algorithm. The proposed algorithm is tested for standard IEEE-14 bus, -30 bus and -56 bus system. The effectiveness of proposed hybrid algorithm is compared with others well known evolutionary, heuristics and meta-heuristics search algorithms. For multi-objective unit commitment, it is found that as there are conflicting relationship between cost and emission, if the performance in cost criterion is improved, performance in the emission is seen to deteriorate.

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization

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Peng Wang

2013-01-01

Full Text Available This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO. The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.

Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey

Energy Technology Data Exchange (ETDEWEB)

Ceylan, Huseyin; Ceylan, Halim; Haldenbilen, Soner; Baskan, Ozgur [Department of Civil Engineering, Engineering Faculty, Pamukkale University, Muh. Fak. Denizli 20017 (Turkey)

2008-07-15

This study proposes a new method for estimating transport energy demand using a harmony search (HS) approach. HArmony Search Transport Energy Demand Estimation (HASTEDE) models are developed taking population, gross domestic product and vehicle kilometers as an input. The HASTEDE models are in forms of linear, exponential and quadratic mathematical expressions and they are applied to Turkish Transportation sector energy consumption. Optimum or near-optimum values of the HS parameters are obtained with sensitivity analysis (SA). Performance of all models is compared with the Ministry of Energy and Natural Resources (MENR) projections. Results showed that HS algorithm may be used for energy modeling, but SA is required to obtain best values of the HS parameters. The quadratic form of HASTEDE will overestimate transport sector energy consumption by about 26% and linear and exponential forms underestimate by about 21% when they are compared with the MENR projections. This may happen due to the modeling procedure and selected parameters for models, but determining the upper and lower values of transportation sector energy consumption will provide a framework and flexibility for setting up energy policies. (author)

Top-k Keyword Search Over Graphs Based On Backward Search

Directory of Open Access Journals (Sweden)

Zeng Jia-Hui

2017-01-01

Full Text Available Keyword search is one of the most friendly and intuitive information retrieval methods. Using the keyword search to get the connected subgraph has a lot of application in the graph-based cognitive computation, and it is a basic technology. This paper focuses on the top-k keyword searching over graphs. We implemented a keyword search algorithm which applies the backward search idea. The algorithm locates the keyword vertices firstly, and then applies backward search to find rooted trees that contain query keywords. The experiment shows that query time is affected by the iteration number of the algorithm.

An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Lihong Guo

2013-01-01

Full Text Available A hybrid metaheuristic approach by hybridizing harmony search (HS and firefly algorithm (FA, namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

Science.gov (United States)

Sonmez, Yusuf; Kahraman, H. Tolga; Dosoglu, M. Kenan; Guvenc, Ugur; Duman, Serhat

2017-05-01

In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.

Dyniqx: a novel meta-search engine for metadata based cross search

OpenAIRE

Zhu, Jianhan; Song, Dawei; Eisenstadt, Marc; Barladeanu, Cristi; Rüger, Stefan

2008-01-01

The effect of metadata in collection fusion has not been sufficiently studied. In response to this, we present a novel meta-search engine called Dyniqx for metadata based cross search. Dyniqx exploits the availability of metadata in academic search services such as PubMed and Google Scholar etc for fusing search results from heterogeneous search engines. In addition, metadata from these search engines are used for generating dynamic query controls such as sliders and tick boxes etc which are ...

Design of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks

Science.gov (United States)

Trueba, Isidoro

fossil fuels to biofuels. In many ways biomass is a unique renewable resource. It can be stored and transported relatively easily in contrast to renewable options such as wind and solar, which create intermittent electrical power that requires immediate consumption and a connection to the grid. This thesis presents two different models for the design optimization of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks. This work compares the performance and solutions obtained by two types of metaheuristic approaches; genetic algorithm and ant colony optimization. Compared to rigorous mathematical optimization methods or iterative algorithms, metaheuristics do not guarantee that a global optimal solution can be found on some class of problems. Problems with similar characteristics to the one presented in this thesis have been previously solved using linear programming, integer programming and mixed integer programming methods. However, depending on the type of problem, these mathematical or complete methods might need exponential computation time in the worst-case. This often leads to computation times too high for practical purposes. Therefore, this thesis develops two types of metaheuristic approaches for the design optimization of a biomass-to-biorefinery logistics system considering multiple types of feedstocks and shows that metaheuristics are highly suitable to solve hard combinatorial optimization problems such as the one addressed in this research work.

Querying archetype-based EHRs by search ontology-based XPath engineering.

Science.gov (United States)

Kropf, Stefan; Uciteli, Alexandr; Schierle, Katrin; Krücken, Peter; Denecke, Kerstin; Herre, Heinrich

2018-05-11

Legacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that lead to false positive results, the precision can be increased by constraining the search to certain parts of documents. A search ontology-based specification of queries on XML documents defines search concepts and relates them to parts in the XML document structure. Such query specification method is practically introduced and evaluated by applying concrete research questions formulated in natural language on a data collection for information retrieval purposes. The search is performed by search ontology-based XPath engineering that reuses ontologies and XML-related W3C standards. The key result is that the specification of research questions can be supported by the usage of search ontology-based XPath engineering. A deeper recognition of entities and a semantic understanding of the content is necessary for a further improvement of precision and recall. Key limitation is that the application of the introduced process requires skills in ontology and software development. In future, the time consuming ontology development could be overcome by implementing a new clinical role: the clinical ontologist. The introduced Search Ontology XML extension connects Search Terms to certain parts in XML documents and enables an ontology-based definition of queries. Search ontology-based XPath engineering can support research question answering by the specification of complex XPath expressions without deep syntax knowledge about XPaths.

Cuckoo search with Lévy flights for weighted Bayesian energy functional optimization in global-support curve data fitting.

Science.gov (United States)

Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis

2014-01-01

The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.

Optimum Design of Gravity Retaining Walls Using Charged System Search Algorithm

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S. Talatahari

2012-01-01

Full Text Available This study focuses on the optimum design retaining walls, as one of the familiar types of the retaining walls which may be constructed of stone masonry, unreinforced concrete, or reinforced concrete. The material cost is one of the major factors in the construction of gravity retaining walls therefore, minimizing the weight or volume of these systems can reduce the cost. To obtain an optimal seismic design of such structures, this paper proposes a method based on a novel meta-heuristic algorithm. The algorithm is inspired by the Coulomb's and Gauss’s laws of electrostatics in physics, and it is called charged system search (CSS. In order to evaluate the efficiency of this algorithm, an example is utilized. Comparing the results of the retaining wall designs obtained by the other methods illustrates a good performance of the CSS. In this paper, we used the Mononobe-Okabe method which is one of the pseudostatic approaches to determine the dynamic earth pressure.

A Meta-Heuristic Applying for the Transportation of Wood Raw Material

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Erhan Çalışkan

2009-04-01

Full Text Available Primary products in Turkish forestry are wood material. Thus, an operational organization is necessary to transport these main products to depots and then to the consumers without quality and volume loss. This organization starts from harvesting area in the stand and continues to roadside depots or ramps and to main depots and even to manufactures from there. The computer-assisted models, which aim to examine the optimum path in transportation, can be utilized in solving this quite complex problem. In this study, an evaluation has been performed in importance and current status of transporting wood material, classification of wood transportation, computer-assisted heuristic and meta-heuristic methods, and possibilities of using these methods in transportation of wood materials.

ASIGNACIÓN DE SUPERVISORES FORESTALES: RESOLUCIÓN MEDIANTE UN ALGORITMO TABU SEARCH ASSIGNMENT OF FOREST SUPERVISORS: RESOLUTION BY MEANS OF A TABU SEARCH ALGORITHM

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Lorena Pradenas Rojas

2008-12-01

Full Text Available En este estudio se presenta un modelo matemático para un problema genérico de asignación de personal. Se implementa y evalúa un procedimiento de solución mediante la metaheurística Tabu Search. El algoritmo propuesto es usado para resolver un caso real de asignación de supervisores forestales. Los resultados muestran que el algoritmo desarrollado es eficiente en la resolución de este tipo de problema y tiene un amplio rango de aplicación para otras situaciones reales.This study presents a mathematical model for a generic problem of staff allocation. A solution is implemented and evaluated by means of the Tabu Search metaheuristic. The proposed algorithm is used to solve a real case of forestry supervisors' allocation. The results show that the developed algorithm is efficient solving this kind of problems and that it has a wide range of application for other real situations.

Process optimization of a non-circular drawing sequence based on multi-surrogate assisted meta-heuristic algorithms

Energy Technology Data Exchange (ETDEWEB)

Pholdee, Nantiwat; Bureerat, Su Jin [Khon Kaen University, Khon Kaen (Thailand); Baek, Hyun Moo [DTaQ, Changwon (Korea, Republic of); Im, Yong Taek [KAIST, Daejeon (Korea, Republic of)

2015-08-15

Process optimization of a Non-circular drawing (NCD) sequence of a pearlitic steel wire was performed to improve the mechanical properties of a drawn wire based on surrogate assisted meta-heuristic algorithms. The objective function was introduced to minimize inhomogeneity of effective strain distribution at the cross-section of the drawn wire, which could deteriorate delamination characteristics of the drawn wires. The design variables introduced were die geometry and reduction of area of the NCD sequence. Several surrogate models and their combinations with the weighted sum technique were utilized. In the process optimization of the NCD sequence, the surrogate models were used to predict effective strain distributions at the cross-section of the drawn wire. Optimization using Differential evolution (DE) algorithm was performed, while the objective function was calculated from the predicted effective strains. The accuracy of all surrogate models was investigated, while optimum results were compared with the previous study available in the literature. It was found that hybrid surrogate models can improve prediction accuracy compared to a single surrogate model. The best result was obtained from the combination of Kriging (KG) and Support vector regression (SVR) models, while the second best was obtained from the combination of four surrogate models: Polynomial response surface (PRS), Radial basic function (RBF), KG, and SVR. The optimum results found in this study showed better effective strain homogeneity at the cross-section of the drawn wire with the same total reduction of area of the previous work available in the literature for fewer number of passes. The multi-surrogate models with the weighted sum technique were found to be powerful in improving the delamination characteristics of the drawn wire and reducing the production cost.

An Improved Harmony Search Algorithm for Power Distribution Network Planning

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Wei Sun

2015-01-01

Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.

HTTP-based Search and Ordering Using ECHO's REST-based and OpenSearch APIs

Science.gov (United States)

Baynes, K.; Newman, D. J.; Pilone, D.

2012-12-01

Metadata is an important entity in the process of cataloging, discovering, and describing Earth science data. NASA's Earth Observing System (EOS) ClearingHOuse (ECHO) acts as the core metadata repository for EOSDIS data centers, providing a centralized mechanism for metadata and data discovery and retrieval. By supporting both the ESIP's Federated Search API and its own search and ordering interfaces, ECHO provides multiple capabilities that facilitate ease of discovery and access to its ever-increasing holdings. Users are able to search and export metadata in a variety of formats including ISO 19115, json, and ECHO10. This presentation aims to inform technically savvy clients interested in automating search and ordering of ECHO's metadata catalog. The audience will be introduced to practical and applicable examples of end-to-end workflows that demonstrate finding, sub-setting and ordering data that is bound by keyword, temporal and spatial constraints. Interaction with the ESIP OpenSearch Interface will be highlighted, as will ECHO's own REST-based API.

A hybrid metaheuristic method to optimize the order of the sequences in continuous-casting

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Achraf Touil

2016-06-01

Full Text Available In this paper, we propose a hybrid metaheuristic algorithm to maximize the production and to minimize the processing time in the steel-making and continuous casting (SCC by optimizing the order of the sequences where a sequence is a group of jobs with the same chemical characteristics. Based on the work Bellabdaoui and Teghem (2006 [Bellabdaoui, A., & Teghem, J. (2006. A mixed-integer linear programming model for the continuous casting planning. International Journal of Production Economics, 104(2, 260-270.], a mixed integer linear programming for scheduling steelmaking continuous casting production is presented to minimize the makespan. The order of the sequences in continuous casting is assumed to be fixed. The main contribution is to analyze an additional way to determine the optimal order of sequences. A hybrid method based on simulated annealing and genetic algorithm restricted by a tabu list (SA-GA-TL is addressed to obtain the optimal order. After parameter tuning of the proposed algorithm, it is tested on different instances using a.NET application and the commercial software solver Cplex v12.5. These results are compared with those obtained by SA-TL (simulated annealing restricted by tabu list.

A Cooperative Harmony Search Algorithm for Function Optimization

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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.

Automated test data generation for branch testing using incremental

Indian Academy of Sciences (India)

Cost of software testing can be reduced by automated test data generation to find a minimal set of data that has maximum coverage. Search-based software testing (SBST) is one of the techniques recently used for automated testing task. SBST makes use of control flow graph (CFG) and meta-heuristic search algorithms to ...

Module-Based Synthesis of Digital Microfluidic Biochips with Droplet-Aware Operation Execution

DEFF Research Database (Denmark)

Maftei, Elena; Pop, Paul; Madsen, Jan

2013-01-01

operations are executed by moving the droplets. So far, researchers have ignored the locations of droplets inside devices, considering that all the electrodes forming the device are occupied throughout the operation execution. In this article, we consider a droplet-aware execution of microfluidic operations......, which means that we know the exact position of droplets inside the modules at each time-step. We propose a Tabu Search-based metaheuristic for the synthesis of digital biochips with droplet-aware operation execution. Experimental results show that our approach can significantly reduce the application...... completion time, allowing us to use smaller area biochips and thus reduce costs....

Modified Parameters of Harmony Search Algorithm for Better Searching

Science.gov (United States)

Farraliza Mansor, Nur; Abal Abas, Zuraida; Samad Shibghatullah, Abdul; Rahman, Ahmad Fadzli Nizam Abdul

2017-08-01

The scheduling and rostering problems are deliberated as integrated due to they depend on each other whereby the input of rostering problems is a scheduling problems. In this research, the integrated scheduling and rostering bus driver problems are defined as maximising the balance of the assignment of tasks in term of distribution of shifts and routes. It is essential to achieve is fairer among driver because this can bring to increase in driver levels of satisfaction. The latest approaches still unable to address the fairness problem that has emerged, thus this research proposes a strategy to adopt an amendment of a harmony search algorithm in order to address the fairness issue and thus the level of fairness will be escalate. The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration. These parameters influence the overall performance of the HS algorithm, and therefore it is crucial to fine-tune them. The contributions to this research are the HMCR parameter using step function while the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter. The model of constant step function is introduced in the alteration of HMCR parameter. The experimental results revealed that our proposed approach is superior than parameter adaptive harmony search algorithm. In conclusion, this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers, which contributed to the lowering of illness, incidents, absenteeism and accidents.

Mathematical programming solver based on local search

CERN Document Server

Gardi, Frédéric; Darlay, Julien; Estellon, Bertrand; Megel, Romain

2014-01-01

This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern about industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces ex...

SOCIAL NETWORK OPTIMIZATION A NEW METHAHEURISTIC FOR GENERAL OPTIMIZATION PROBLEMS

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Hassan Sherafat

2017-12-01

Full Text Available In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.

Improved Harmony Search Algorithm for Truck Scheduling Problem in Multiple-Door Cross-Docking Systems

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Zhanzhong Wang

2018-01-01

Full Text Available The key of realizing the cross docking is to design the joint of inbound trucks and outbound trucks, so a proper sequence of trucks will make the cross-docking system much more efficient and need less makespan. A cross-docking system is proposed with multiple receiving and shipping dock doors. The objective is to find the best door assignments and the sequences of trucks in the principle of products distribution to minimize the total makespan of cross docking. To solve the problem that is regarded as a mixed integer linear programming (MILP model, three metaheuristics, namely, harmony search (HS, improved harmony search (IHS, and genetic algorithm (GA, are proposed. Furthermore, the fixed parameters are optimized by Taguchi experiments to improve the accuracy of solutions further. Finally, several numerical examples are put forward to evaluate the performances of proposed algorithms.

Golden Sine Algorithm: A Novel Math-Inspired Algorithm

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TANYILDIZI, E.

2017-05-01

Full Text Available In this study, Golden Sine Algorithm (Gold-SA is presented as a new metaheuristic method for solving optimization problems. Gold-SA has been developed as a new search algorithm based on population. This math-based algorithm is inspired by sine that is a trigonometric function. In the algorithm, random individuals are created as many as the number of search agents with uniform distribution for each dimension. The Gold-SA operator searches to achieve a better solution in each iteration by trying to bring the current situation closer to the target value. The solution space is narrowed by the golden section so that the areas that are supposed to give only good results are scanned instead of the whole solution space scan. In the tests performed, it is seen that Gold-SA has better results than other population based methods. In addition, Gold-SA has fewer algorithm-dependent parameters and operators than other metaheuristic methods, increasing the importance of this method by providing faster convergence of this new method.

IBRI-CASONTO: Ontology-based semantic search engine

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Awny Sayed

2017-11-01

Full Text Available The vast availability of information, that added in a very fast pace, in the data repositories creates a challenge in extracting correct and accurate information. Which has increased the competition among developers in order to gain access to technology that seeks to understand the intent researcher and contextual meaning of terms. While the competition for developing an Arabic Semantic Search systems are still in their infancy, and the reason could be traced back to the complexity of Arabic Language. It has a complex morphological, grammatical and semantic aspects, as it is a highly inflectional and derivational language. In this paper, we try to highlight and present an Ontological Search Engine called IBRI-CASONTO for Colleges of Applied Sciences, Oman. Our proposed engine supports both Arabic and English language. It is also employed two types of search which are a keyword-based search and a semantics-based search. IBRI-CASONTO is based on different technologies such as Resource Description Framework (RDF data and Ontological graph. The experiments represent in two sections, first it shows a comparison among Entity-Search and the Classical-Search inside the IBRI-CASONTO itself, second it compares the Entity-Search of IBRI-CASONTO with currently used search engines, such as Kngine, Wolfram Alpha and the most popular engine nowadays Google, in order to measure their performance and efficiency.

Optimization in optical systems revisited: Beyond genetic algorithms

Science.gov (United States)

Gagnon, Denis; Dumont, Joey; Dubé, Louis

2013-05-01

Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Optimum Performance-Based Seismic Design Using a Hybrid Optimization Algorithm

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S. Talatahari

2014-01-01

Full Text Available A hybrid optimization method is presented to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized. The algorithm is based on the concepts of the charged system search in which each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Comparison of the results of the hybrid algorithm with those of other metaheuristic algorithms shows the efficiency of the hybrid algorithm.

New technique for global solar radiation forecasting by simulated annealing and genetic algorithms using

International Nuclear Information System (INIS)

Tolabi, H.B.; Ayob, S.M.

2014-01-01

In this paper, a novel approach based on simulated annealing algorithm as a meta-heuristic method is implemented in MATLAB software to estimate the monthly average daily global solar radiation on a horizontal surface for six different climate cities of Iran. A search method based on genetic algorithm is applied to accelerate problem solving. Results show that simulated annealing based on genetic algorithm search is a suitable method to find the global solar radiation. (author)

Nature-inspired Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array

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K. N. Abdul Rani

2012-09-01

Full Text Available In this paper, we proposed a newly modified cuckoo search (MCS algorithm integrated with the Roulette wheel selection operator and the inertia weight controlling the search ability towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL and/or nulls control. The basic cuckoo search (CS algorithm is primarily based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. The CS metaheuristic approach is straightforward and capable of solving effectively general N-dimensional, linear and nonlinear optimization problems. The array geometry synthesis is first formulated as an optimization problem with the goal of SLL suppression and/or null prescribed placement in certain directions, and then solved by the newly MCS algorithm for the optimum element or isotropic radiator locations in the azimuth-plane or xy-plane. The study also focuses on the four internal parameters of MCS algorithm specifically on their implicit effects in the array synthesis. The optimal inter-element spacing solutions obtained by the MCS-optimizer are validated through comparisons with the standard CS-optimizer and the conventional array within the uniform and the Dolph-Chebyshev envelope patterns using MATLABTM. Finally, we also compared the fine-tuned MCS algorithm with two popular evolutionary algorithm (EA techniques include particle swarm optimization (PSO and genetic algorithms (GA.

Computer-based literature search in medical institutions in India

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Kalita Jayantee

2007-01-01

Full Text Available Aim: To study the use of computer-based literature search and its application in clinical training and patient care as a surrogate marker of evidence-based medicine. Materials and Methods: A questionnaire comprising of questions on purpose (presentation, patient management, research, realm (site accessed, nature and frequency of search, effect, infrastructure, formal training in computer based literature search and suggestions for further improvement were sent to residents and faculty of a Postgraduate Medical Institute (PGI and a Medical College. The responses were compared amongst different subgroups of respondents. Results: Out of 300 subjects approached 194 responded; of whom 103 were from PGI and 91 from Medical College. There were 97 specialty residents, 58 super-specialty residents and 39 faculty members. Computer-based literature search was done at least once a month by 89% though there was marked variability in frequency and extent. The motivation for computer-based literature search was for presentation in 90%, research in 65% and patient management in 60.3%. The benefit of search was acknowledged in learning and teaching by 80%, research by 65% and patient care by 64.4% of respondents. Formal training in computer based literature search was received by 41% of whom 80% were residents. Residents from PGI did more frequent and more extensive computer-based literature search, which was attributed to better infrastructure and training. Conclusion: Training and infrastructure both are crucial for computer-based literature search, which may translate into evidence based medicine.

Design and Implementation of a Combinatorial Optimization Multi-population Meta-heuristic for Solving Vehicle Routing Problems

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Eneko Osaba

2016-12-01

Full Text Available This paper aims to give a presentation of the PhD defended by Eneko Osaba on November 16th, 2015, at the University of Deusto. The thesis can be placed in the field of artificial intelligence. Specifically, it is related with multi- population meta-heuristics for solving vehicle routing problems. The dissertation was held in the main auditorium of the University, in a publicly open presentation. After the presentation, Eneko was awarded with the highest grade (cum laude. Additionally, Eneko obtained the PhD obtaining award granted by the Basque Government through.

A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

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Sajad Sabzi

2018-03-01

Full Text Available Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied. This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges (Citrus sinensis L., namely Bam, Payvandi and Thomson. A total of 300 color images were used for the experiments, 100 samples for each orange variety, which are publicly available. After segmentation, 263 parameters, including texture, color and shape features, were extracted from each sample using image processing. Among them, the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm (ANN-PSO. Then, three different classifiers were applied and compared: hybrid artificial neural network – artificial bee colony (ANN-ABC; hybrid artificial neural network – harmony search (ANN-HS; and k-nearest neighbors (kNN. The experimental results show that the hybrid approaches outperform the results of kNN. The average correct classification rate of ANN-HS was 94.28%, while ANN-ABS achieved 96.70% accuracy with the available data, contrasting with the 70.9% baseline accuracy of kNN. Thus, this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties, which can be easily implemented in processing factories. The main contribution of this work is that the method can be directly adapted to other use cases, since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.

Multi-objective Search-based Mobile Testing

OpenAIRE

Mao, K.

2017-01-01

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis...

Optimizing Transmission Network Expansion Planning With The Mean Of Chaotic Differential Evolution Algorithm

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Ahmed R. Abdelaziz

2015-08-01

Full Text Available This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning TNEP using an AC model associated with reactive power planning RPP. The reliabilityredundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits can be subject to the cost weight and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliabilityredundancy optimization problems. Evolutionary algorithms EAs paradigms of evolutionary computation field are stochastic and robust meta-heuristics useful to solve reliabilityredundancy optimization problems. EAs such as genetic algorithm evolutionary programming evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm DEA population-based algorithm is an optimal algorithm with powerful global searching capability but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm CDE based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.

Fuel Management in Candu Reactors Using Tabu Search

International Nuclear Information System (INIS)

Chambon, R.; Varin, E.

2008-01-01

Meta-heuristic methods are perfectly suited to solve fuel management optimization problem in LWR. Indeed, they are originally designed for combinatorial or integer parameter problems which can represent the reloading pattern of the assemblies. For the Candu reactors the problem is however completely different. Indeed, this type of reactor is refueled online. Thus, for their design at fuel reloading equilibrium, the parameter to optimize is the average exit burnup of each fuel channel (which is related to the frequency at which each channel has to be reloaded). It is then a continuous variable that we have to deal with. Originally, this problem was solved using gradient methods. However, their major drawback is the potential local optimum into which they can be trapped. This makes the meta-heuristic methods interesting. In this paper, we have successfully implemented the Tabu Search (TS) method in the reactor diffusion code DONJON. The case of an ACR-700 using 7 burnup zones has been tested. The results have been compared to those we obtained previously with gradient methods. Both methods give equivalent results. This validates them both. The TS has however a major drawback concerning the computation time. A problem with the enrichment as an additional parameter has been tested. In this case, the feasible domain is very narrow, and the optimization process has encountered limitations. Actually, the TS method may not be suitable to find the exact solution of the fuel management problem, but it may be used in a hybrid method such as a TS to find the global optimum region coupled with a gradient method to converge faster on the exact solution. (authors)

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

Science.gov (United States)

Garcia-Santiago, C. A.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S.

2015-11-01

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array.

Science.gov (United States)

Abdul Rani, Khairul Najmy; Abdulmalek, Mohamedfareq; A Rahim, Hasliza; Siew Chin, Neoh; Abd Wahab, Alawiyah

2017-04-20

This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler-Deb-Thiele's (ZDT's) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

Optimal path planning for a mobile robot using cuckoo search algorithm

Science.gov (United States)

Mohanty, Prases K.; Parhi, Dayal R.

2016-03-01

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

The gravitational attraction algorithm: a new metaheuristic applied to a nuclear reactor core design optimization problem

International Nuclear Information System (INIS)

Sacco, Wagner F.; Oliveira, Cassiano R.E. de

2005-01-01

A new metaheuristic called 'Gravitational Attraction Algorithm' (GAA) is introduced in this article. It is an analogy with the gravitational force field, where a body attracts another proportionally to both masses and inversely to their distances. The GAA is a populational algorithm where, first of all, the solutions are clustered using the Fuzzy Clustering Means (FCM) algorithm. Following that, the gravitational forces of the individuals in relation to each cluster are evaluated and this individual or solution is displaced to the cluster with the greatest attractive force. Once it is inside this cluster, the solution receives small stochastic variations, performing a local exploration. Then the solutions are crossed over and the process starts all over again. The parameters required by the GAA are the 'diversity factor', which is used to create a random diversity in a fashion similar to genetic algorithm's mutation, and the number of clusters for the FCM. GAA is applied to the reactor core design optimization problem which consists in adjusting several reactor cell parameters in order to minimize the average peak-factor in a 3-enrichment-zone reactor, considering operational restrictions. This problem was previously attacked using the canonical genetic algorithm (GA) and a Niching Genetic Algorithm (NGA). The new metaheuristic is then compared to those two algorithms. The three algorithms are submitted to the same computational effort and GAA reaches the best results, showing its potential for other applications in the nuclear engineering field as, for instance, the nuclear core reload optimization problem. (author)

A GRASP METAHEURISTIC FOR THE ORDERED CUTTING STOCK PROBLEM UN META-HEURÍSTICO GRASP PARA EL PROBLEMA DE STOCK DE CORTE ORDENADO

Directory of Open Access Journals (Sweden)

Rodrigo Rabello Golfeto

2008-12-01

Full Text Available This study presents a new mathematical model and a Greedy Randomized Adaptive Search Procedure (GRASP meta-heuristic to solve the ordered cutting stock problem. The ordered cutting stock problem was recently introduced in literature. It is appropriate to minimize the raw material used by industries that deal with reduced product inventories, such as industries that use the just-in-time basis for their production. In such cases, classic models for solving the cutting stock problem are useless. Results obtained from computational experiments for a set of random instances demonstrate that the proposed method can be applied to large industries that process cuts on their production lines and do not stock their products.Este estudio presenta un nuevo modelo matemático y un procedimiento meta-heurístico de búsqueda voraz adaptativa y aleatoria (GRASP, por sus siglas en inglés para resolver el problema de stock de corte ordenado. Éste problema ha sido introducido recientemente en la literatura. Es apropiado minimizar la materia prima usada por las industrias que manipulan inventarios reducidos de productos, tales como las industrias que usan la base justo a tiempo para su producción. En tales casos, los modelos clásicos para resolver el problema de stock de corte ordenado son inútiles. Los resultados obtenidos, mediante experimentos computacionales para un conjunto de ejemplos aleatorios, demuestran que el método propuesto puede ser aplicado a industrias grandes que procesan cortes en sus líneas de producción y no mantienen en stock sus productos.

Optimizing the warranty period by cuckoo meta-heuristic algorithm in heterogeneous customers' population

Science.gov (United States)

Roozitalab, Ali; Asgharizadeh, Ezzatollah

2013-12-01

Warranty is now an integral part of each product. Since its length is directly related to the cost of production, it should be set in such a way that it would maximize revenue generation and customers' satisfaction. Furthermore, based on the behavior of customers, it is assumed that increasing the warranty period to earn the trust of more customers leads to more sales until the market is saturated. We should bear in mind that different groups of consumers have different consumption behaviors and that performance of the product has a direct impact on the failure rate over the life of the product. Therefore, the optimum duration for every group is different. In fact, we cannot present different warranty periods for various customer groups. In conclusion, using cuckoo meta-heuristic optimization algorithm, we try to find a common period for the entire population. Results with high convergence offer a term length that will maximize the aforementioned goals simultaneously. The study was tested using real data from Appliance Company. The results indicate a significant increase in sales when the optimization approach was applied; it provides a longer warranty through increased revenue from selling, not only reducing profit margins but also increasing it.

Optimizing heliostat positions with local search metaheuristics using a ray tracing optical model

Science.gov (United States)

Reinholz, Andreas; Husenbeth, Christof; Schwarzbözl, Peter; Buck, Reiner

2017-06-01

The life cycle costs of solar tower power plants are mainly determined by the investment costs of its construction. Significant parts of these investment costs are used for the heliostat field. Therefore, an optimized placement of the heliostats gaining the maximal annual power production has a direct impact on the life cycle costs revenue ratio. We present a two level local search method implemented in MATLAB utilizing the Monte Carlo raytracing software STRAL [1] for the evaluation of the annual power output for a specific weighted annual time scheme. The algorithm was applied to a solar tower power plant (PS10) with 624 heliostats. Compared to former work of Buck [2], we were able to improve both runtime of the algorithm and quality of the output solutions significantly. Using the same environment for both algorithms, we were able to reach Buck's best solution with a speed up factor of about 20.

A Novel Hierarchical Model to Locate Health Care Facilities with Fuzzy Demand Solved by Harmony Search Algorithm

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Mehdi Alinaghian

2014-08-01

Full Text Available In the field of health losses resulting from failure to establish the facilities in a suitable location and the required number, beyond the cost and quality of service will result in an increase in mortality and the spread of diseases. So the facility location models have special importance in this area. In this paper, a successively inclusive hierarchical model for location of health centers in term of the transfer of patients from a lower level to a higher level of health centers has been developed. Since determination the exact number of demand for health care in the future is difficult and in order to make the model close to the real conditions of demand uncertainty, a fuzzy programming model based on credibility theory is considered. To evaluate the proposed model, several numerical examples are solved in small size. In order to solve large scale problems, a meta-heuristic algorithm based on harmony search algorithm was developed in conjunction with the GAMS software which indicants the performance of the proposed algorithm.

Attribute-based proxy re-encryption with keyword search.

Science.gov (United States)

Shi, Yanfeng; Liu, Jiqiang; Han, Zhen; Zheng, Qingji; Zhang, Rui; Qiu, Shuo

2014-01-01

Keyword search on encrypted data allows one to issue the search token and conduct search operations on encrypted data while still preserving keyword privacy. In the present paper, we consider the keyword search problem further and introduce a novel notion called attribute-based proxy re-encryption with keyword search (ABRKS), which introduces a promising feature: In addition to supporting keyword search on encrypted data, it enables data owners to delegate the keyword search capability to some other data users complying with the specific access control policy. To be specific, ABRKS allows (i) the data owner to outsource his encrypted data to the cloud and then ask the cloud to conduct keyword search on outsourced encrypted data with the given search token, and (ii) the data owner to delegate other data users keyword search capability in the fine-grained access control manner through allowing the cloud to re-encrypted stored encrypted data with a re-encrypted data (embedding with some form of access control policy). We formalize the syntax and security definitions for ABRKS, and propose two concrete constructions for ABRKS: key-policy ABRKS and ciphertext-policy ABRKS. In the nutshell, our constructions can be treated as the integration of technologies in the fields of attribute-based cryptography and proxy re-encryption cryptography.

Attribute-Based Proxy Re-Encryption with Keyword Search

Science.gov (United States)

Shi, Yanfeng; Liu, Jiqiang; Han, Zhen; Zheng, Qingji; Zhang, Rui; Qiu, Shuo

2014-01-01

Keyword search on encrypted data allows one to issue the search token and conduct search operations on encrypted data while still preserving keyword privacy. In the present paper, we consider the keyword search problem further and introduce a novel notion called attribute-based proxy re-encryption with keyword search (), which introduces a promising feature: In addition to supporting keyword search on encrypted data, it enables data owners to delegate the keyword search capability to some other data users complying with the specific access control policy. To be specific, allows (i) the data owner to outsource his encrypted data to the cloud and then ask the cloud to conduct keyword search on outsourced encrypted data with the given search token, and (ii) the data owner to delegate other data users keyword search capability in the fine-grained access control manner through allowing the cloud to re-encrypted stored encrypted data with a re-encrypted data (embedding with some form of access control policy). We formalize the syntax and security definitions for , and propose two concrete constructions for : key-policy and ciphertext-policy . In the nutshell, our constructions can be treated as the integration of technologies in the fields of attribute-based cryptography and proxy re-encryption cryptography. PMID:25549257

Assessment and Comparison of Search capabilities of Web-based Meta-Search Engines: A Checklist Approach

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Alireza Isfandiyari Moghadam

2010-03-01

Full Text Available   The present investigation concerns evaluation, comparison and analysis of search options existing within web-based meta-search engines. 64 meta-search engines were identified. 19 meta-search engines that were free, accessible and compatible with the objectives of the present study were selected. An author’s constructed check list was used for data collection. Findings indicated that all meta-search engines studied used the AND operator, phrase search, number of results displayed setting, previous search query storage and help tutorials. Nevertheless, none of them demonstrated any search options for hypertext searching and displaying the size of the pages searched. 94.7% support features such as truncation, keywords in title and URL search and text summary display. The checklist used in the study could serve as a model for investigating search options in search engines, digital libraries and other internet search tools.

A hybridised variable neighbourhood tabu search heuristic to increase security in a utility network

International Nuclear Information System (INIS)

Janssens, Jochen; Talarico, Luca; Sörensen, Kenneth

2016-01-01

We propose a decision model aimed at increasing security in a utility network (e.g., electricity, gas, water or communication network). The network is modelled as a graph, the edges of which are unreliable. We assume that all edges (e.g., pipes, cables) have a certain, not necessarily equal, probability of failure, which can be reduced by selecting edge-specific security strategies. We develop a mathematical programming model and a metaheuristic approach that uses a greedy random adaptive search procedure to find an initial solution and uses tabu search hybridised with iterated local search and a variable neighbourhood descend heuristic to improve this solution. The main goal is to reduce the risk of service failure between an origin and a destination node by selecting the right combination of security measures for each network edge given a limited security budget. - Highlights: • A decision model aimed at increasing security in a utility network is proposed. • The goal is to reduce the risk of service failure given a limited security budget. • An exact approach and a variable neighbourhood tabu search heuristic are developed. • A generator for realistic networks is built and used to test the solution methods. • The hybridised heuristic reduces the total risk on average with 32%.

Allocation and sequencing in 1-out-of-N heterogeneous cold-standby systems: Multi-objective harmony search with dynamic parameters tuning

International Nuclear Information System (INIS)

Valaei, M.R.; Behnamian, J.

2017-01-01

A redundancy allocation is a famous problem in reliability sciences. A lot of researcher investigated about this problem, but a few of them focus on heterogeneous 1-out-of-N: G cold-standby redundancy in each subsystem. This paper considers a redundancy allocation problem (RAP) and standby element sequencing problem (SESP) for 1-out-of-N: G heterogeneous cold-standby system, simultaneously. Moreover, here, maximizing reliability of cold-standby allocation and minimizing cost of buying and time-independent elements are considered as two conflict objectives. This problem is NP-Hard and consequently, devizing a metaheuristic to solve this problem, especially for large-sized instances, is highly desirable. In this paper, we propose a multi-objective harmony search. Based on Taguchi experimental design, we, also, present a new parameters tuning method to improve the proposed algorithm. - Graphical abstract: Example of solution encoding. - Highlights: • This paper considers a redundancy allocation and standby element sequencing problem. • To solve this problem, a multi-objective harmony search algorithm is proposed. • Dynamic parameters tuning method is applied to improved the algorithm. • With this method, sensitivity of the algorithm to initial parameters is reduced.

Optimal design and tuning of novel fractional order PID power system stabilizer using a new metaheuristic Bat algorithm

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Lakhdar Chaib

2017-06-01

Full Text Available This paper proposes a novel robust power system stabilizer (PSS, based on hybridization of fractional order PID controller (PIλDμ and PSS for optimal stabilizer (FOPID-PSS for the first time, using a new metaheuristic optimization Bat algorithm (BA inspired by the echolocation behavior to improve power system stability. The problem of FOPID-PSS design is transformed as an optimization problem based on performance indices (PI, including Integral Absolute Error (IAE, Integral Squared Error (ISE, Integral of the Time-Weighted Absolute Error (ITAE and Integral of Time multiplied by the Squared Error (ITSE, where, BA is employed to obtain the optimal stabilizer parameters. In order to examine the robustness of FOPID-PSS, it has been tested on a Single Machine Infinite Bus (SMIB power system under different disturbances and operating conditions. The performance of the system with FOPID-PSS controller is compared with a PID-PSS and PSS. Further, the simulation results obtained with the proposed BA based FOPID-PSS are compared with those obtained with FireFly algorithm (FFA based FOPID-PSS. Simulation results show the effectiveness of BA for FOPID-PSS design, and superior robust performance for enhancement power system stability compared to other with different cases.

The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

Science.gov (United States)

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Artificial Intelligence, Evolutionary Computing and Metaheuristics In the Footsteps of Alan Turing

CERN Document Server

2013-01-01

Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation.  Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo sear...

An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem

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Majid Yousefikhoshbakht

2016-08-01

Full Text Available The traveling salesman problem (TSP is one of the most important NP-hard Problems and probably the most famous and extensively studied problem in the field of combinatorial optimization. In this problem, a salesman is required to visit each of n given nodes once and only once, starting from any node and returning to the original place of departure. This paper presents an efficient evolutionary optimization algorithm developed through combining imperialist competitive algorithm and lin-kernighan algorithm called (MICALK in order to solve the TSP. The MICALK is tested on 44 TSP instances involving from 24 to 1655 nodes from the literature so that 26 best known solutions of the benchmark problem are also found by our algorithm. Furthermore, the performance of MICALK is compared with several metaheuristic algorithms, including GA, BA, IBA, ICA, GSAP, ABO, PSO and BCO on 32 instances from TSPLIB. The results indicate that the MICALK performs well and is quite competitive with the above algorithms.

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

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Damianos Gavalas

2016-01-01

Full Text Available Given a graph whose nodes and edges are associated with a profit, a visiting (or traversing time and an admittance time window, the Mixed Team Orienteering Problem with Time Windows (MTOPTW seeks for a specific number of walks spanning a subset of nodes and edges of the graph so as to maximize the overall collected profit. The visit of the included nodes and edges should take place within their respective time window and the overall duration of each walk should be below a certain threshold. In this paper we introduce the MTOPTW, which can be used for modeling a realistic variant of the Tourist Trip Design Problem where the objective is the derivation of near-optimal multiple-day itineraries for tourists visiting a destination which features several points of interest (POIs and scenic routes. Since the MTOPTW is a NP-hard problem, we propose the first metaheuristic approaches to tackle it. The effectiveness of our algorithms is validated through a number of experiments on POI and scenic route sets compiled from the city of Athens (Greece.

A Secured Cognitive Agent based Multi-strategic Intelligent Search System

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Neha Gulati

2018-04-01

Full Text Available Search Engine (SE is the most preferred information retrieval tool ubiquitously used. In spite of vast scale involvement of users in SE’s, their limited capabilities to understand the user/searcher context and emotions places high cognitive, perceptual and learning load on the user to maintain the search momentum. In this regard, the present work discusses a Cognitive Agent (CA based approach to support the user in Web-based search process. The work suggests a framework called Secured Cognitive Agent based Multi-strategic Intelligent Search System (CAbMsISS to assist the user in search process. It helps to reduce the contextual and emotional mismatch between the SE’s and user. After implementation of the proposed framework, performance analysis shows that CAbMsISS framework improves Query Retrieval Time (QRT and effectiveness for retrieving relevant results as compared to Present Search Engine (PSE. Supplementary to this, it also provides search suggestions when user accesses a resource previously tagged with negative emotions. Overall, the goal of the system is to enhance the search experience for keeping the user motivated. The framework provides suggestions through the search log that tracks the queries searched, resources accessed and emotions experienced during the search. The implemented framework also considers user security. Keywords: BDI model, Cognitive Agent, Emotion, Information retrieval, Intelligent search, Search Engine

Hybrid real-code ant colony optimisation for constrained mechanical design

Science.gov (United States)

Pholdee, Nantiwat; Bureerat, Sujin

2016-01-01

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

Analysis of human error in occupational accidents in the power plant industries using combining innovative FTA and meta-heuristic algorithms

OpenAIRE

M. Omidvari; M. R. Gharmaroudi

2015-01-01

Introduction: Occupational accidents are of the main issues in industries. It is necessary to identify the main root causes of accidents for their control. Several models have been proposed for determining the accidents root causes. FTA is one of the most widely used models which could graphically establish the root causes of accidents. The non-linear function is one of the main challenges in FTA compliance and in order to obtain the exact number, the meta-heuristic algorithms can be used. ...

Considerations for the development of task-based search engines

DEFF Research Database (Denmark)

Petcu, Paula; Dragusin, Radu

2013-01-01

Based on previous experience from working on a task-based search engine, we present a list of suggestions and ideas for an Information Retrieval (IR) framework that could inform the development of next generation professional search systems. The specific task that we start from is the clinicians......' information need in finding rare disease diagnostic hypotheses at the time and place where medical decisions are made. Our experience from the development of a search engine focused on supporting clinicians in completing this task has provided us valuable insights in what aspects should be considered...... by the developers of vertical search engines....

Comparative Analysis of Rank Aggregation Techniques for Metasearch Using Genetic Algorithm

Science.gov (United States)

Kaur, Parneet; Singh, Manpreet; Singh Josan, Gurpreet

2017-01-01

Rank Aggregation techniques have found wide applications for metasearch along with other streams such as Sports, Voting System, Stock Markets, and Reduction in Spam. This paper presents the optimization of rank lists for web queries put by the user on different MetaSearch engines. A metaheuristic approach such as Genetic algorithm based rank…

Object-based target templates guide attention during visual search

OpenAIRE

Berggren, Nick; Eimer, Martin

2018-01-01

During visual search, attention is believed to be controlled in a strictly feature-based fashion, without any guidance by object-based target representations. To challenge this received view, we measured electrophysiological markers of attentional selection (N2pc component) and working memory (SPCN) in search tasks where two possible targets were defined by feature conjunctions (e.g., blue circles and green squares). Critically, some search displays also contained nontargets with two target f...

A new hybrid metaheuristic algorithm for wind farm micrositing

International Nuclear Information System (INIS)

Massan, S.U.R.; Wagan, A.I.; Shaikh, M.M.

2017-01-01

This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic Algorithm) for the solution of the WTO (Wind Turbine Optimization) problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (Differential Evolution Algorithm) and the FA (Firefly Algorithm). The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic Algorithm) used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together. (author)

A New Hybrid Metaheuristic Algorithm for Wind Farm Micrositing

Directory of Open Access Journals (Sweden)

SHAFIQ-UR-REHMAN MASSAN

2017-07-01

Full Text Available This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic Algorithm for the solution of the WTO (Wind Turbine Optimization problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (Differential Evolution Algorithm and the FA (Firefly Algorithm. The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic Algorithm used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together.

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

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Sivashan Chetty

2015-01-01

Full Text Available The Just-In-Time (JIT scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA, is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS and Simulated Annealing (SA. SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm.

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships.

Science.gov (United States)

Zong, Nansu; Lee, Sungin; Ahn, Jinhyun; Kim, Hong-Gee

2017-08-01

The keyword-based entity search restricts search space based on the preference of search. When given keywords and preferences are not related to the same biomedical topic, existing biomedical Linked Data search engines fail to deliver satisfactory results. This research aims to tackle this issue by supporting an inter-topic search-improving search with inputs, keywords and preferences, under different topics. This study developed an effective algorithm in which the relations between biomedical entities were used in tandem with a keyword-based entity search, Siren. The algorithm, PERank, which is an adaptation of Personalized PageRank (PPR), uses a pair of input: (1) search preferences, and (2) entities from a keyword-based entity search with a keyword query, to formalize the search results on-the-fly based on the index of the precomputed Individual Personalized PageRank Vectors (IPPVs). Our experiments were performed over ten linked life datasets for two query sets, one with keyword-preference topic correspondence (intra-topic search), and the other without (inter-topic search). The experiments showed that the proposed method achieved better search results, for example a 14% increase in precision for the inter-topic search than the baseline keyword-based search engine. The proposed method improved the keyword-based biomedical entity search by supporting the inter-topic search without affecting the intra-topic search based on the relations between different entities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

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Laxmi A. Bewoor

2017-10-01

Full Text Available The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

Chemical Information in Scirus and BASE (Bielefeld Academic Search Engine)

Science.gov (United States)

Bendig, Regina B.

2009-01-01

The author sought to determine to what extent the two search engines, Scirus and BASE (Bielefeld Academic Search Engines), would be useful to first-year university students as the first point of searching for chemical information. Five topics were searched and the first ten records of each search result were evaluated with regard to the type of…

Content-based Music Search and Recommendation System

Science.gov (United States)

Takegawa, Kazuki; Hijikata, Yoshinori; Nishida, Shogo

Recently, the turn volume of music data on the Internet has increased rapidly. This has increased the user's cost to find music data suiting their preference from such a large data set. We propose a content-based music search and recommendation system. This system has an interface for searching and finding music data and an interface for editing a user profile which is necessary for music recommendation. By exploiting the visualization of the feature space of music and the visualization of the user profile, the user can search music data and edit the user profile. Furthermore, by exploiting the infomation which can be acquired from each visualized object in a mutually complementary manner, we make it easier for the user to search music data and edit the user profile. Concretely, the system gives to the user an information obtained from the user profile when searching music data and an information obtained from the feature space of music when editing the user profile.

GeoSearcher: Location-Based Ranking of Search Engine Results.

Science.gov (United States)

Watters, Carolyn; Amoudi, Ghada

2003-01-01

Discussion of Web queries with geospatial dimensions focuses on an algorithm that assigns location coordinates dynamically to Web sites based on the URL. Describes a prototype search system that uses the algorithm to re-rank search engine results for queries with a geospatial dimension, thus providing an alternative ranking order for search engine…

Heuristic for Solving the Multiple Alignment Sequence Problem

Directory of Open Access Journals (Sweden)

Roman Anselmo Mora Gutiérrez

2011-03-01

Full Text Available In this paper we developed a new algorithm for solving the problem of multiple sequence alignment (AM S, which is a hybrid metaheuristic based on harmony search and simulated annealing. The hybrid was validated with the methodology of Julie Thompson. This is a basic algorithm and and results obtained during this stage are encouraging.

Knowledge-based personalized search engine for the Web-based Human Musculoskeletal System Resources (HMSR) in biomechanics.

Science.gov (United States)

Dao, Tien Tuan; Hoang, Tuan Nha; Ta, Xuan Hien; Tho, Marie Christine Ho Ba

2013-02-01

Human musculoskeletal system resources of the human body are valuable for the learning and medical purposes. Internet-based information from conventional search engines such as Google or Yahoo cannot response to the need of useful, accurate, reliable and good-quality human musculoskeletal resources related to medical processes, pathological knowledge and practical expertise. In this present work, an advanced knowledge-based personalized search engine was developed. Our search engine was based on a client-server multi-layer multi-agent architecture and the principle of semantic web services to acquire dynamically accurate and reliable HMSR information by a semantic processing and visualization approach. A security-enhanced mechanism was applied to protect the medical information. A multi-agent crawler was implemented to develop a content-based database of HMSR information. A new semantic-based PageRank score with related mathematical formulas were also defined and implemented. As the results, semantic web service descriptions were presented in OWL, WSDL and OWL-S formats. Operational scenarios with related web-based interfaces for personal computers and mobile devices were presented and analyzed. Functional comparison between our knowledge-based search engine, a conventional search engine and a semantic search engine showed the originality and the robustness of our knowledge-based personalized search engine. In fact, our knowledge-based personalized search engine allows different users such as orthopedic patient and experts or healthcare system managers or medical students to access remotely into useful, accurate, reliable and good-quality HMSR information for their learning and medical purposes. Copyright © 2012 Elsevier Inc. All rights reserved.

Three dimensional pattern recognition using feature-based indexing and rule-based search

Science.gov (United States)

Lee, Jae-Kyu

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells. This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene. Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage. Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size

The Process Synthesis Pyramid: Conceptual design of a Liquefied Energy Chain using Pinch Analysis,Exergy Analysis,Deterministic Optimization and Metaheuristic Searches

International Nuclear Information System (INIS)

Aspelund, Audun

2012-01-01

Process Synthesis (PS) is a term used to describe a class of general and systematic methods for the conceptual design of processing plants and energy systems. The term also refers to the development of the process flowsheet (structure or topology), the selection of unit operations and the determination of the most important operating conditions.In this thesis an attempt is made to characterize some of the most common methodologies in a PS pyramid and discuss their advantages and disadvantages as well as where in the design phase they could be used most efficiently. The thesis shows how design tools have been developed for subambient processes by combining and expanding PS methods such as Heuristic Rules, sequential modular Process Simulations, Pinch Analysis, Exergy Analysis, Mathematical Programming using Deterministic Optimization methods and optimization using Stochastic Optimization methods. The most important contributions to the process design community are three new methodologies that include the pressure as an important variable in heat exchanger network synthesis (HENS).The methodologies have been used to develop a novel and efficient energy chain based on stranded natural gas including power production with carbon capture and sequestration (CCS). This Liquefied Energy Chain consists of an offshore process a combined gas carrier and an onshore process. This energy chain is capable of efficiently exploiting resources that cannot be utilized economically today with minor Co2 emissions. Finally, a new Stochastic Optimization approach based on a Tabu Search (TS), the Nelder Mead method or Downhill Simplex Method (NMDS) and the sequential process simulator HYSYS is used to search for better solutions for the Liquefied Energy Chain with respect to minimum cost or maximum profit. (au)

The Process Synthesis Pyramid: Conceptual design of a Liquefied Energy Chain using Pinch Analysis,Exergy Analysis,Deterministic Optimization and Metaheuristic Searches

Energy Technology Data Exchange (ETDEWEB)

Aspelund, Audun

2012-07-01

Process Synthesis (PS) is a term used to describe a class of general and systematic methods for the conceptual design of processing plants and energy systems. The term also refers to the development of the process flowsheet (structure or topology), the selection of unit operations and the determination of the most important operating conditions.In this thesis an attempt is made to characterize some of the most common methodologies in a PS pyramid and discuss their advantages and disadvantages as well as where in the design phase they could be used most efficiently. The thesis shows how design tools have been developed for subambient processes by combining and expanding PS methods such as Heuristic Rules, sequential modular Process Simulations, Pinch Analysis, Exergy Analysis, Mathematical Programming using Deterministic Optimization methods and optimization using Stochastic Optimization methods. The most important contributions to the process design community are three new methodologies that include the pressure as an important variable in heat exchanger network synthesis (HENS).The methodologies have been used to develop a novel and efficient energy chain based on stranded natural gas including power production with carbon capture and sequestration (CCS). This Liquefied Energy Chain consists of an offshore process a combined gas carrier and an onshore process. This energy chain is capable of efficiently exploiting resources that cannot be utilized economically today with minor Co2 emissions. Finally, a new Stochastic Optimization approach based on a Tabu Search (TS), the Nelder Mead method or Downhill Simplex Method (NMDS) and the sequential process simulator HYSYS is used to search for better solutions for the Liquefied Energy Chain with respect to minimum cost or maximum profit. (au)

MetaboSearch: tool for mass-based metabolite identification using multiple databases.

Directory of Open Access Journals (Sweden)

Bin Zhou

Full Text Available Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB, Madison Metabolomics Consortium Database (MMCD, Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information.The software tool is available at http://omics.georgetown.edu/MetaboSearch.html.

Research on perturbation based Monte Carlo reactor criticality search

International Nuclear Information System (INIS)

Li Zeguang; Wang Kan; Li Yangliu; Deng Jingkang

2013-01-01

Criticality search is a very important aspect in reactor physics analysis. Due to the advantages of Monte Carlo method and the development of computer technologies, Monte Carlo criticality search is becoming more and more necessary and feasible. Traditional Monte Carlo criticality search method is suffered from large amount of individual criticality runs and uncertainty and fluctuation of Monte Carlo results. A new Monte Carlo criticality search method based on perturbation calculation is put forward in this paper to overcome the disadvantages of traditional method. By using only one criticality run to get initial k_e_f_f and differential coefficients of concerned parameter, the polynomial estimator of k_e_f_f changing function is solved to get the critical value of concerned parameter. The feasibility of this method was tested. The results show that the accuracy and efficiency of perturbation based criticality search method are quite inspiring and the method overcomes the disadvantages of traditional one. (authors)

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

GRASP (Greedy Randomized Adaptive Search Procedures) applied to optimization of petroleum products distribution in pipeline networks; GRASP (Greedy Randomized Adaptative Search Procedures) aplicado ao 'scheduling' de redes de distribuicao de petroleo e derivados

Energy Technology Data Exchange (ETDEWEB)

Conte, Viviane Cristhyne Bini; Arruda, Lucia Valeria Ramos de; Yamamoto, Lia [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil)

2008-07-01

Planning and scheduling of the pipeline network operations aim the most efficient use of the resources resulting in a better performance of the network. A petroleum distribution pipeline network is composed by refineries, sources and/or storage parks, connected by a set of pipelines, which operate the transportation of petroleum and derivatives among adjacent areas. In real scenes, this problem is considered a combinatorial problem, which has difficult solution, which makes necessary methodologies of the resolution that present low computational time. This work aims to get solutions that attempt the demands and minimize the number of batch fragmentations on the sent operations of products for the pipelines in a simplified model of a real network, through by application of the local search metaheuristic GRASP. GRASP does not depend of solutions of previous iterations and works in a random way so it allows the search for the solution in an ampler and diversified search space. GRASP utilization does not demand complex calculation, even the construction stage that requires more computational effort, which provides relative rapidity in the attainment of good solutions. GRASP application on the scheduling of the operations of this network presented feasible solutions in a low computational time. (author)

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

Directory of Open Access Journals (Sweden)

Kevin M. Betts

2016-01-01

Full Text Available Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing and the method most commonly used today (Monte Carlo testing. The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1 finding the single most challenging test case and (2 finding the set of fifty test cases with the highest mean degree of challenge.

A Systematic Understanding of Successful Web Searches in Information-Based Tasks

Science.gov (United States)

Zhou, Mingming

2013-01-01

The purpose of this study is to research how Chinese university students solve information-based problems. With the Search Performance Index as the measure of search success, participants were divided into high, medium and low-performing groups. Based on their web search logs, these three groups were compared along five dimensions of the search…

Development of health information search engine based on metadata and ontology.

Science.gov (United States)

Song, Tae-Min; Park, Hyeoun-Ae; Jin, Dal-Lae

2014-04-01

The aim of the study was to develop a metadata and ontology-based health information search engine ensuring semantic interoperability to collect and provide health information using different application programs. Health information metadata ontology was developed using a distributed semantic Web content publishing model based on vocabularies used to index the contents generated by the information producers as well as those used to search the contents by the users. Vocabulary for health information ontology was mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and a list of about 1,500 terms was proposed. The metadata schema used in this study was developed by adding an element describing the target audience to the Dublin Core Metadata Element Set. A metadata schema and an ontology ensuring interoperability of health information available on the internet were developed. The metadata and ontology-based health information search engine developed in this study produced a better search result compared to existing search engines. Health information search engine based on metadata and ontology will provide reliable health information to both information producer and information consumers.

Supervised learning of tools for content-based search of image databases

Science.gov (United States)

Delanoy, Richard L.

1996-03-01

A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

Perturbation based Monte Carlo criticality search in density, enrichment and concentration

International Nuclear Information System (INIS)

Li, Zeguang; Wang, Kan; Deng, Jingkang

2015-01-01

Highlights: • A new perturbation based Monte Carlo criticality search method is proposed. • The method could get accurate results with only one individual criticality run. • The method is used to solve density, enrichment and concentration search problems. • Results show the feasibility and good performances of this method. • The relationship between results’ accuracy and perturbation order is discussed. - Abstract: Criticality search is a very important aspect in reactor physics analysis. Due to the advantages of Monte Carlo method and the development of computer technologies, Monte Carlo criticality search is becoming more and more necessary and feasible. Existing Monte Carlo criticality search methods need large amount of individual criticality runs and may have unstable results because of the uncertainties of criticality results. In this paper, a new perturbation based Monte Carlo criticality search method is proposed and discussed. This method only needs one individual criticality calculation with perturbation tallies to estimate k eff changing function using initial k eff and differential coefficients results, and solves polynomial equations to get the criticality search results. The new perturbation based Monte Carlo criticality search method is implemented in the Monte Carlo code RMC, and criticality search problems in density, enrichment and concentration are taken out. Results show that this method is quite inspiring in accuracy and efficiency, and has advantages compared with other criticality search methods

Impact of Glaucoma and Dry Eye on Text-Based Searching

Science.gov (United States)

Sun, Michelle J.; Rubin, Gary S.; Akpek, Esen K.; Ramulu, Pradeep Y.

2017-01-01

Purpose We determine if visual field loss from glaucoma and/or measures of dry eye severity are associated with difficulty searching, as judged by slower search times on a text-based search task. Methods Glaucoma patients with bilateral visual field (VF) loss, patients with clinically significant dry eye, and normally-sighted controls were enrolled from the Wilmer Eye Institute clinics. Subjects searched three Yellow Pages excerpts for a specific phone number, and search time was recorded. Results A total of 50 glaucoma subjects, 40 dry eye subjects, and 45 controls completed study procedures. On average, glaucoma patients exhibited 57% longer search times compared to controls (95% confidence interval [CI], 26%–96%, P Dry eye subjects demonstrated similar search times compared to controls, though worse Ocular Surface Disease Index (OSDI) vision-related subscores were associated with longer search times (P dry eye (P > 0.08 for Schirmer's testing without anesthesia, corneal fluorescein staining, and tear film breakup time). Conclusions Text-based visual search is slower for glaucoma patients with greater levels of VF loss and dry eye patients with greater self-reported visual difficulty, and these difficulties may contribute to decreased quality of life in these groups. Translational Relevance Visual search is impaired in glaucoma and dry eye groups compared to controls, highlighting the need for compensatory strategies and tools to assist individuals in overcoming their deficiencies. PMID:28670502

Evidence-based librarianship: searching for the needed EBL evidence.

Science.gov (United States)

Eldredge, J D

2000-01-01

This paper discusses the challenges of finding evidence needed to implement Evidence-Based Librarianship (EBL). Focusing first on database coverage for three health sciences librarianship journals, the article examines the information contents of different databases. Strategies are needed to search for relevant evidence in the library literature via these databases, and the problems associated with searching the grey literature of librarianship. Database coverage, plausible search strategies, and the grey literature of library science all pose challenges to finding the needed research evidence for practicing EBL. Health sciences librarians need to ensure that systems are designed that can track and provide access to needed research evidence to support Evidence-Based Librarianship (EBL).

Robust object tacking based on self-adaptive search area

Science.gov (United States)

Dong, Taihang; Zhong, Sheng

2018-02-01

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in the unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.

Biclustering of gene expression data using reactive greedy randomized adaptive search procedure.

Science.gov (United States)

Dharan, Smitha; Nair, Achuthsankar S

2009-01-30

Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously. We performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach. The Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.

Knowledge base, information search and intention to adopt innovation

NARCIS (Netherlands)

Rijnsoever, van F.J.; Castaldi, C.

2008-01-01

Innovation is a process that involves searching for new information. This paper builds upon theoretical insights on individual and organizational learning and proposes a knowledge based model of how actors search for information when confronted with innovation. The model takes into account different

Development and Evaluation of Thesauri-Based Bibliographic Biomedical Search Engine

Science.gov (United States)

Alghoson, Abdullah

2017-01-01

Due to the large volume and exponential growth of biomedical documents (e.g., books, journal articles), it has become increasingly challenging for biomedical search engines to retrieve relevant documents based on users' search queries. Part of the challenge is the matching mechanism of free-text indexing that performs matching based on…

Solving the dial-a-ride problem using agent-based simulation

Directory of Open Access Journals (Sweden)

Campbell, Ian

2016-11-01

Full Text Available The ‘dial-a-ride problem’ (DARP requires a set of customers to be transported by a limited fleet of vehicles between unique origins and destinations under several service constraints, including within defined time windows. The problem is considered NP-hard, and has typically been solved using metaheuristic methods. An agent-based simulation (ABS model was developed, where each vehicle bids to service customers based on a weighted objective function that considers the cost to service the customer and the time quality of the service that would be achieved. The approach applied a pre- processing technique to reduce the search space, given the service time window constraints. Tests of the model showed significantly better customer transit and waiting times than the benchmark datasets. The ABS was able to obtain solutions for much larger problem sizes than the benchmark solutions, with this work being the first known application of ABS to the DARP.

SciRide Finder: a citation-based paradigm in biomedical literature search.

Science.gov (United States)

Volanakis, Adam; Krawczyk, Konrad

2018-04-18

There are more than 26 million peer-reviewed biomedical research items according to Medline/PubMed. This breadth of information is indicative of the progress in biomedical sciences on one hand, but an overload for scientists performing literature searches on the other. A major portion of scientific literature search is to find statements, numbers and protocols that can be cited to build an evidence-based narrative for a new manuscript. Because science builds on prior knowledge, such information has likely been written out and cited in an older manuscript. Thus, Cited Statements, pieces of text from scientific literature supported by citing other peer-reviewed publications, carry significant amount of condensed information on prior art. Based on this principle, we propose a literature search service, SciRide Finder (finder.sciride.org), which constrains the search corpus to such Cited Statements only. We demonstrate that Cited Statements can carry different information to this found in titles/abstracts and full text, giving access to alternative literature search results than traditional search engines. We further show how presenting search results as a list of Cited Statements allows researchers to easily find information to build an evidence-based narrative for their own manuscripts.

Object-based target templates guide attention during visual search.

Science.gov (United States)

Berggren, Nick; Eimer, Martin

2018-05-03

During visual search, attention is believed to be controlled in a strictly feature-based fashion, without any guidance by object-based target representations. To challenge this received view, we measured electrophysiological markers of attentional selection (N2pc component) and working memory (sustained posterior contralateral negativity; SPCN) in search tasks where two possible targets were defined by feature conjunctions (e.g., blue circles and green squares). Critically, some search displays also contained nontargets with two target features (incorrect conjunction objects, e.g., blue squares). Because feature-based guidance cannot distinguish these objects from targets, any selective bias for targets will reflect object-based attentional control. In Experiment 1, where search displays always contained only one object with target-matching features, targets and incorrect conjunction objects elicited identical N2pc and SPCN components, demonstrating that attentional guidance was entirely feature-based. In Experiment 2, where targets and incorrect conjunction objects could appear in the same display, clear evidence for object-based attentional control was found. The target N2pc became larger than the N2pc to incorrect conjunction objects from 250 ms poststimulus, and only targets elicited SPCN components. This demonstrates that after an initial feature-based guidance phase, object-based templates are activated when they are required to distinguish target and nontarget objects. These templates modulate visual processing and control access to working memory, and their activation may coincide with the start of feature integration processes. Results also suggest that while multiple feature templates can be activated concurrently, only a single object-based target template can guide attention at any given time. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Teaching AI Search Algorithms in a Web-Based Educational System

Science.gov (United States)

Grivokostopoulou, Foteini; Hatzilygeroudis, Ioannis

2013-01-01

In this paper, we present a way of teaching AI search algorithms in a web-based adaptive educational system. Teaching is based on interactive examples and exercises. Interactive examples, which use visualized animations to present AI search algorithms in a step-by-step way with explanations, are used to make learning more attractive. Practice…

The Time-Dependent Multiple-Vehicle Prize-Collecting Arc Routing Problem

DEFF Research Database (Denmark)

Black, Daniel; Eglese, Richard; Wøhlk, Sanne

2015-01-01

-life traffic situations where the travel times change with the time of day are taken into account. Two metaheuristic algorithms, one based on Variable Neighborhood Search and one based on Tabu Search, are proposed and tested for a set of benchmark problems, generated from real road networks and travel time......In this paper, we introduce a multi vehicle version of the Time-Dependent Prize-Collecting Arc Routing Problem (TD-MPARP). It is inspired by a situation where a transport manager has to choose between a number of full truck load pick-ups and deliveries to be performed by a fleet of vehicles. Real...

The time-dependent prize-collecting arc routing problem

DEFF Research Database (Denmark)

Black, Dan; Eglese, Richard; Wøhlk, Sanne

2013-01-01

with the time of day. Two metaheuristic algorithms, one based on Variable Neighborhood Search and one based on Tabu Search, are proposed and tested for a set of benchmark problems, generated from real road networks and travel time information. Both algorithms are capable of finding good solutions, though......A new problem is introduced named the Time-Dependent Prize-Collecting Arc Routing Problem (TD-PARP). It is particularly relevant to situations where a transport manager has to choose between a number of full truck load pick-ups and deliveries on a road network where travel times change...

GeNemo: a search engine for web-based functional genomic data.

Science.gov (United States)

Zhang, Yongqing; Cao, Xiaoyi; Zhong, Sheng

2016-07-08

A set of new data types emerged from functional genomic assays, including ChIP-seq, DNase-seq, FAIRE-seq and others. The results are typically stored as genome-wide intensities (WIG/bigWig files) or functional genomic regions (peak/BED files). These data types present new challenges to big data science. Here, we present GeNemo, a web-based search engine for functional genomic data. GeNemo searches user-input data against online functional genomic datasets, including the entire collection of ENCODE and mouse ENCODE datasets. Unlike text-based search engines, GeNemo's searches are based on pattern matching of functional genomic regions. This distinguishes GeNemo from text or DNA sequence searches. The user can input any complete or partial functional genomic dataset, for example, a binding intensity file (bigWig) or a peak file. GeNemo reports any genomic regions, ranging from hundred bases to hundred thousand bases, from any of the online ENCODE datasets that share similar functional (binding, modification, accessibility) patterns. This is enabled by a Markov Chain Monte Carlo-based maximization process, executed on up to 24 parallel computing threads. By clicking on a search result, the user can visually compare her/his data with the found datasets and navigate the identified genomic regions. GeNemo is available at www.genemo.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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.

Q-learning-based adjustable fixed-phase quantum Grover search algorithm

International Nuclear Information System (INIS)

Guo Ying; Shi Wensha; Wang Yijun; Hu, Jiankun

2017-01-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one. (author)

Search-based model identification of smart-structure damage

Science.gov (United States)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

Parallel content-based sub-image retrieval using hierarchical searching.

Science.gov (United States)

Yang, Lin; Qi, Xin; Xing, Fuyong; Kurc, Tahsin; Saltz, Joel; Foran, David J

2014-04-01

The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.

A new distributed systems scheduling algorithm: a swarm intelligence approach

Science.gov (United States)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

AN OVERVIEW OF SEARCHING AND DISCOVERING WEB BASED INFORMATION RESOURCES

Directory of Open Access Journals (Sweden)

Cezar VASILESCU

2010-01-01

Full Text Available The Internet becomes for most of us a daily used instrument, for professional or personal reasons. We even do not remember the times when a computer and a broadband connection were luxury items. More and more people are relying on the complicated web network to find the needed information.This paper presents an overview of Internet search related issues, upon search engines and describes the parties and the basic mechanism that is embedded in a search for web based information resources. Also presents ways to increase the efficiency of web searches, through a better understanding of what search engines ignore at websites content.

A GIS-based Quantitative Approach for the Search of Clandestine Graves, Italy.

Science.gov (United States)

Somma, Roberta; Cascio, Maria; Silvestro, Massimiliano; Torre, Eliana

2018-05-01

Previous research on the RAG color-coded prioritization systems for the discovery of clandestine graves has not considered all the factors influencing the burial site choice within a GIS project. The goal of this technical note was to discuss a GIS-based quantitative approach for the search of clandestine graves. The method is based on cross-referenced RAG maps with cumulative suitability factors to host a burial, leading to the editing of different search scenarios for ground searches showing high-(Red), medium-(Amber), and low-(Green) priority areas. The application of this procedure allowed several outcomes to be determined: If the concealment occurs at night, then the "search scenario without the visibility" will be the most effective one; if the concealment occurs in daylight, then the "search scenario with the DSM-based visibility" will be most appropriate; the different search scenarios may be cross-referenced with offender's confessions and eyewitnesses' testimonies to verify the veracity of their statements. © 2017 American Academy of Forensic Sciences.

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

Science.gov (United States)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Search for brown dwarfs in the IRAS data bases

International Nuclear Information System (INIS)

Low, F.J.

1986-01-01

A report is given on the initial searches for brown dwarf stars in the IRAS data bases. The paper was presented to the workshop on 'Astrophysics of brown dwarfs', Virginia, USA, 1985. To date no brown dwarfs have been discovered in the solar neighbourhood. Opportunities for future searches with greater sensitivity and different wavelengths are outlined. (U.K.)

Ant colony optimization and constraint programming

CERN Document Server

Solnon, Christine

2013-01-01

Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search

Pathfinder: multiresolution region-based searching of pathology images using IRM.

OpenAIRE

Wang, J. Z.

2000-01-01

The fast growth of digitized pathology slides has created great challenges in research on image database retrieval. The prevalent retrieval technique involves human-supplied text annotations to describe slide contents. These pathology images typically have very high resolution, making it difficult to search based on image content. In this paper, we present Pathfinder, an efficient multiresolution region-based searching system for high-resolution pathology image libraries. The system uses wave...

Use of Tabu Search in a Solver to Map Complex Networks onto Emulab Testbeds

National Research Council Canada - National Science Library

MacDonald, Jason E

2007-01-01

The University of Utah's solver for the testbed mapping problem uses a simulated annealing metaheuristic algorithm to map a researcher's experimental network topology onto available testbed resources...

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.

Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms.

Science.gov (United States)

Razavi Termeh, Seyed Vahid; Kornejady, Aiding; Pourghasemi, Hamid Reza; Keesstra, Saskia

2018-02-15

Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the

MVMO-based approach for optimal placement and tuning of ...

African Journals Online (AJOL)

DR OKE

differential evolution DE algorithm with adaptive crossover operator, .... x are assigned by using a sequential scheme which accounts for mean and ... the representative scenarios from probabilistic model based Monte Carlo ... Comparison of average convergence of MVMO-S with other metaheuristic optimization methods.

Quantum signature scheme based on a quantum search algorithm

International Nuclear Information System (INIS)

Yoon, Chun Seok; Kang, Min Sung; Lim, Jong In; Yang, Hyung Jin

2015-01-01

We present a quantum signature scheme based on a two-qubit quantum search algorithm. For secure transmission of signatures, we use a quantum search algorithm that has not been used in previous quantum signature schemes. A two-step protocol secures the quantum channel, and a trusted center guarantees non-repudiation that is similar to other quantum signature schemes. We discuss the security of our protocol. (paper)

Novel citation-based search method for scientific literature: application to meta-analyses

NARCIS (Netherlands)

Janssens, A.C.J.W.; Gwinn, M.

2015-01-01

Background: Finding eligible studies for meta-analysis and systematic reviews relies on keyword-based searching as the gold standard, despite its inefficiency. Searching based on direct citations is not sufficiently comprehensive. We propose a novel strategy that ranks articles on their degree of

Order Tracking Based on Robust Peak Search Instantaneous Frequency Estimation

International Nuclear Information System (INIS)

Gao, Y; Guo, Y; Chi, Y L; Qin, S R

2006-01-01

Order tracking plays an important role in non-stationary vibration analysis of rotating machinery, especially to run-up or coast down. An instantaneous frequency estimation (IFE) based order tracking of rotating machinery is introduced. In which, a peak search algorithms of spectrogram of time-frequency analysis is employed to obtain IFE of vibrations. An improvement to peak search is proposed, which can avoid strong non-order components or noises disturbing to the peak search work. Compared with traditional methods of order tracking, IFE based order tracking is simplified in application and only software depended. Testing testify the validity of the method. This method is an effective supplement to traditional methods, and the application in condition monitoring and diagnosis of rotating machinery is imaginable

Visibiome: an efficient microbiome search engine based on a scalable, distributed architecture.

Science.gov (United States)

Azman, Syafiq Kamarul; Anwar, Muhammad Zohaib; Henschel, Andreas

2017-07-24

Given the current influx of 16S rRNA profiles of microbiota samples, it is conceivable that large amounts of them eventually are available for search, comparison and contextualization with respect to novel samples. This process facilitates the identification of similar compositional features in microbiota elsewhere and therefore can help to understand driving factors for microbial community assembly. We present Visibiome, a microbiome search engine that can perform exhaustive, phylogeny based similarity search and contextualization of user-provided samples against a comprehensive dataset of 16S rRNA profiles environments, while tackling several computational challenges. In order to scale to high demands, we developed a distributed system that combines web framework technology, task queueing and scheduling, cloud computing and a dedicated database server. To further ensure speed and efficiency, we have deployed Nearest Neighbor search algorithms, capable of sublinear searches in high-dimensional metric spaces in combination with an optimized Earth Mover Distance based implementation of weighted UniFrac. The search also incorporates pairwise (adaptive) rarefaction and optionally, 16S rRNA copy number correction. The result of a query microbiome sample is the contextualization against a comprehensive database of microbiome samples from a diverse range of environments, visualized through a rich set of interactive figures and diagrams, including barchart-based compositional comparisons and ranking of the closest matches in the database. Visibiome is a convenient, scalable and efficient framework to search microbiomes against a comprehensive database of environmental samples. The search engine leverages a popular but computationally expensive, phylogeny based distance metric, while providing numerous advantages over the current state of the art tool.

Update on CERN Search based on SharePoint 2013

Science.gov (United States)

Alvarez, E.; Fernandez, S.; Lossent, A.; Posada, I.; Silva, B.; Wagner, A.

2017-10-01

CERN’s enterprise Search solution “CERN Search” provides a central search solution for users and CERN service providers. A total of about 20 million public and protected documents from a wide range of document collections is indexed, including Indico, TWiki, Drupal, SharePoint, JACOW, E-group archives, EDMS, and CERN Web pages. In spring 2015, CERN Search was migrated to a new infrastructure based on SharePoint 2013. In the context of this upgrade, the document pre-processing and indexing process was redesigned and generalised. The new data feeding framework allows to profit from new functionality and it facilitates the long term maintenance of the system.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

Science.gov (United States)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this

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.

Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm

Directory of Open Access Journals (Sweden)

T.P. Shabeera

2017-04-01

Full Text Available Nowadays data-intensive applications for processing big data are being hosted in the cloud. Since the cloud environment provides virtualized resources for computation, and data-intensive applications require communication between the computing nodes, the placement of Virtual Machines (VMs and location of data affect the overall computation time. Majority of the research work reported in the current literature consider the selection of physical nodes for placing data and VMs as independent problems. This paper proposes an approach which considers VM placement and data placement hand in hand. The primary objective is to reduce cross network traffic and bandwidth usage, by placing required number of VMs and data in Physical Machines (PMs which are physically closer. The VM and data placement problem (referred as MinDistVMDataPlacement problem is defined in this paper and has been proved to be NP- Hard. This paper presents and evaluates a metaheuristic algorithm based on Ant Colony Optimization (ACO, which selects a set of adjacent PMs for placing data and VMs. Data is distributed in the physical storage devices of the selected PMs. According to the processing capacity of each PM, a set of VMs are placed on these PMs to process data stored in them. We use simulation to evaluate our algorithm. The results show that the proposed algorithm selects PMs in close proximity and the jobs executed in the VMs allocated by the proposed scheme outperforms other allocation schemes.

Search Method Based on Figurative Indexation of Folksonomic Features of Graphic Files

Directory of Open Access Journals (Sweden)

Oleg V. Bisikalo

2013-11-01

Full Text Available In this paper the search method based on usage of figurative indexation of folksonomic characteristics of graphical files is described. The method takes into account extralinguistic information, is based on using a model of figurative thinking of humans. The paper displays the creation of a method of searching image files based on their formal, including folksonomical clues.

Clinician search behaviors may be influenced by search engine design.

Science.gov (United States)

Lau, Annie Y S; Coiera, Enrico; Zrimec, Tatjana; Compton, Paul

2010-06-30

Searching the Web for documents using information retrieval systems plays an important part in clinicians' practice of evidence-based medicine. While much research focuses on the design of methods to retrieve documents, there has been little examination of the way different search engine capabilities influence clinician search behaviors. Previous studies have shown that use of task-based search engines allows for faster searches with no loss of decision accuracy compared with resource-based engines. We hypothesized that changes in search behaviors may explain these differences. In all, 75 clinicians (44 doctors and 31 clinical nurse consultants) were randomized to use either a resource-based or a task-based version of a clinical information retrieval system to answer questions about 8 clinical scenarios in a controlled setting in a university computer laboratory. Clinicians using the resource-based system could select 1 of 6 resources, such as PubMed; clinicians using the task-based system could select 1 of 6 clinical tasks, such as diagnosis. Clinicians in both systems could reformulate search queries. System logs unobtrusively capturing clinicians' interactions with the systems were coded and analyzed for clinicians' search actions and query reformulation strategies. The most frequent search action of clinicians using the resource-based system was to explore a new resource with the same query, that is, these clinicians exhibited a "breadth-first" search behaviour. Of 1398 search actions, clinicians using the resource-based system conducted 401 (28.7%, 95% confidence interval [CI] 26.37-31.11) in this way. In contrast, the majority of clinicians using the task-based system exhibited a "depth-first" search behavior in which they reformulated query keywords while keeping to the same task profiles. Of 585 search actions conducted by clinicians using the task-based system, 379 (64.8%, 95% CI 60.83-68.55) were conducted in this way. This study provides evidence that

A New Metaheuristic Algorithm for Long-Term Open-Pit Production Planning / Nowy meta-heurystyczny algorytm wspomagający długoterminowe planowanie produkcji w kopalni odkrywkowej

Science.gov (United States)

Sattarvand, Javad; Niemann-Delius, Christian

2013-03-01

Paper describes a new metaheuristic algorithm which has been developed based on the Ant Colony Optimisation (ACO) and its efficiency have been discussed. To apply the ACO process on mine planning problem, a series of variables are considered for each block as the pheromone trails that represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. During implementation several mine schedules are constructed in each iteration. Then the pheromone values of all blocks are reduced to a certain percentage and additionally the pheromone value of those blocks that are used in defining the constructed schedules are increased according to the quality of the generated solutions. By repeated iterations, the pheromone values of those blocks that define the shape of the optimum solution are increased whereas those of the others have been significantly evaporated.

Meta-heuristic CRPS minimization for the calibration of short-range probabilistic forecasts

Science.gov (United States)

Mohammadi, Seyedeh Atefeh; Rahmani, Morteza; Azadi, Majid

2016-08-01

This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098-1118, 2005), Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm optimization (PSO), a robust meta-heuristic method, to minimize the CRPS. The ensemble prediction system used in this study consists of nine different configurations of the weather research and forecasting model for 48-h forecasts of temperature during autumn and winter 2011 and 2012. The probabilistic forecasts were evaluated using several common verification scores including Brier score, attribute diagram and rank histogram. Results show that both BFGS and PSO find the optimal solution and show the same evaluation scores, but PSO can do this with a feasible random first guess and much less computational complexity.

A Full-Text-Based Search Engine for Finding Highly Matched Documents Across Multiple Categories

Science.gov (United States)

Nguyen, Hung D.; Steele, Gynelle C.

2016-01-01

This report demonstrates the full-text-based search engine that works on any Web-based mobile application. The engine has the capability to search databases across multiple categories based on a user's queries and identify the most relevant or similar. The search results presented here were found using an Android (Google Co.) mobile device; however, it is also compatible with other mobile phones.

A product feature-based user-centric product search model

OpenAIRE

Ben Jabeur, Lamjed; Soulier, Laure; Tamine, Lynda; Mousset, Paul

2016-01-01

During the online shopping process, users would search for interesting products and quickly access those that fit with their needs among a long tail of similar or closely related products. Our contribution addresses head queries that are frequently submitted on e-commerce Web sites. Head queries usually target featured products with several variations, accessories, and complementary products. We present in this paper a product feature-based user-centric model for product search involving in a...

Architecture for knowledge-based and federated search of online clinical evidence.

Science.gov (United States)

Coiera, Enrico; Walther, Martin; Nguyen, Ken; Lovell, Nigel H

2005-10-24

It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings. The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements. A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources. Clinicians performed 1662 searches over the trial. The average search duration was 4.9 +/- 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s. The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the

Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm

Directory of Open Access Journals (Sweden)

Hamid Taghavifar

2014-08-01

Full Text Available The novel imperialist competitive algorithm (ICA has presented outstanding fitness on various optimization problems. Application of meta-heuristics has been a dynamic studying interest of the reliability optimization to determine idleness and reliability constituents. The application of a meta-heuristic evolutionary optimization method, imperialist competitive algorithm (ICA, for minimization of energy loss due to wheel rolling resistance in a soil bin facility equipped with single-wheel tester is discussed. The required data were collected thorough various designed experiments in the controlled soil bin environment. Local and global searching of the search space proposed that the energy loss could be reduced to the minimum amount of 15.46 J at the optimized input variable configuration of wheel load at 1.2 kN, tire inflation pressure of 296 kPa and velocity of 2 m/s. Meanwhile, genetic algorithm (GA, particle swarm optimization (PSO and hybridized GA–PSO approaches were benchmarked among the broad spectrum of meta-heuristics to find the outperforming approach. It was deduced that, on account of the obtained results, ICA can achieve optimum configuration with superior accuracy in less required computational time.

Proceedings of the ECIR 2012 Workshop on Task-Based and Aggregated Search (TBAS2012)

DEFF Research Database (Denmark)

2012-01-01

Task-based search aims to understand the user's current task and desired outcomes, and how this may provide useful context for the Information Retrieval (IR) process. An example of task-based search is situations where additional user information on e.g. the purpose of the search or what the user...

Cuckoo Search Algorithm with Lévy Flights for Global-Support Parametric Surface Approximation in Reverse Engineering

Directory of Open Access Journals (Sweden)

Andrés Iglesias

2018-03-01

Full Text Available This paper concerns several important topics of the Symmetry journal, namely, computer-aided design, computational geometry, computer graphics, visualization, and pattern recognition. We also take advantage of the symmetric structure of the tensor-product surfaces, where the parametric variables u and v play a symmetric role in shape reconstruction. In this paper we address the general problem of global-support parametric surface approximation from clouds of data points for reverse engineering applications. Given a set of measured data points, the approximation is formulated as a nonlinear continuous least-squares optimization problem. Then, a recent metaheuristics called Cuckoo Search Algorithm (CSA is applied to compute all relevant free variables of this minimization problem (namely, the data parameters and the surface poles. The method includes the iterative generation of new solutions by using the Lévy flights to promote the diversity of solutions and prevent stagnation. A critical advantage of this method is its simplicity: the CSA requires only two parameters, many fewer than any other metaheuristic approach, so the parameter tuning becomes a very easy task. The method is also simple to understand and easy to implement. Our approach has been applied to a benchmark of three illustrative sets of noisy data points corresponding to surfaces exhibiting several challenging features. Our experimental results show that the method performs very well even for the cases of noisy and unorganized data points. Therefore, the method can be directly used for real-world applications for reverse engineering without further pre/post-processing. Comparative work with the most classical mathematical techniques for this problem as well as a recent modification of the CSA called Improved CSA (ICSA is also reported. Two nonparametric statistical tests show that our method outperforms the classical mathematical techniques and provides equivalent results to ICSA

Better Drumming Through Calibration: Techniques for Pre-Performance Robotic Percussion Optimization

OpenAIRE

Murphy, Jim; Kapur, Ajay; Carnegie, Dale

2012-01-01

A problem with many contemporary musical robotic percussion systems lies in the fact that solenoids fail to respond lin-early to linear increases in input velocity. This nonlinearity forces performers to individually tailor their compositions to specific robotic drummers. To address this problem, we introduce a method of pre-performance calibration using metaheuristic search techniques. A variety of such techniques are introduced and evaluated and the results of the optimized solenoid-based p...

Global polar geospatial information service retrieval based on search engine and ontology reasoning

Science.gov (United States)

Chen, Nengcheng; E, Dongcheng; Di, Liping; Gong, Jianya; Chen, Zeqiang

2007-01-01

In order to improve the access precision of polar geospatial information service on web, a new methodology for retrieving global spatial information services based on geospatial service search and ontology reasoning is proposed, the geospatial service search is implemented to find the coarse service from web, the ontology reasoning is designed to find the refined service from the coarse service. The proposed framework includes standardized distributed geospatial web services, a geospatial service search engine, an extended UDDI registry, and a multi-protocol geospatial information service client. Some key technologies addressed include service discovery based on search engine and service ontology modeling and reasoning in the Antarctic geospatial context. Finally, an Antarctica multi protocol OWS portal prototype based on the proposed methodology is introduced.

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification.

Science.gov (United States)

Wang, Yi; Wan, Jianwu; Guo, Jun; Cheung, Yiu-Ming; C Yuen, Pong

2017-07-14

Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives.

A hybrid neural network – world cup optimization algorithm for melanoma detection

Directory of Open Access Journals (Sweden)

Razmjooy Navid

2018-03-01

Full Text Available One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN. World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

A meta-heuristic cuckoo search and eigen permutation approach for model order ... The proposed approach preserves the stability of the original system into the lower ... AKHILESH KUMAR GUPTA1 DEEPAK KUMAR1 PAULSON SAMUEL1.

SearchResultFinder: federated search made easy

NARCIS (Netherlands)

Trieschnigg, Rudolf Berend; Tjin-Kam-Jet, Kien; Hiemstra, Djoerd

Building a federated search engine based on a large number existing web search engines is a challenge: implementing the programming interface (API) for each search engine is an exacting and time-consuming job. In this demonstration we present SearchResultFinder, a browser plugin which speeds up

Computer-Assisted Search Of Large Textual Data Bases

Science.gov (United States)

Driscoll, James R.

1995-01-01

"QA" denotes high-speed computer system for searching diverse collections of documents including (but not limited to) technical reference manuals, legal documents, medical documents, news releases, and patents. Incorporates previously available and emerging information-retrieval technology to help user intelligently and rapidly locate information found in large textual data bases. Technology includes provision for inquiries in natural language; statistical ranking of retrieved information; artificial-intelligence implementation of semantics, in which "surface level" knowledge found in text used to improve ranking of retrieved information; and relevance feedback, in which user's judgements of relevance of some retrieved documents used automatically to modify search for further information.

OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

Science.gov (United States)

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Ramzan, Naeem

2017-01-01

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search (OS2) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, OS2 ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables OS2 to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of OS2 is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations. PMID:28692697

Personalizing Web Search based on User Profile

OpenAIRE

Utage, Sharyu; Ahire, Vijaya

2016-01-01

Web Search engine is most widely used for information retrieval from World Wide Web. These Web Search engines help user to find most useful information. When different users Searches for same information, search engine provide same result without understanding who is submitted that query. Personalized web search it is search technique for proving useful result. This paper models preference of users as hierarchical user profiles. a framework is proposed called UPS. It generalizes profile and m...

Noesis: Ontology based Scoped Search Engine and Resource Aggregator for Atmospheric Science

Science.gov (United States)

Ramachandran, R.; Movva, S.; Li, X.; Cherukuri, P.; Graves, S.

2006-12-01

The goal for search engines is to return results that are both accurate and complete. The search engines should find only what you really want and find everything you really want. Search engines (even meta search engines) lack semantics. The basis for search is simply based on string matching between the user's query term and the resource database and the semantics associated with the search string is not captured. For example, if an atmospheric scientist is searching for "pressure" related web resources, most search engines return inaccurate results such as web resources related to blood pressure. In this presentation Noesis, which is a meta-search engine and a resource aggregator that uses domain ontologies to provide scoped search capabilities will be described. Noesis uses domain ontologies to help the user scope the search query to ensure that the search results are both accurate and complete. The domain ontologies guide the user to refine their search query and thereby reduce the user's burden of experimenting with different search strings. Semantics are captured by refining the query terms to cover synonyms, specializations, generalizations and related concepts. Noesis also serves as a resource aggregator. It categorizes the search results from different online resources such as education materials, publications, datasets, web search engines that might be of interest to the user.

The role of space and time in object-based visual search

NARCIS (Netherlands)

Schreij, D.B.B.; Olivers, C.N.L.

2013-01-01

Recently we have provided evidence that observers more readily select a target from a visual search display if the motion trajectory of the display object suggests that the observer has dealt with it before. Here we test the prediction that this object-based memory effect on search breaks down if

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

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Christoff Fourie

2014-11-01

Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

Application of Meta-Heuristic Hybrid Artificial Intelligence Techniques for Modeling of Bonding Strength of Plywood Panels

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Cenk Demirkır

2014-04-01

Full Text Available Plywood, which is one of the most important wood based panels, has many usage areas changing from traffic signs to building constructions in many countries. It is known that the high quality plywood panel manufacturing has been achieved with a good bonding under the optimum pressure conditions depending on adhesive type. This is a study of determining the using possibilities of modern meta-heuristic hybrid artificial intelligence techniques such as IKE and AANN methods for prediction of bonding strength of plywood panels. This study has composed of two main parts as experimental and analytical. Scots pine, maritime pine and European black pine logs were used as wood species. The pine veneers peeled at 32°C and 50°C were dried at 110°C, 140°C and 160°C temperatures. Phenol formaldehyde and melamine urea formaldehyde resins were used as adhesive types. EN 314-1 standard was used to determine the bonding shear strength values of plywood panels in experimental part of this study. Then the intuitive k-nearest neighbor estimator (IKE and adaptive artificial neural network (AANN were used to estimate bonding strength of plywood panels. The best estimation performance was obtained from MA metric for k-value=10. The most effective factor on bonding strength was determined as adhesive type. Error rates were determined less than 5% for both of the IKE and AANN. It may be recommended that proposed methods could be used in applying to estimation of bonding strength values of plywood panels.

A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks

Science.gov (United States)

De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio

2016-05-01

This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.

Heuristic algorithms for joint optimization of unicast and anycast traffic in elastic optical network–based large–scale computing systems

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Markowski Marcin

2017-09-01

Full Text Available In recent years elastic optical networks have been perceived as a prospective choice for future optical networks due to better adjustment and utilization of optical resources than is the case with traditional wavelength division multiplexing networks. In the paper we investigate the elastic architecture as the communication network for distributed data centers. We address the problems of optimization of routing and spectrum assignment for large-scale computing systems based on an elastic optical architecture; particularly, we concentrate on anycast user to data center traffic optimization. We assume that computational resources of data centers are limited. For this offline problems we formulate the integer linear programming model and propose a few heuristics, including a meta-heuristic algorithm based on a tabu search method. We report computational results, presenting the quality of approximate solutions and efficiency of the proposed heuristics, and we also analyze and compare some data center allocation scenarios.

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

Science.gov (United States)

Sur, Chiranjib; Shukla, Anupam

2018-03-01

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

Differential Search Coils Based Magnetometers: Conditioning, Magnetic Sensitivity, Spatial Resolution

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Timofeeva Maria

2012-03-01

Full Text Available A theoretical and experimental comparison of optimized search coils based magnetometers, operating either in the Flux mode or in the classical Lenz-Faraday mode, is presented. The improvements provided by the Flux mode in terms of bandwidth and measuring range of the sensor are detailed. Theory, SPICE model and measurements are in good agreement. The spatial resolution of the sensor is studied which is an important parameter for applications in non destructive evaluation. A general expression of the magnetic sensitivity of search coils sensors is derived. Solutions are proposed to design magnetometers with reduced weight and volume without degrading the magnetic sensitivity. An original differential search coil based magnetometer, made of coupled coils, operating in flux mode and connected to a differential transimpedance amplifier is proposed. It is shown that this structure is better in terms of volume occupancy than magnetometers using two separated coils without any degradation in magnetic sensitivity. Experimental results are in good agreement with calculations.

Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning

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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.

ONTOLOGY BASED MEANINGFUL SEARCH USING SEMANTIC WEB AND NATURAL LANGUAGE PROCESSING TECHNIQUES

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K. Palaniammal

2013-10-01

Full Text Available The semantic web extends the current World Wide Web by adding facilities for the machine understood description of meaning. The ontology based search model is used to enhance efficiency and accuracy of information retrieval. Ontology is the core technology for the semantic web and this mechanism for representing formal and shared domain descriptions. In this paper, we proposed ontology based meaningful search using semantic web and Natural Language Processing (NLP techniques in the educational domain. First we build the educational ontology then we present the semantic search system. The search model consisting three parts which are embedding spell-check, finding synonyms using WordNet API and querying ontology using SPARQL language. The results are both sensitive to spell check and synonymous context. This paper provides more accurate results and the complete details for the selected field in a single page.

Meta-Heuristic Cuckoo Search Algorithm for the Correction of Faulty Array Antenna

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Shafqatullah Khan

2015-10-01

Full Text Available When the distribution system is disconnected from the transmission system, the islanded portion of the network comprising DG (Distributed Generation units forms a MG (Micro Grid. It is essential either to shut down the DG units or ensure the stable and the controlled operation of the islanded MG. The frequency and the voltage of the islanded MG vary when it is isolated from the main transmission grid. The voltage and the frequency of the islanded MG can be controlled to the permissible limits by providing the required amount of the active and reactive power by the local available sources in the MG. The main focus of this paper is about the control of the network frequency in the islanded MG by employing PI controllers based STATCOM (Static Compensator and BESS-STATCOM (Battery Energy Storage System Equipped devices. The study is done by using DIgSILENT power factory software version 15.0

A semantics-based method for clustering of Chinese web search results

Science.gov (United States)

Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong

2014-01-01

Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.

XSemantic: An Extension of LCA Based XML Semantic Search

Science.gov (United States)

Supasitthimethee, Umaporn; Shimizu, Toshiyuki; Yoshikawa, Masatoshi; Porkaew, Kriengkrai

One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.

Extended-Search, Bézier Curve-Based Lane Detection and Reconstruction System for an Intelligent Vehicle

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Xiaoyun Huang

2015-09-01

Full Text Available To improve the real-time performance and detection rate of a Lane Detection and Reconstruction (LDR system, an extended-search-based lane detection method and a Bézier curve-based lane reconstruction algorithm are proposed in this paper. The extended-search-based lane detection method is designed to search boundary blocks from the initial position, in an upwards direction and along the lane, with small search areas including continuous search, discontinuous search and bending search in order to detect different lane boundaries. The Bézier curve-based lane reconstruction algorithm is employed to describe a wide range of lane boundary forms with comparatively simple expressions. In addition, two Bézier curves are adopted to reconstruct the lanes' outer boundaries with large curvature variation. The lane detection and reconstruction algorithm — including initial-blocks' determining, extended search, binarization processing and lane boundaries' fitting in different scenarios — is verified in road tests. The results show that this algorithm is robust against different shadows and illumination variations; the average processing time per frame is 13 ms. Significantly, it presents an 88.6% high-detection rate on curved lanes with large or variable curvatures, where the accident rate is higher than that of straight lanes.

Mathematical and Metaheuristic Applications in Design Optimization of Steel Frame Structures: An Extensive Review

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Mehmet Polat Saka

2013-01-01

Full Text Available The type of mathematical modeling selected for the optimum design problems of steel skeletal frames affects the size and mathematical complexity of the programming problem obtained. Survey on the structural optimization literature reveals that there are basically two types of design optimization formulation. In the first type only cross sectional properties of frame members are taken as design variables. In such formulation when the values of design variables change during design cycles, it becomes necessary to analyze the structure and update the response of steel frame to the external loading. Structural analysis in this type is a complementary part of the design process. In the second type joint coordinates are also treated as design variables in addition to the cross sectional properties of members. Such formulation eliminates the necessity of carrying out structural analysis in every design cycle. The values of the joint displacements are determined by the optimization techniques in addition to cross sectional properties. The structural optimization literature contains structural design algorithms that make use of both type of formulation. In this study a review is carried out on mathematical and metaheuristic algorithms where the effect of the mathematical modeling on the efficiency of these algorithms is discussed.

Tag-Based Social Image Search: Toward Relevant and Diverse Results

Science.gov (United States)

Yang, Kuiyuan; Wang, Meng; Hua, Xian-Sheng; Zhang, Hong-Jiang

Recent years have witnessed a great success of social media websites. Tag-based image search is an important approach to access the image content of interest on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or lack of diversity. This chapter presents a diverse relevance ranking scheme which simultaneously takes relevance and diversity into account by exploring the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both visual information of images and semantic information of associated tags. Then semantic similarities of social images are estimated based on their tags. Based on the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes Average Diverse Precision (ADP), a novel measure that is extended from the conventional Average Precision (AP). Comprehensive experiments and user studies demonstrate the effectiveness of the approach.

Snippet-based relevance predictions for federated web search

NARCIS (Netherlands)

Demeester, Thomas; Nguyen, Dong-Phuong; Trieschnigg, Rudolf Berend; Develder, Chris; Hiemstra, Djoerd

How well can the relevance of a page be predicted, purely based on snippets? This would be highly useful in a Federated Web Search setting where caching large amounts of result snippets is more feasible than caching entire pages. The experiments reported in this paper make use of result snippets and

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

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Ruey-Maw Chen

2011-01-01

Full Text Available The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

Composition-based statistics and translated nucleotide searches: Improving the TBLASTN module of BLAST

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Schäffer Alejandro A

2006-12-01

Full Text Available Abstract Background TBLASTN is a mode of operation for BLAST that aligns protein sequences to a nucleotide database translated in all six frames. We present the first description of the modern implementation of TBLASTN, focusing on new techniques that were used to implement composition-based statistics for translated nucleotide searches. Composition-based statistics use the composition of the sequences being aligned to generate more accurate E-values, which allows for a more accurate distinction between true and false matches. Until recently, composition-based statistics were available only for protein-protein searches. They are now available as a command line option for recent versions of TBLASTN and as an option for TBLASTN on the NCBI BLAST web server. Results We evaluate the statistical and retrieval accuracy of the E-values reported by a baseline version of TBLASTN and by two variants that use different types of composition-based statistics. To test the statistical accuracy of TBLASTN, we ran 1000 searches using scrambled proteins from the mouse genome and a database of human chromosomes. To test retrieval accuracy, we modernize and adapt to translated searches a test set previously used to evaluate the retrieval accuracy of protein-protein searches. We show that composition-based statistics greatly improve the statistical accuracy of TBLASTN, at a small cost to the retrieval accuracy. Conclusion TBLASTN is widely used, as it is common to wish to compare proteins to chromosomes or to libraries of mRNAs. Composition-based statistics improve the statistical accuracy, and therefore the reliability, of TBLASTN results. The algorithms used by TBLASTN are not widely known, and some of the most important are reported here. The data used to test TBLASTN are available for download and may be useful in other studies of translated search algorithms.

Particle Swarm Optimization and harmony search based clustering and routing in Wireless Sensor Networks

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Veena Anand

2017-01-01

Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

Tree decomposition based fast search of RNA structures including pseudoknots in genomes.

Science.gov (United States)

Song, Yinglei; Liu, Chunmei; Malmberg, Russell; Pan, Fangfang; Cai, Liming

2005-01-01

Searching genomes for RNA secondary structure with computational methods has become an important approach to the annotation of non-coding RNAs. However, due to the lack of efficient algorithms for accurate RNA structure-sequence alignment, computer programs capable of fast and effectively searching genomes for RNA secondary structures have not been available. In this paper, a novel RNA structure profiling model is introduced based on the notion of a conformational graph to specify the consensus structure of an RNA family. Tree decomposition yields a small tree width t for such conformation graphs (e.g., t = 2 for stem loops and only a slight increase for pseudo-knots). Within this modelling framework, the optimal alignment of a sequence to the structure model corresponds to finding a maximum valued isomorphic subgraph and consequently can be accomplished through dynamic programming on the tree decomposition of the conformational graph in time O(k(t)N(2)), where k is a small parameter; and N is the size of the projiled RNA structure. Experiments show that the application of the alignment algorithm to search in genomes yields the same search accuracy as methods based on a Covariance model with a significant reduction in computation time. In particular; very accurate searches of tmRNAs in bacteria genomes and of telomerase RNAs in yeast genomes can be accomplished in days, as opposed to months required by other methods. The tree decomposition based searching tool is free upon request and can be downloaded at our site h t t p ://w.uga.edu/RNA-informatics/software/index.php.

Category Theory Approach to Solution Searching Based on Photoexcitation Transfer Dynamics

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Makoto Naruse

2017-07-01

Full Text Available Solution searching that accompanies combinatorial explosion is one of the most important issues in the age of artificial intelligence. Natural intelligence, which exploits natural processes for intelligent functions, is expected to help resolve or alleviate the difficulties of conventional computing paradigms and technologies. In fact, we have shown that a single-celled organism such as an amoeba can solve constraint satisfaction problems and related optimization problems as well as demonstrate experimental systems based on non-organic systems such as optical energy transfer involving near-field interactions. However, the fundamental mechanisms and limitations behind solution searching based on natural processes have not yet been understood. Herein, we present a theoretical background of solution searching based on optical excitation transfer from a category-theoretic standpoint. One important indication inspired by the category theory is that the satisfaction of short exact sequences is critical for an adequate computational operation that determines the flow of time for the system and is termed as “short-exact-sequence-based time.” In addition, the octahedral and braid structures known in triangulated categories provide a clear understanding of the underlying mechanisms, including a quantitative indication of the difficulties of obtaining solutions based on homology dimension. This study contributes to providing a fundamental background of natural intelligence.

A Binary Cat Swarm Optimization Algorithm for the Non-Unicost Set Covering Problem

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Broderick Crawford

2015-01-01

Full Text Available The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library.

A novel line segment detection algorithm based on graph search

Science.gov (United States)

Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

2018-02-01

To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

[Formula: see text]: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain.

Science.gov (United States)

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Ramzan, Naeem; Khan, Wajahat Ali

2017-01-01

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search ([Formula: see text]) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, [Formula: see text] ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables [Formula: see text] to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of [Formula: see text] is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations.

Effect of Reading Ability and Internet Experience on Keyword-Based Image Search

Science.gov (United States)

Lei, Pei-Lan; Lin, Sunny S. J.; Sun, Chuen-Tsai

2013-01-01

Image searches are now crucial for obtaining information, constructing knowledge, and building successful educational outcomes. We investigated how reading ability and Internet experience influence keyword-based image search behaviors and performance. We categorized 58 junior-high-school students into four groups of high/low reading ability and…

Symbiotic organism search algorithm for simulation of J- V characteristics and optimizing internal parameters of DSSC developed using electrospun TiO2 nanofibers

Science.gov (United States)

Vinoth, S.; Kanimozhi, G.; Kumar, Harish; Srinadhu, E. S.; Satyanarayana, N.

2017-12-01

In the present investigation, the recently developed, simple, robust, and powerful metaheuristic symbiotic organism search (SOS) algorithm was used for simulation of J- V characteristics and optimizing the internal parameters of the dye-sensitized solar cells (DSSCs) fabricated using electrospun 1-D mesoporous TiO2 nanofibers as photoanode. The efficiency ( η = 5.80 %) of the DSSC made up of TiO2 nanofibers as photoanode is found to be ˜ 21.59% higher compared to the efficiency ( η = 4.77 %) of the DSSC made up of TiO2 nanoparticles as photoanode. The observed high efficiency can be attributed to high dye loading as well as high electron transport in the mesoporous 1-D TiO2 nanofibers. Further, the validity and advantage of SOS algorithm are verified by simulating J- V characteristics of DSSC with Lambert-W function.

Colliding bodies optimization extensions and applications

CERN Document Server

Kaveh, A

2015-01-01

This book presents and applies a novel efficient meta-heuristic optimization algorithm called Colliding Bodies Optimization (CBO) for various optimization problems. The first part of the book introduces the concepts and methods involved, while the second is devoted to the applications. Though optimal design of structures is the main topic, two chapters on optimal analysis and applications in constructional management are also included.  This algorithm is based on one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass. After a collision of two moving bodies with specified masses and velocities, these bodies again separate, with new velocities. This collision causes the agents to move toward better positions in the search space.  The main algorithm (CBO) is internally parameter independent, setting it apart from previously developed meta-heuristics. This algorithm is enhanced (ECBO) for more efficient applications in the optimal design of structures...

OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

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Dhananjay Kumar

2016-01-01

Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

Open meta-search with OpenSearch: a case study

OpenAIRE

O'Riordan, Adrian P.

2007-01-01

The goal of this project was to demonstrate the possibilities of open source search engine and aggregation technology in a Web environment by building a meta-search engine which employs free open search engines and open protocols. In contrast many meta-search engines on the Internet use proprietary search systems. The search engines employed in this case study are all based on the OpenSearch protocol. OpenSearch-compliant systems support XML technologies such as RSS and Atom for aggregation a...

Automatic synthesis of MEMS devices using self-adaptive hybrid metaheuristics

DEFF Research Database (Denmark)

Tutum, Cem Celal; Fan, Zhun

2011-01-01

- multaneous minimization of size and power input of a MEMS device, while investigating optimum geometrical conguration as the main concern. The major contribution of this paper is the application of self-adaptive memetic computing in MEMS design. An evolutionary multi-objective optimization (EMO) technique......, in particular non-dominated sorting genetic algorithm (NSGA-II), has been applied to- gether with a pattern recognition statistical tool, i.e. Principal Component Analysis (PCA), to nd multiple trade-o solutions in an ecient manner. Following this, a gradient- based local search, i.e. sequential quadratic...

Constraint-Based Local Search for Constrained Optimum Paths Problems

Science.gov (United States)

Pham, Quang Dung; Deville, Yves; van Hentenryck, Pascal

Constrained Optimum Path (COP) problems arise in many real-life applications and are ubiquitous in communication networks. They have been traditionally approached by dedicated algorithms, which are often hard to extend with side constraints and to apply widely. This paper proposes a constraint-based local search (CBLS) framework for COP applications, bringing the compositionality, reuse, and extensibility at the core of CBLS and CP systems. The modeling contribution is the ability to express compositional models for various COP applications at a high level of abstraction, while cleanly separating the model and the search procedure. The main technical contribution is a connected neighborhood based on rooted spanning trees to find high-quality solutions to COP problems. The framework, implemented in COMET, is applied to Resource Constrained Shortest Path (RCSP) problems (with and without side constraints) and to the edge-disjoint paths problem (EDP). Computational results show the potential significance of the approach.

Path Searching Based Fault Automated Recovery Scheme for Distribution Grid with DG

Science.gov (United States)

Xia, Lin; Qun, Wang; Hui, Xue; Simeng, Zhu

2016-12-01

Applying the method of path searching based on distribution network topology in setting software has a good effect, and the path searching method containing DG power source is also applicable to the automatic generation and division of planned islands after the fault. This paper applies path searching algorithm in the automatic division of planned islands after faults: starting from the switch of fault isolation, ending in each power source, and according to the line load that the searching path traverses and the load integrated by important optimized searching path, forming optimized division scheme of planned islands that uses each DG as power source and is balanced to local important load. Finally, COBASE software and distribution network automation software applied are used to illustrate the effectiveness of the realization of such automatic restoration program.

Sound Search Engine Concept

DEFF Research Database (Denmark)

2006-01-01

Sound search is provided by the major search engines, however, indexing is text based, not sound based. We will establish a dedicated sound search services with based on sound feature indexing. The current demo shows the concept of the sound search engine. The first engine will be realased June...

Applying Cuckoo Search for analysis of LFSR based cryptosystem

Directory of Open Access Journals (Sweden)

Maiya Din

2016-09-01

Full Text Available Cryptographic techniques are employed for minimizing security hazards to sensitive information. To make the systems more robust, cyphers or crypts being used need to be analysed for which cryptanalysts require ways to automate the process, so that cryptographic systems can be tested more efficiently. Evolutionary algorithms provide one such resort as these are capable of searching global optimal solution very quickly. Cuckoo Search (CS Algorithm has been used effectively in cryptanalysis of conventional systems like Vigenere and Transposition cyphers. Linear Feedback Shift Register (LFSR is a crypto primitive used extensively in design of cryptosystems. In this paper, we analyse LFSR based cryptosystem using Cuckoo Search to find correct initial states of used LFSR. Primitive polynomials of degree 11, 13, 17 and 19 are considered to analyse text crypts of length 200, 300 and 400 characters. Optimal solutions were obtained for the following CS parameters: Levy distribution parameter (β = 1.5 and Alien eggs discovering probability (pa = 0.25.

A unified architecture for biomedical search engines based on semantic web technologies.

Science.gov (United States)

Jalali, Vahid; Matash Borujerdi, Mohammad Reza

2011-04-01

There is a huge growth in the volume of published biomedical research in recent years. Many medical search engines are designed and developed to address the over growing information needs of biomedical experts and curators. Significant progress has been made in utilizing the knowledge embedded in medical ontologies and controlled vocabularies to assist these engines. However, the lack of common architecture for utilized ontologies and overall retrieval process, hampers evaluating different search engines and interoperability between them under unified conditions. In this paper, a unified architecture for medical search engines is introduced. Proposed model contains standard schemas declared in semantic web languages for ontologies and documents used by search engines. Unified models for annotation and retrieval processes are other parts of introduced architecture. A sample search engine is also designed and implemented based on the proposed architecture in this paper. The search engine is evaluated using two test collections and results are reported in terms of precision vs. recall and mean average precision for different approaches used by this search engine.

Road Traffic Congestion Management Based on a Search-Allocation Approach

Directory of Open Access Journals (Sweden)

Raiyn Jamal

2017-03-01

Full Text Available This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA. An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT technologies.

Agent-oriented Architecture for Task-based Information Search System

NARCIS (Netherlands)

Aroyo, Lora; de Bra, Paul M.E.; De Bra, P.; Hardman, L.

1999-01-01

The topic of the reported research discusses an agent-oriented architecture of an educational information search system AIMS - a task-based learner support system. It is implemented within the context of 'Courseware Engineering' on-line course at the Faculty of Educational Science and Technology,

Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

Science.gov (United States)

Chaudhuri, Sutapa; Goswami, Sayantika; Das, Debanjana; Middey, Anirban

2014-05-01

Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5-35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Sadhana. KARTHIK CHANDRAN. Articles written in Sadhana. Volume 42 Issue 6 June 2017 pp 817-826. Deterministic oscillatory search: a new meta-heuristic optimization algorithm · N ARCHANA R VIDHYAPRIYA ANTONY BENEDICT KARTHIK CHANDRAN · More Details Abstract Fulltext PDF.

A Novel Approach for Bi-Level Segmentation of Tuberculosis Bacilli Based on Meta-Heuristic Algorithms

Directory of Open Access Journals (Sweden)

AYAS, S.

2018-02-01

Full Text Available Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.

Using Metaheuristic Algorithms for Solving a Hub Location Problem: Application in Passive Optical Network Planning

Directory of Open Access Journals (Sweden)

Masoud Rabbani

2017-02-01

Full Text Available Nowadays, fiber-optic due to having greater bandwidth and being more efficient compared with other similar technologies, are counted as one the most important tools for data transfer. In this article, an integrated mathematical model for a three-level fiber-optic distribution network with consideration of simultaneous backbone and local access networks is presented in which the backbone network is a ring and the access networks has a star-star topology. The aim of the model is to determine the location of the central offices and splitters, how connections are made between central offices, and allocation of each demand node to a splitter or central office in a way that the wiring cost of fiber optical and concentrator installation are minimized. Moreover, each user’s desired bandwidth should be provided efficiently. Then, the proposed model is validated by GAMS software in small-sized problems, afterwards the model is solved by two meta-heuristic methods including differential evolution (DE and genetic algorithm (GA in large-scaled problems and the results of two algorithms are compared with respect to computational time and objective function obtained value. Finally, a sensitivity analysis is provided. Keyword: Fiber-optic, telecommunication network, hub-location, passive splitter, three-level network.

Can social tagged images aid concept-based video search?

NARCIS (Netherlands)

Setz, A.T.; Snoek, C.G.M.

2009-01-01

This paper seeks to unravel whether commonly available social tagged images can be exploited as a training resource for concept-based video search. Since social tags are known to be ambiguous, overly personalized, and often error prone, we place special emphasis on the role of disambiguation. We

A World Wide Web Region-Based Image Search Engine

DEFF Research Database (Denmark)

Kompatsiaris, Ioannis; Triantafyllou, Evangelia; Strintzis, Michael G.

2001-01-01

In this paper the development of an intelligent image content-based search engine for the World Wide Web is presented. This system will offer a new form of media representation and access of content available in WWW. Information Web Crawlers continuously traverse the Internet and collect images...

Developing a distributed HTML5-based search engine for geospatial resource discovery

Science.gov (United States)

ZHOU, N.; XIA, J.; Nebert, D.; Yang, C.; Gui, Z.; Liu, K.

2013-12-01

With explosive growth of data, Geospatial Cyberinfrastructure(GCI) components are developed to manage geospatial resources, such as data discovery and data publishing. However, the efficiency of geospatial resources discovery is still challenging in that: (1) existing GCIs are usually developed for users of specific domains. Users may have to visit a number of GCIs to find appropriate resources; (2) The complexity of decentralized network environment usually results in slow response and pool user experience; (3) Users who use different browsers and devices may have very different user experiences because of the diversity of front-end platforms (e.g. Silverlight, Flash or HTML). To address these issues, we developed a distributed and HTML5-based search engine. Specifically, (1)the search engine adopts a brokering approach to retrieve geospatial metadata from various and distributed GCIs; (2) the asynchronous record retrieval mode enhances the search performance and user interactivity; (3) the search engine based on HTML5 is able to provide unified access capabilities for users with different devices (e.g. tablet and smartphone).

SHOP: scaffold hopping by GRID-based similarity searches

DEFF Research Database (Denmark)

Bergmann, Rikke; Linusson, Anna; Zamora, Ismael

2007-01-01

A new GRID-based method for scaffold hopping (SHOP) is presented. In a fully automatic manner, scaffolds were identified in a database based on three types of 3D-descriptors. SHOP's ability to recover scaffolds was assessed and validated by searching a database spiked with fragments of known...... scaffolds were in the 31 top-ranked scaffolds. SHOP also identified new scaffolds with substantially different chemotypes from the queries. Docking analysis indicated that the new scaffolds would have similar binding modes to those of the respective query scaffolds observed in X-ray structures...

A Study on Control System Design Based on ARM Sea Target Search System

Directory of Open Access Journals (Sweden)

Lin Xinwei

2015-01-01

Full Text Available The infrared detector is used for sea target search, which can assist humans in searching suspicious objects at night and under poor visibility conditions, and improving search efficiency. This paper applies for interrupt and stack technology to solve problems of data losses that may be caused by one-to-many multi-byte protocol communication. Meanwhile, this paper implements hardware and software design of the system based on industrial-grade ARM control chip and uC / OS-II embedded operating system. The control system in the sea target search system is an information exchange and control center of the whole system, which solves the problem of controlling over the shooting angle of the infrared detector in the process of target search. After testing, the control system operates stably and reliably, and realizes rotation and control functions of the pan/tilt platform during automatic search, manual search and track.

An Analysis of Literature Searching Anxiety in Evidence-Based Medicine Education

Directory of Open Access Journals (Sweden)

Hui-Chin Chang

2014-01-01

Full Text Available Introduction. Evidence-Based Medicine (EBM is hurtling towards a cornerstone in lifelong learning for healthcare personnel worldwide. This study aims to evaluate the literature searching anxiety in graduate students in practicing EBM. Method The study participants were 48 graduate students who enrolled the EBM course at aMedical Universityin central Taiwan. Student’s t-test, Pearson correlation and multivariate regression, interviewing are used to evaluate the students’ literature searching anxiety of EBM course. The questionnaire is Literature Searching Anxiety Rating Scale -LSARS. Results The sources of anxiety are uncertainty of database selection, literatures evaluation and selection, technical assistance request, computer programs use, English and EBM education programs were disclosed. The class performance is negatively related to LSARS score, however, the correlation is statistically insignificant with the adjustment of gender, degree program, age category and experience of publication. Conclusion This study helps in understanding the causes and the extent of anxiety in order to work on a better teaching program planning to improve user’s searching skills and the capability of utilization the information; At the same time, provide friendly-user facilities of evidence searching. In short, we need to upgrade the learner’s searching 45 skills and reduce theanxiety. We also need to stress on the auxiliary teaching program for those with the prevalent and profoundanxiety during literature searching.

Ant colony optimization techniques for the hamiltonian p-median problem

Directory of Open Access Journals (Sweden)

M. Zohrehbandian

2010-12-01

Full Text Available Location-Routing problems involve locating a number of facilitiesamong candidate sites and establishing delivery routes to a set of users in such a way that the total system cost is minimized. A special case of these problems is Hamiltonian p-Median problem (HpMP. This research applies the metaheuristic method of ant colony optimization (ACO to solve the HpMP. Modifications are made to the ACO algorithm used to solve the traditional vehicle routing problem (VRP in order to allow the search of the optimal solution of the HpMP. Regarding this metaheuristic algorithm a computational experiment is reported as well.

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

Directory of Open Access Journals (Sweden)

Danping Wang

2017-01-01

Full Text Available A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional, 10 CEC2005 benchmark functions (30-dimensional, and a real-world problem (multilevel image segmentation problems. Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

MotionExplorer: exploratory search in human motion capture data based on hierarchical aggregation.

Science.gov (United States)

Bernard, Jürgen; Wilhelm, Nils; Krüger, Björn; May, Thorsten; Schreck, Tobias; Kohlhammer, Jörn

2013-12-01

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

Web-based information search and retrieval: effects of strategy use and age on search success.

Science.gov (United States)

Stronge, Aideen J; Rogers, Wendy A; Fisk, Arthur D

2006-01-01

The purpose of this study was to investigate the relationship between strategy use and search success on the World Wide Web (i.e., the Web) for experienced Web users. An additional goal was to extend understanding of how the age of the searcher may influence strategy use. Current investigations of information search and retrieval on the Web have provided an incomplete picture of Web strategy use because participants have not been given the opportunity to demonstrate their knowledge of Web strategies while also searching for information on the Web. Using both behavioral and knowledge-engineering methods, we investigated searching behavior and system knowledge for 16 younger adults (M = 20.88 years of age) and 16 older adults (M = 67.88 years). Older adults were less successful than younger adults in finding correct answers to the search tasks. Knowledge engineering revealed that the age-related effect resulted from ineffective search strategies and amount of Web experience rather than age per se. Our analysis led to the development of a decision-action diagram representing search behavior for both age groups. Older adults had more difficulty than younger adults when searching for information on the Web. However, this difficulty was related to the selection of inefficient search strategies, which may have been attributable to a lack of knowledge about available Web search strategies. Actual or potential applications of this research include training Web users to search more effectively and suggestions to improve the design of search engines.

Constraint-based local search for container stowage slot planning

DEFF Research Database (Denmark)

Pacino, Dario; Jensen, Rune Møller; Bebbington, Tom

2012-01-01

-sea vessels. This paper describes the constrained-based local search algorithm used in the second phase of this approach where individual containers are assigned to slots in each bay section. The algorithm can solve this problem in an average of 0.18 seconds per bay, corresponding to a 20 seconds runtime...

A DE-Based Scatter Search for Global Optimization Problems

Directory of Open Access Journals (Sweden)

Kun Li

2015-01-01

Full Text Available This paper proposes a hybrid scatter search (SS algorithm for continuous global optimization problems by incorporating the evolution mechanism of differential evolution (DE into the reference set updated procedure of SS to act as the new solution generation method. This hybrid algorithm is called a DE-based SS (SSDE algorithm. Since different kinds of mutation operators of DE have been proposed in the literature and they have shown different search abilities for different kinds of problems, four traditional mutation operators are adopted in the hybrid SSDE algorithm. To adaptively select the mutation operator that is most appropriate to the current problem, an adaptive mechanism for the candidate mutation operators is developed. In addition, to enhance the exploration ability of SSDE, a reinitialization method is adopted to create a new population and subsequently construct a new reference set whenever the search process of SSDE is trapped in local optimum. Computational experiments on benchmark problems show that the proposed SSDE is competitive or superior to some state-of-the-art algorithms in the literature.

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Sadhana. ANTONY BENEDICT. Articles written in Sadhana. Volume 42 Issue 6 June 2017 pp 817-826. Deterministic oscillatory search: a new meta-heuristic optimization algorithm · N ARCHANA R VIDHYAPRIYA ANTONY BENEDICT KARTHIK CHANDRAN · More Details Abstract Fulltext PDF. The paper ...

Binary Bees Algorithm - bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem

Science.gov (United States)

Xu, Shuo; Ji, Ze; Truong Pham, Duc; Yu, Fan

2011-11-01

The simultaneous mission assignment and home allocation for hospital service robots studied is a Multidimensional Assignment Problem (MAP) with multiobjectives and multiconstraints. A population-based metaheuristic, the Binary Bees Algorithm (BBA), is proposed to optimize this NP-hard problem. Inspired by the foraging mechanism of honeybees, the BBA's most important feature is an explicit functional partitioning between global search and local search for exploration and exploitation, respectively. Its key parts consist of adaptive global search, three-step elitism selection (constraint handling, non-dominated solutions selection, and diversity preservation), and elites-centred local search within a Hamming neighbourhood. Two comparative experiments were conducted to investigate its single objective optimization, optimization effectiveness (indexed by the S-metric and C-metric) and optimization efficiency (indexed by computational burden and CPU time) in detail. The BBA outperformed its competitors in almost all the quantitative indices. Hence, the above overall scheme, and particularly the searching history-adapted global search strategy was validated.

A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

Mostafa Khorramizadeh

2015-01-01

Full Text Available The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems.

Efficient Multi-keyword Ranked Search over Outsourced Cloud Data based on Homomorphic Encryption

Directory of Open Access Journals (Sweden)

Nie Mengxi

2016-01-01

Full Text Available With the development of cloud computing, more and more data owners are motivated to outsource their data to the cloud server for great flexibility and less saving expenditure. Because the security of outsourced data must be guaranteed, some encryption methods should be used which obsoletes traditional data utilization based on plaintext, e.g. keyword search. To solve the search of encrypted data, some schemes were proposed to solve the search of encrypted data, e.g. top-k single or multiple keywords retrieval. However, the efficiency of these proposed schemes is not high enough to be impractical in the cloud computing. In this paper, we propose a new scheme based on homomorphic encryption to solve this challenging problem of privacy-preserving efficient multi-keyword ranked search over outsourced cloud data. In our scheme, the inner product is adopted to measure the relevance scores and the technique of relevance feedback is used to reflect the search preference of the data users. Security analysis shows that the proposed scheme can meet strict privacy requirements for such a secure cloud data utilization system. Performance evaluation demonstrates that the proposed scheme can achieve low overhead on both computation and communication.

Analysis on the Correlation of Traffic Flow in Hainan Province Based on Baidu Search

Science.gov (United States)

Chen, Caixia; Shi, Chun

2018-03-01

Internet search data records user’s search attention and consumer demand, providing necessary database for the Hainan traffic flow model. Based on Baidu Index, with Hainan traffic flow as example, this paper conduct both qualitative and quantitative analysis on the relationship between search keyword from Baidu Index and actual Hainan tourist traffic flow, and build multiple regression model by SPSS.

Analysis of Search Engines and Meta Search Engines\\\\\\' Position by University of Isfahan Users Based on Rogers\\\\\\' Diffusion of Innovation Theory

Directory of Open Access Journals (Sweden)

Maryam Akbari

2012-10-01

Full Text Available The present study investigated the analysis of search engines and meta search engines adoption process by University of Isfahan users during 2009-2010 based on the Rogers' diffusion of innovation theory. The main aim of the research was to study the rate of adoption and recognizing the potentials and effective tools in search engines and meta search engines adoption among University of Isfahan users. The research method was descriptive survey study. The cases of the study were all of the post graduate students of the University of Isfahan. 351 students were selected as the sample and categorized by a stratified random sampling method. Questionnaire was used for collecting data. The collected data was analyzed using SPSS 16 in both descriptive and analytic statistic. For descriptive statistic frequency, percentage and mean were used, while for analytic statistic t-test and Kruskal-Wallis non parametric test (H-test were used. The finding of t-test and Kruscal-Wallis indicated that the mean of search engines and meta search engines adoption did not show statistical differences gender, level of education and the faculty. Special search engines adoption process was different in terms of gender but not in terms of the level of education and the faculty. Other results of the research indicated that among general search engines, Google had the most adoption rate. In addition, among the special search engines, Google Scholar and among the meta search engines Mamma had the most adopting rate. Findings also showed that friends played an important role on how students adopted general search engines while professors had important role on how students adopted special search engines and meta search engines. Moreover, results showed that the place where students got the most acquaintance with search engines and meta search engines was in the university. The finding showed that the curve of adoption rate was not normal and it was not also in S-shape. Morover

A HYBRID HEURISTIC ALGORITHM FOR THE CLUSTERED TRAVELING SALESMAN PROBLEM

Directory of Open Access Journals (Sweden)

Mário Mestria

2016-04-01

Full Text Available ABSTRACT This paper proposes a hybrid heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedure, Iterated Local Search and Variable Neighborhood Descent, to solve the Clustered Traveling Salesman Problem (CTSP. Hybrid Heuristic algorithm uses several variable neighborhood structures combining the intensification (using local search operators and diversification (constructive heuristic and perturbation routine. In the CTSP, the vertices are partitioned into clusters and all vertices of each cluster have to be visited contiguously. The CTSP is -hard since it includes the well-known Traveling Salesman Problem (TSP as a special case. Our hybrid heuristic is compared with three heuristics from the literature and an exact method. Computational experiments are reported for different classes of instances. Experimental results show that the proposed hybrid heuristic obtains competitive results within reasonable computational time.

Is Internet search better than structured instruction for web-based health education?

Science.gov (United States)

Finkelstein, Joseph; Bedra, McKenzie

2013-01-01

Internet provides access to vast amounts of comprehensive information regarding any health-related subject. Patients increasingly use this information for health education using a search engine to identify education materials. An alternative approach of health education via Internet is based on utilizing a verified web site which provides structured interactive education guided by adult learning theories. Comparison of these two approaches in older patients was not performed systematically. The aim of this study was to compare the efficacy of a web-based computer-assisted education (CO-ED) system versus searching the Internet for learning about hypertension. Sixty hypertensive older adults (age 45+) were randomized into control or intervention groups. The control patients spent 30 to 40 minutes searching the Internet using a search engine for information about hypertension. The intervention patients spent 30 to 40 minutes using the CO-ED system, which provided computer-assisted instruction about major hypertension topics. Analysis of pre- and post- knowledge scores indicated a significant improvement among CO-ED users (14.6%) as opposed to Internet users (2%). Additionally, patients using the CO-ED program rated their learning experience more positively than those using the Internet.

Promoting evidence based medicine in preclinical medical students via a federated literature search tool.

Science.gov (United States)

Keim, Samuel Mark; Howse, David; Bracke, Paul; Mendoza, Kathryn

2008-01-01

Medical educators are increasingly faced with directives to teach Evidence Based Medicine (EBM) skills. Because of its nature, integrating fundamental EBM educational content is a challenge in the preclinical years. To analyse preclinical medical student user satisfaction and feedback regarding a clinical EBM search strategy. The authors introduced a custom EBM search option with a self-contained education structure to first-year medical students. The implementation took advantage of a major curricular change towards case-based instruction. Medical student views and experiences were studied regarding the tool's convenience, problems and the degree to which they used it to answer questions raised by case-based instruction. Surveys were completed by 70% of the available first-year students. Student satisfaction and experiences were strongly positive towards the EBM strategy, especially of the tool's convenience and utility for answering issues raised during case-based learning sessions. About 90% of the students responded that the tool was easy to use, productive and accessed for half or more of their search needs. This study provides evidence that the integration of an educational EBM search tool can be positively received by preclinical medical students.

Developing a Grid-based search and categorization tool

CERN Document Server

Haya, Glenn; Vigen, Jens

2003-01-01

Grid technology has the potential to improve the accessibility of digital libraries. The participants in Project GRACE (Grid Search And Categorization Engine) are in the process of developing a search engine that will allow users to search through heterogeneous resources stored in geographically distributed digital collections. What differentiates this project from current search tools is that GRACE will be run on the European Data Grid, a large distributed network, and will not have a single centralized index as current web search engines do. In some cases, the distributed approach offers advantages over the centralized approach since it is more scalable, can be used on otherwise inaccessible material, and can provide advanced search options customized for each data source.

SA-Search: a web tool for protein structure mining based on a Structural Alphabet.

Science.gov (United States)

Guyon, Frédéric; Camproux, Anne-Claude; Hochez, Joëlle; Tufféry, Pierre

2004-07-01

SA-Search is a web tool that can be used to mine for protein structures and extract structural similarities. It is based on a hidden Markov model derived Structural Alphabet (SA) that allows the compression of three-dimensional (3D) protein conformations into a one-dimensional (1D) representation using a limited number of prototype conformations. Using such a representation, classical methods developed for amino acid sequences can be employed. Currently, SA-Search permits the performance of fast 3D similarity searches such as the extraction of exact words using a suffix tree approach, and the search for fuzzy words viewed as a simple 1D sequence alignment problem. SA-Search is available at http://bioserv.rpbs.jussieu.fr/cgi-bin/SA-Search.

Improved teaching-learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

Science.gov (United States)

Buddala, Raviteja; Mahapatra, Siba Sankar

2017-11-01

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

The Search for Extension: 7 Steps to Help People Find Research-Based Information on the Internet

Science.gov (United States)

Hill, Paul; Rader, Heidi B.; Hino, Jeff

2012-01-01

For Extension's unbiased, research-based content to be found by people searching the Internet, it needs to be organized in a way conducive to the ranking criteria of a search engine. With proper web design and search engine optimization techniques, Extension's content can be found, recognized, and properly indexed by search engines and…

Web Search Engines

OpenAIRE

Rajashekar, TB

1998-01-01

The World Wide Web is emerging as an all-in-one information source. Tools for searching Web-based information include search engines, subject directories and meta search tools. We take a look at key features of these tools and suggest practical hints for effective Web searching.

A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure.

Science.gov (United States)

Naghibi, Tofigh; Hoffmann, Sarah; Pfister, Beat

2015-08-01

Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximum-cut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.

An ontology-based search engine for protein-protein interactions.

Science.gov (United States)

Park, Byungkyu; Han, Kyungsook

2010-01-18

Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

evaluating search effectiveness of some selected search engines

African Journals Online (AJOL)

Precision, relative recall and response time were considered for this ... a total of 24 search queries were sampled based on information queries, .... searching process and results, although there are other ... Q3.2 Software prototype model.

Augmented neural networks and problem structure-based heuristics for the bin-packing problem

Science.gov (United States)

Kasap, Nihat; Agarwal, Anurag

2012-08-01

In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.

Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

Directory of Open Access Journals (Sweden)

Chunhua Ju

2012-01-01

Full Text Available Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM and an improved artificial immune network algorithm (aiNet are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA, simulated annealing algorithm (SA, and ant colony optimization (ACO.

MULTI-OBJECTIVE OPTIMISATION OF LASER CUTTING USING CUCKOO SEARCH ALGORITHM

Directory of Open Access Journals (Sweden)

M. MADIĆ

2015-03-01

Full Text Available Determining of optimal laser cutting conditions for improving cut quality characteristics is of great importance in process planning. This paper presents multi-objective optimisation of the CO2 laser cutting process considering three cut quality characteristics such as surface roughness, heat affected zone (HAZ and kerf width. It combines an experimental design by using Taguchi’s method, modelling the relationships between the laser cutting factors (laser power, cutting speed, assist gas pressure and focus position and cut quality characteristics by artificial neural networks (ANNs, formulation of the multiobjective optimisation problem using weighting sum method, and solving it by the novel meta-heuristic cuckoo search algorithm (CSA. The objective is to obtain optimal cutting conditions dependent on the importance order of the cut quality characteristics for each of four different case studies presented in this paper. The case studies considered in this study are: minimisation of cut quality characteristics with equal priority, minimisation of cut quality characteristics with priority given to surface roughness, minimisation of cut quality characteristics with priority given to HAZ, and minimisation of cut quality characteristics with priority given to kerf width. The results indicate that the applied CSA for solving the multi-objective optimisation problem is effective, and that the proposed approach can be used for selecting the optimal laser cutting factors for specific production requirements.

Evaluation of Voltage Control Approaches for Future Smart Distribution Networks

Directory of Open Access Journals (Sweden)

Pengfei Wang

2017-08-01

Full Text Available This paper evaluates meta-heuristic and deterministic approaches for distribution network voltage control. As part of this evaluation, a novel meta-heuristic algorithm, Cuckoo Search, is applied for distribution network voltage control and compared with a deterministic voltage control algorithm, the oriented discrete coordinate decent method (ODCDM. ODCDM has been adopted in a state-of-the-art industrial product and applied in real distribution networks. These two algorithms have been evaluated under a set of test cases, which were generated to represent the voltage control problems in current and future distribution networks. Sampled test results have been presented, and findings have been discussed regarding the adoption of different optimization algorithms for current and future distribution networks.

A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects

International Nuclear Information System (INIS)

Secui, Dinu Calin

2016-01-01

This paper proposes a new metaheuristic algorithm, called Modified Symbiotic Organisms Search (MSOS) algorithm, to solve the economic dispatch problem considering the valve-point effects, the prohibited operating zones (POZ), the transmission line losses, multi-fuel sources, as well as other operating constraints of the generating units and power system. The MSOS algorithm introduces, in all of its phases, new relations to update the solutions to improve its capacity of identifying stable and of high-quality solutions in a reasonable time. Furthermore, to increase the capacity of exploring the MSOS algorithm in finding the most promising zones, it is endowed with a chaotic component generated by the Logistic map. The performance of the modified algorithm and of the original algorithm Symbiotic Organisms Search (SOS) is tested on five systems of different characteristics, constraints and dimensions (13-unit, 40-unit, 80-unit, 160-unit and 320-unit). The results obtained by applying the proposed algorithm (MSOS) show that this has a better performance than other techniques of optimization recently used in solving the economic dispatch problem with valve-point effects. - Highlights: • A new modified SOS algorithm (MSOS) is proposed to solve the EcD problem. • Valve-point effects, ramp-rate limits, POZ, multi-fuel sources, transmission losses were considered. • The algorithm is tested on five systems having 13, 40, 80, 160 and 320 thermal units. • MSOS algorithm outperforms many other optimization techniques.

The Research and Test of Fast Radio Burst Real-time Search Algorithm Based on GPU Acceleration

Science.gov (United States)

Wang, J.; Chen, M. Z.; Pei, X.; Wang, Z. Q.

2017-03-01

In order to satisfy the research needs of Nanshan 25 m radio telescope of Xinjiang Astronomical Observatory (XAO) and study the key technology of the planned QiTai radio Telescope (QTT), the receiver group of XAO studied the GPU (Graphics Processing Unit) based real-time FRB searching algorithm which developed from the original FRB searching algorithm based on CPU (Central Processing Unit), and built the FRB real-time searching system. The comparison of the GPU system and the CPU system shows that: on the basis of ensuring the accuracy of the search, the speed of the GPU accelerated algorithm is improved by 35-45 times compared with the CPU algorithm.

Metaheuristic approaches to order sequencing on a unidirectional ...

African Journals Online (AJOL)

is that tabu search (TS) incorporates “intelligence. ..... location that will benefit the solution quality by considering implications that may arise ..... [12] Glover F, 1986, Future paths for integer programming and links to artificial intelligence, ...

An opposition-based harmony search algorithm for engineering optimization problems

Directory of Open Access Journals (Sweden)

Abhik Banerjee

2014-03-01

Full Text Available Harmony search (HS is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. The proposed opposition-based HS (OHS of the present work employs opposition-based learning for harmony memory initialization and also for generation jumping. The concept of opposite number is utilized in OHS to improve the convergence rate of the HS algorithm. The potential of the proposed algorithm is assessed by means of an extensive comparative study of the numerical results on sixteen benchmark test functions. Additionally, the effectiveness of the proposed algorithm is tested for reactive power compensation of an autonomous power system. For real-time reactive power compensation of the studied model, Takagi Sugeno fuzzy logic (TSFL is employed. Time-domain simulation reveals that the proposed OHS-TSFL yields on-line, off-nominal model parameters, resulting in real-time incremental change in terminal voltage response profile.

FPS-RAM: Fast Prefix Search RAM-Based Hardware for Forwarding Engine

Science.gov (United States)

Zaitsu, Kazuya; Yamamoto, Koji; Kuroda, Yasuto; Inoue, Kazunari; Ata, Shingo; Oka, Ikuo

Ternary content addressable memory (TCAM) is becoming very popular for designing high-throughput forwarding engines on routers. However, TCAM has potential problems in terms of hardware and power costs, which limits its ability to deploy large amounts of capacity in IP routers. In this paper, we propose new hardware architecture for fast forwarding engines, called fast prefix search RAM-based hardware (FPS-RAM). We designed FPS-RAM hardware with the intent of maintaining the same search performance and physical user interface as TCAM because our objective is to replace the TCAM in the market. Our RAM-based hardware architecture is completely different from that of TCAM and has dramatically reduced the costs and power consumption to 62% and 52%, respectively. We implemented FPS-RAM on an FPGA to examine its lookup operation.

A Novel Discrete Global-Best Harmony Search Algorithm for Solving 0-1 Knapsack Problems

Directory of Open Access Journals (Sweden)

Wan-li Xiang

2014-01-01

is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems.

Location-based Services using Image Search

DEFF Research Database (Denmark)

Vertongen, Pieter-Paulus; Hansen, Dan Witzner

2008-01-01

Recent developments in image search has made them sufficiently efficient to be used in real-time applications. GPS has become a popular navigation tool. While GPS information provide reasonably good accuracy, they are not always present in all hand held devices nor are they accurate in all situat...... of the image search engine and database image location knowledge, the location is determined of the query image and associated data can be presented to the user....

Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

Directory of Open Access Journals (Sweden)

Ibidun Christiana Obagbuwa

2016-09-01

Full Text Available The Cockroach Swarm Optimization (CSO algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP, which is considered to be an NP-hard Combinatorial Optimization Problem (COP. A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO algorithm on TSP were compared to other meta-heuristic algorithms.

Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

Directory of Open Access Journals (Sweden)

Gai-Ge Wang

2013-01-01

Full Text Available Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH, for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH method is proposed for optimization tasks. A new krill selecting (KS operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA. In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

Optimal Routing for Heterogeneous Fixed Fleets of Multicompartment Vehicles

OpenAIRE

Wang, Qian; Ji, Qingkai; Chiu, Chun-Hung

2014-01-01

We present a metaheuristic called the reactive guided tabu search (RGTS) to solve the heterogeneous fleet multicompartment vehicle routing problem (MCVRP), where a single vehicle is required for cotransporting multiple customer orders. MCVRP is commonly found in delivery of fashion apparel, petroleum distribution, food distribution, and waste collection. In searching the optimum solution of MCVRP, we need to handle a large amount of local optima in the solution spaces. To overcome this proble...

Large Neighborhood Search

DEFF Research Database (Denmark)

Pisinger, David; Røpke, Stefan

2010-01-01

Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better...... candidate solutions in each iteration and hence traverse a more promising search path. Starting from the large neighborhood search method,we give an overview of very large scale neighborhood search methods and discuss recent variants and extensions like variable depth search and adaptive large neighborhood...

Novel citation-based search method for scientific literature: application to meta-analyses.

Science.gov (United States)

Janssens, A Cecile J W; Gwinn, M

2015-10-13

Finding eligible studies for meta-analysis and systematic reviews relies on keyword-based searching as the gold standard, despite its inefficiency. Searching based on direct citations is not sufficiently comprehensive. We propose a novel strategy that ranks articles on their degree of co-citation with one or more "known" articles before reviewing their eligibility. In two independent studies, we aimed to reproduce the results of literature searches for sets of published meta-analyses (n = 10 and n = 42). For each meta-analysis, we extracted co-citations for the randomly selected 'known' articles from the Web of Science database, counted their frequencies and screened all articles with a score above a selection threshold. In the second study, we extended the method by retrieving direct citations for all selected articles. In the first study, we retrieved 82% of the studies included in the meta-analyses while screening only 11% as many articles as were screened for the original publications. Articles that we missed were published in non-English languages, published before 1975, published very recently, or available only as conference abstracts. In the second study, we retrieved 79% of included studies while screening half the original number of articles. Citation searching appears to be an efficient and reasonably accurate method for finding articles similar to one or more articles of interest for meta-analysis and reviews.

Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems

International Nuclear Information System (INIS)

Ahmadi, Mohamadreza; Mojallali, Hamed

2012-01-01

Highlights: ► A new meta-heuristic optimization algorithm. ► Integration of invasive weed optimization and chaotic search methods. ► A novel parameter identification scheme for chaotic systems. - Abstract: This paper introduces a novel hybrid optimization algorithm by taking advantage of the stochastic properties of chaotic search and the invasive weed optimization (IWO) method. In order to deal with the weaknesses associated with the conventional method, the proposed chaotic invasive weed optimization (CIWO) algorithm is presented which incorporates the capabilities of chaotic search methods. The functionality of the proposed optimization algorithm is investigated through several benchmark multi-dimensional functions. Furthermore, an identification technique for chaotic systems based on the CIWO algorithm is outlined and validated by several examples. The results established upon the proposed scheme are also supplemented which demonstrate superior performance with respect to other conventional methods.

Polyphyletic migration operator and orthogonal learning aided biogeography-based optimization for dynamic economic dispatch with valve-point effects

International Nuclear Information System (INIS)

Xiong, Guojiang; Li, Yinhong; Chen, Jinfu; Shi, Dongyuan; Duan, Xianzhong

2014-01-01

Highlights: • New method for dynamic economic dispatch problem using POLBBO. • Considering valve-point effects, ramp rate limits, transmission network losses. • POLBBO is able to balance the global exploration and the local exploitation. • An effective simultaneous constraints handling technique is proposed. • The achieved results by POLBBO are better than those reported in other literatures. - Abstract: Shortage of energy resources, rising power generation cost, and increasing electric energy demand make the dynamic economic dispatch (DED) increasingly necessary in today’s competitive electricity market. In this paper, an enhanced biogeography-based optimization (BBO) referred to as POLBBO is proposed to solve the DED problem with valve-point effects. BBO is a relatively new powerful population-based meta-heuristic algorithm inspired by biogeography and has been extensively applied to many scientific and engineering problems. However, its direct-copying-based migration and random mutation operators make BBO possess good local exploitation ability but lack enough global exploration ability. To remedy the defect, on one hand, an efficient operator named polyphyletic migration operator is proposed to enhance the search ability of POLBBO. This operator can not only generate new features from more promising areas in the search space, but also effectively increase the population diversity. On the other hand, an orthogonal learning (OL) strategy based on orthogonal experimental design is presented. The OL strategy can quickly discover useful information from the search experiences and effectively utilize the information to construct a more promising solution, and thereby provide a systematic and elaborate reasoning method to guide the search directions of POLBBO. In addition, an effective simultaneous constraints handling technique without penalty factor settings is developed to handle various complicated constraints of the DED problem. Finally, four test

Spatial planning via extremal optimization enhanced by cell-based local search

International Nuclear Information System (INIS)

Sidiropoulos, Epaminondas

2014-01-01

A new treatment is presented for land use planning problems by means of extremal optimization in conjunction to cell-based neighborhood local search. Extremal optimization, inspired by self-organized critical models of evolution has been applied mainly to the solution of classical combinatorial optimization problems. Cell-based local search has been employed by the author elsewhere in problems of spatial resource allocation in combination with genetic algorithms and simulated annealing. In this paper it complements extremal optimization in order to enhance its capacity for a spatial optimization problem. The hybrid method thus formed is compared to methods of the literature on a specific characteristic problem. It yields better results both in terms of objective function values and in terms of compactness. The latter is an important quantity for spatial planning. The present treatment yields significant compactness values as emergent results

Prospects for SUSY discovery based on inclusive searches with the ATLAS detector

International Nuclear Information System (INIS)

Ventura, Andrea

2009-01-01

The search for Supersymmetry (SUSY) among the possible scenarios of new physics is one of the most relevant goals of the ATLAS experiment running at CERN's Large Hadron Collider. In the present work the expected prospects for discovering SUSY with the ATLAS detector are reviewed, in particular for the first fb -1 of collected integrated luminosity. All studies and results reported here are based on inclusive search analyses realized with Monte Carlo signal and background data simulated through the ATLAS apparatus.

A hybrid approach based on ANP, ELECTRE and SIMANP metaheuristic method for outsourcing manufacturing procedures according to supply chain risks - Case study: A medical equipment manufacturer company in Iran

Directory of Open Access Journals (Sweden)

Hiwa Farughi

2017-01-01

Full Text Available Nowadays enterprises should consider seeking to reduce the supply chain risks as a crucial part of their activities in order to improve their competitiveness in the international context. Choosing the suitable strategy in connection with assigning some parts of the production process to outside the organization is a complex multi-criteria decision making problem and it gets more complicated when supply chain risk factors as the factors to select the strategy as well as dependence and the close ties between these criteria also be considered. In this paper, after the identification of risks in the supply chain of a medical equipment manufacturer company, dependence and ties between criteria in line with choosing the best strategy among existing alternatives has been examined in the form of a combined ANP-ELECTRE method. This combined model is of high performance to give a solution to the problem considered in this paper. But given the complex and time consuming nature of the AHP and ELECTRE, in this study a meta-heuristic algorithm is developed called SIMANP that despite the simplicity of computing and high-speed, is good in the terms of precision and efficiency. The results of comparing SIMANP algorithm and the proposed ANP - ELECTRE method are presented at the end.

Supporting ontology-based keyword search over medical databases.

Science.gov (United States)

Kementsietsidis, Anastasios; Lim, Lipyeow; Wang, Min

2008-11-06

The proliferation of medical terms poses a number of challenges in the sharing of medical information among different stakeholders. Ontologies are commonly used to establish relationships between different terms, yet their role in querying has not been investigated in detail. In this paper, we study the problem of supporting ontology-based keyword search queries on a database of electronic medical records. We present several approaches to support this type of queries, study the advantages and limitations of each approach, and summarize the lessons learned as best practices.

New Architectures for Presenting Search Results Based on Web Search Engines Users Experience

Science.gov (United States)

Martinez, F. J.; Pastor, J. A.; Rodriguez, J. V.; Lopez, Rosana; Rodriguez, J. V., Jr.

2011-01-01

Introduction: The Internet is a dynamic environment which is continuously being updated. Search engines have been, currently are and in all probability will continue to be the most popular systems in this information cosmos. Method: In this work, special attention has been paid to the series of changes made to search engines up to this point,…

Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy.

Science.gov (United States)

Cai, Yi; Li, Qing; Xie, Haoran; Min, Huaqin

2014-10-01

With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hidden policy ciphertext-policy attribute-based encryption with keyword search against keyword guessing attack

Institute of Scientific and Technical Information of China (English)

Shuo; QIU; Jiqiang; LIU; Yanfeng; SHI; Rui; ZHANG

2017-01-01

Attribute-based encryption with keyword search(ABKS) enables data owners to grant their search capabilities to other users by enforcing an access control policy over the outsourced encrypted data. However,existing ABKS schemes cannot guarantee the privacy of the access structures, which may contain some sensitive private information. Furthermore, resulting from the exposure of the access structures, ABKS schemes are susceptible to an off-line keyword guessing attack if the keyword space has a polynomial size. To solve these problems, we propose a novel primitive named hidden policy ciphertext-policy attribute-based encryption with keyword search(HP-CPABKS). With our primitive, the data user is unable to search on encrypted data and learn any information about the access structure if his/her attribute credentials cannot satisfy the access control policy specified by the data owner. We present a rigorous selective security analysis of the proposed HP-CPABKS scheme, which simultaneously keeps the indistinguishability of the keywords and the access structures. Finally,the performance evaluation verifies that our proposed scheme is efficient and practical.

An image-based search for pulsars among Fermi unassociated LAT sources

Science.gov (United States)

Frail, D. A.; Ray, P. S.; Mooley, K. P.; Hancock, P.; Burnett, T. H.; Jagannathan, P.; Ferrara, E. C.; Intema, H. T.; de Gasperin, F.; Demorest, P. B.; Stovall, K.; McKinnon, M. M.

2018-03-01

We describe an image-based method that uses two radio criteria, compactness, and spectral index, to identify promising pulsar candidates among Fermi Large Area Telescope (LAT) unassociated sources. These criteria are applied to those radio sources from the Giant Metrewave Radio Telescope all-sky survey at 150 MHz (TGSS ADR1) found within the error ellipses of unassociated sources from the 3FGL catalogue and a preliminary source list based on 7 yr of LAT data. After follow-up interferometric observations to identify extended or variable sources, a list of 16 compact, steep-spectrum candidates is generated. An ongoing search for pulsations in these candidates, in gamma rays and radio, has found 6 ms pulsars and one normal pulsar. A comparison of this method with existing selection criteria based on gamma-ray spectral and variability properties suggests that the pulsar discovery space using Fermi may be larger than previously thought. Radio imaging is a hitherto underutilized source selection method that can be used, as with other multiwavelength techniques, in the search for Fermi pulsars.

Research on the optimization strategy of web search engine based on data mining

Science.gov (United States)

Chen, Ronghua

2018-04-01

With the wide application of search engines, web site information has become an important way for people to obtain information. People have found that they are growing in an increasingly explosive manner. Web site information is verydifficult to find the information they need, and now the search engine can not meet the need, so there is an urgent need for the network to provide website personalized information service, data mining technology for this new challenge is to find a breakthrough. In order to improve people's accuracy of finding information from websites, a website search engine optimization strategy based on data mining is proposed, and verified by website search engine optimization experiment. The results show that the proposed strategy improves the accuracy of the people to find information, and reduces the time for people to find information. It has an important practical value.

Personalized Metaheuristic Clustering Onto Web Documents

Institute of Scientific and Technical Information of China (English)

Wookey Lee

2004-01-01

Optimal clustering for the web documents is known to complicated cornbinatorial Optimization problem and it is hard to develop a generally applicable oplimal algorithm. An accelerated simuIated arlneaIing aIgorithm is developed for automatic web document classification. The web document classification problem is addressed as the problem of best describing a match between a web query and a hypothesized web object. The normalized term frequency and inverse document frequency coefficient is used as a measure of the match. Test beds are generated on - line during the search by transforming model web sites. As a result, web sites can be clustered optimally in terms of keyword vectofs of corresponding web documents.

Using fuzzy rule-based knowledge model for optimum plating conditions search

Science.gov (United States)

Solovjev, D. S.; Solovjeva, I. A.; Litovka, Yu V.; Arzamastsev, A. A.; Glazkov, V. P.; L’vov, A. A.

2018-03-01

The paper discusses existing approaches to plating process modeling in order to decrease the distribution thickness of plating surface cover. However, these approaches do not take into account the experience, knowledge, and intuition of the decision-makers when searching the optimal conditions of electroplating technological process. The original approach to optimal conditions search for applying the electroplating coatings, which uses the rule-based model of knowledge and allows one to reduce the uneven product thickness distribution, is proposed. The block diagrams of a conventional control system of a galvanic process as well as the system based on the production model of knowledge are considered. It is shown that the fuzzy production model of knowledge in the control system makes it possible to obtain galvanic coatings of a given thickness unevenness with a high degree of adequacy to the experimental data. The described experimental results confirm the theoretical conclusions.

Development of a PubMed Based Search Tool for Identifying Sex and Gender Specific Health Literature.

Science.gov (United States)

Song, Michael M; Simonsen, Cheryl K; Wilson, Joanna D; Jenkins, Marjorie R

2016-02-01

An effective literature search strategy is critical to achieving the aims of Sex and Gender Specific Health (SGSH): to understand sex and gender differences through research and to effectively incorporate the new knowledge into the clinical decision making process to benefit both male and female patients. The goal of this project was to develop and validate an SGSH literature search tool that is readily and freely available to clinical researchers and practitioners. PubMed, a freely available search engine for the Medline database, was selected as the platform to build the SGSH literature search tool. Combinations of Medical Subject Heading terms, text words, and title words were evaluated for optimal specificity and sensitivity. The search tool was then validated against reference bases compiled for two disease states, diabetes and stroke. Key sex and gender terms and limits were bundled to create a search tool to facilitate PubMed SGSH literature searches. During validation, the search tool retrieved 50 of 94 (53.2%) stroke and 62 of 95 (65.3%) diabetes reference articles selected for validation. A general keyword search of stroke or diabetes combined with sex difference retrieved 33 of 94 (35.1%) stroke and 22 of 95 (23.2%) diabetes reference base articles, with lower sensitivity and specificity for SGSH content. The Texas Tech University Health Sciences Center SGSH PubMed Search Tool provides higher sensitivity and specificity to sex and gender specific health literature. The tool will facilitate research, clinical decision-making, and guideline development relevant to SGSH.

Similarity-based search of model organism, disease and drug effect phenotypes

KAUST Repository

Hoehndorf, Robert

2015-02-19

Background: Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity. Results: We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet. Conclusions: Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

Directory of Open Access Journals (Sweden)

Mohammad Dreidy

2017-01-01

Full Text Available Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs. However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP, Binary genetic algorithm (BGA, and Binary particle swarm optimization (BPSO, to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.

Omicseq: a web-based search engine for exploring omics datasets

Science.gov (United States)

Sun, Xiaobo; Pittard, William S.; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng

2017-01-01

Abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. PMID:28402462

Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

Science.gov (United States)

Zhu, Chuang; Tao, Li; Yang, Fan; Lu, Tao; Jia, Huizhu; Xie, Xiaodong

2018-01-01

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.

COORDINATE-BASED META-ANALYTIC SEARCH FOR THE SPM NEUROIMAGING PIPELINE

DEFF Research Database (Denmark)

Wilkowski, Bartlomiej; Szewczyk, Marcin; Rasmussen, Peter Mondrup

2009-01-01

. BredeQuery offers a direct link from SPM5 to the Brede Database coordinate-based search engine. BredeQuery is able to ‘grab’ brain location coordinates from the SPM windows and enter them as a query for the Brede Database. Moreover, results of the query can be displayed in an SPM window and/or exported...

Web-Based Search and Plot System for Nuclear Reaction Data

International Nuclear Information System (INIS)

Otuka, N.; Nakagawa, T.; Fukahori, T.; Katakura, J.; Aikawa, M.; Suda, T.; Naito, K.; Korennov, S.; Arai, K.; Noto, H.; Ohnishi, A.; Kato, K.

2005-01-01

A web-based search and plot system for nuclear reaction data has been developed, covering experimental data in EXFOR format and evaluated data in ENDF format. The system is implemented for Linux OS, with Perl and MySQL used for CGI scripts and the database manager, respectively. Two prototypes for experimental and evaluated data are presented

Proposed parameters for a circular particle accelerator for proton beam therapy obtained by genetic algorithm

International Nuclear Information System (INIS)

Campos, Gustavo L.; Campos, Tarcísio P.R.

2017-01-01

This paper brings to light optimized proposal for a circular particle accelerator for proton beam therapy purposes (named as ACPT). The methodology applied is based on computational metaheuristics based on genetic algorithms (GA) were used to obtain optimized parameters of the equipment. Some fundamental concepts in the metaheuristics developed in Matlab® software will be presented. Four parameters were considered for the proposed modeling for the equipment, being: potential difference, magnetic field, length and radius of the resonant cavity. As result, this article showed optimized parameters for two ACPT, one of them used for ocular radiation therapy, as well some parameters that will allow teletherapy, called in order ACPT - 65 and ACPT - 250, obtained through metaheuristics based in GA. (author)

Proposed parameters for a circular particle accelerator for proton beam therapy obtained by genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Campos, Gustavo L.; Campos, Tarcísio P.R., E-mail: gustavo.lobato@ifmg.edu.br, E-mail: tprcampos@pq.cnpq.br, E-mail: gustavo.lobato@ifmg.edu.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Departamento de Engenharia Nuclear

2017-07-01

This paper brings to light optimized proposal for a circular particle accelerator for proton beam therapy purposes (named as ACPT). The methodology applied is based on computational metaheuristics based on genetic algorithms (GA) were used to obtain optimized parameters of the equipment. Some fundamental concepts in the metaheuristics developed in Matlab® software will be presented. Four parameters were considered for the proposed modeling for the equipment, being: potential difference, magnetic field, length and radius of the resonant cavity. As result, this article showed optimized parameters for two ACPT, one of them used for ocular radiation therapy, as well some parameters that will allow teletherapy, called in order ACPT - 65 and ACPT - 250, obtained through metaheuristics based in GA. (author)

Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Weizhe Zhang

2014-01-01

Full Text Available Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

Pep-3D-Search: a method for B-cell epitope prediction based on mimotope analysis.

Science.gov (United States)

Huang, Yan Xin; Bao, Yong Li; Guo, Shu Yan; Wang, Yan; Zhou, Chun Guang; Li, Yu Xin

2008-12-16

The prediction of conformational B-cell epitopes is one of the most important goals in immunoinformatics. The solution to this problem, even if approximate, would help in designing experiments to precisely map the residues of interaction between an antigen and an antibody. Consequently, this area of research has received considerable attention from immunologists, structural biologists and computational biologists. Phage-displayed random peptide libraries are powerful tools used to obtain mimotopes that are selected by binding to a given monoclonal antibody (mAb) in a similar way to the native epitope. These mimotopes can be considered as functional epitope mimics. Mimotope analysis based methods can predict not only linear but also conformational epitopes and this has been the focus of much research in recent years. Though some algorithms based on mimotope analysis have been proposed, the precise localization of the interaction site mimicked by the mimotopes is still a challenging task. In this study, we propose a method for B-cell epitope prediction based on mimotope analysis called Pep-3D-Search. Given the 3D structure of an antigen and a set of mimotopes (or a motif sequence derived from the set of mimotopes), Pep-3D-Search can be used in two modes: mimotope or motif. To evaluate the performance of Pep-3D-Search to predict epitopes from a set of mimotopes, 10 epitopes defined by crystallography were compared with the predicted results from a Pep-3D-Search: the average Matthews correlation coefficient (MCC), sensitivity and precision were 0.1758, 0.3642 and 0.6948. Compared with other available prediction algorithms, Pep-3D-Search showed comparable MCC, specificity and precision, and could provide novel, rational results. To verify the capability of Pep-3D-Search to align a motif sequence to a 3D structure for predicting epitopes, 6 test cases were used. The predictive performance of Pep-3D-Search was demonstrated to be superior to that of other similar programs

Solution quality improvement in chiller loading optimization

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

In order to reduce greenhouse gas emission, we can energy-efficiently operate a multiple chiller system using optimization techniques. So far, various optimization techniques have been proposed to the optimal chiller loading problem. Most of those techniques are meta-heuristic algorithms such as genetic algorithm, simulated annealing, and particle swarm optimization. However, this study applied a gradient-based method, named generalized reduced gradient, and then obtains better results when compared with other approaches. When two additional approaches (hybridization between meta-heuristic algorithm and gradient-based algorithm; and reformulation of optimization structure by adding a binary variable which denotes chiller's operating status) were introduced, generalized reduced gradient found even better solutions. - Highlights: → Chiller loading problem is optimized by generalized reduced gradient (GRG) method. → Results are compared with meta-heuristic algorithms such as genetic algorithm. → Results are even enhanced by hybridizing meta-heuristic and gradient techniques. → Results are even enhanced by modifying the optimization formulation.

Improved particle swarm optimization combined with chaos

International Nuclear Information System (INIS)

Liu Bo; Wang Ling; Jin Yihui; Tang Fang; Huang Dexian

2005-01-01

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality

Search Advertising

OpenAIRE

Cornière (de), Alexandre

2016-01-01

Search engines enable advertisers to target consumers based on the query they have entered. In a framework with horizontal product differentiation, imperfect product information and in which consumers incur search costs, I study a game in which advertisers have to choose a price and a set of relevant keywords. The targeting mechanism brings about three kinds of efficiency gains, namely lower search costs, better matching, and more intense product market price-competition. A monopolistic searc...

Power law-based local search in spider monkey optimisation for lower order system modelling

Science.gov (United States)

Sharma, Ajay; Sharma, Harish; Bhargava, Annapurna; Sharma, Nirmala

2017-01-01

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimisation problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modelling using spider monkey optimisation (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely, power law-based local search is incorporated with SMO. The proposed strategy is named as power law-based local search in SMO (PLSMO). The efficiency, accuracy and reliability of the proposed algorithm is tested over 20 well-known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modelling problem.

Searching and Archiving : Exploring Online Search Behaviors of Researchers

NARCIS (Netherlands)

Vyas, Dhaval; de Groot, S.; van der Veer, Gerrit C.; Dainoff, Marvin J.

2007-01-01

Searching for relevant peer-reviewed material is an integral part of corporate and academic researchers. Researchers collect huge amount of information over the years and sometimes struggle organizing it. Based on a study with 30 academic researchers, we explore, in combination, different searching

Content Based Retrieval Database Management System with Support for Similarity Searching and Query Refinement

National Research Council Canada - National Science Library

Ortega-Binderberger, Michael

2002-01-01

... as a critical area of research. This thesis explores how to enhance database systems with content based search over arbitrary abstract data types in a similarity based framework with query refinement...

The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

Directory of Open Access Journals (Sweden)

Wasim A. Hussein

2017-01-01

Full Text Available Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA, on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others.

Contextual Cueing in Multiconjunction Visual Search Is Dependent on Color- and Configuration-Based Intertrial Contingencies

Science.gov (United States)

Geyer, Thomas; Shi, Zhuanghua; Muller, Hermann J.

2010-01-01

Three experiments examined memory-based guidance of visual search using a modified version of the contextual-cueing paradigm (Jiang & Chun, 2001). The target, if present, was a conjunction of color and orientation, with target (and distractor) features randomly varying across trials (multiconjunction search). Under these conditions, reaction times…

Three-dimensional high-precision indoor positioning strategy using Tabu search based on visible light communication

Science.gov (United States)

Peng, Qi; Guan, Weipeng; Wu, Yuxiang; Cai, Ye; Xie, Canyu; Wang, Pengfei

2018-01-01

This paper proposes a three-dimensional (3-D) high-precision indoor positioning strategy using Tabu search based on visible light communication. Tabu search is a powerful global optimization algorithm, and the 3-D indoor positioning can be transformed into an optimal solution problem. Therefore, in the 3-D indoor positioning, the optimal receiver coordinate can be obtained by the Tabu search algorithm. For all we know, this is the first time the Tabu search algorithm is applied to visible light positioning. Each light-emitting diode (LED) in the system broadcasts a unique identity (ID) and transmits the ID information. When the receiver detects optical signals with ID information from different LEDs, using the global optimization of the Tabu search algorithm, the 3-D high-precision indoor positioning can be realized when the fitness value meets certain conditions. Simulation results show that the average positioning error is 0.79 cm, and the maximum error is 5.88 cm. The extended experiment of trajectory tracking also shows that 95.05% positioning errors are below 1.428 cm. It can be concluded from the data that the 3-D indoor positioning based on the Tabu search algorithm achieves the requirements of centimeter level indoor positioning. The algorithm used in indoor positioning is very effective and practical and is superior to other existing methods for visible light indoor positioning.

A New Biobjective Model to Optimize Integrated Redundancy Allocation and Reliability-Centered Maintenance Problems in a System Using Metaheuristics

Directory of Open Access Journals (Sweden)

Shima MohammadZadeh Dogahe

2015-01-01

Full Text Available A novel integrated model is proposed to optimize the redundancy allocation problem (RAP and the reliability-centered maintenance (RCM simultaneously. A system of both repairable and nonrepairable components has been considered. In this system, electronic components are nonrepairable while mechanical components are mostly repairable. For nonrepairable components, a redundancy allocation problem is dealt with to determine optimal redundancy strategy and number of redundant components to be implemented in each subsystem. In addition, a maintenance scheduling problem is considered for repairable components in order to identify the best maintenance policy and optimize system reliability. Both active and cold standby redundancy strategies have been taken into account for electronic components. Also, net present value of the secondary cost including operational and maintenance costs has been calculated. The problem is formulated as a biobjective mathematical programming model aiming to reach a tradeoff between system reliability and cost. Three metaheuristic algorithms are employed to solve the proposed model: Nondominated Sorting Genetic Algorithm (NSGA-II, Multiobjective Particle Swarm Optimization (MOPSO, and Multiobjective Firefly Algorithm (MOFA. Several test problems are solved using the mentioned algorithms to test efficiency and effectiveness of the solution approaches and obtained results are analyzed.

SemantGeo: Powering Ecological and Environment Data Discovery and Search with Standards-Based Geospatial Reasoning

Science.gov (United States)

Seyed, P.; Ashby, B.; Khan, I.; Patton, E. W.; McGuinness, D. L.

2013-12-01

Recent efforts to create and leverage standards for geospatial data specification and inference include the GeoSPARQL standard, Geospatial OWL ontologies (e.g., GAZ, Geonames), and RDF triple stores that support GeoSPARQL (e.g., AllegroGraph, Parliament) that use RDF instance data for geospatial features of interest. However, there remains a gap on how best to fuse software engineering best practices and GeoSPARQL within semantic web applications to enable flexible search driven by geospatial reasoning. In this abstract we introduce the SemantGeo module for the SemantEco framework that helps fill this gap, enabling scientists find data using geospatial semantics and reasoning. SemantGeo provides multiple types of geospatial reasoning for SemantEco modules. The server side implementation uses the Parliament SPARQL Endpoint accessed via a Tomcat servlet. SemantGeo uses the Google Maps API for user-specified polygon construction and JsTree for providing containment and categorical hierarchies for search. SemantGeo uses GeoSPARQL for spatial reasoning alone and in concert with RDFS/OWL reasoning capabilities to determine, e.g., what geofeatures are within, partially overlap with, or within a certain distance from, a given polygon. We also leverage qualitative relationships defined by the Gazetteer ontology that are composites of spatial relationships as well as administrative designations or geophysical phenomena. We provide multiple mechanisms for exploring data, such as polygon (map-based) and named-feature (hierarchy-based) selection, that enable flexible search constraints using boolean combination of selections. JsTree-based hierarchical search facets present named features and include a 'part of' hierarchy (e.g., measurement-site-01, Lake George, Adirondack Region, NY State) and type hierarchies (e.g., nodes in the hierarchy for WaterBody, Park, MeasurementSite), depending on the ';axis of choice' option selected. Using GeoSPARQL and aforementioned ontology

Interleaver Optimization using Population-Based Metaheuristics

Czech Academy of Sciences Publication Activity Database

Snášel, V.; Platoš, J.; Krömer, P.; Abraham, A.; Ouddane, N.; Húsek, Dušan

2010-01-01

Roč. 20, č. 5 (2010), s. 591-608 ISSN 1210-0552 R&D Projects: GA ČR GA205/09/1079 Grant - others:GA ČR(CZ) GA102/09/1494 Institutional research plan: CEZ:AV0Z10300504 Keywords : turbo codes * global optimization * genetic algorithms * differential evolution * noisy communication channel Subject RIV: IN - Informatics, Computer Science Impact factor: 0.511, year: 2010

Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE.

Science.gov (United States)

Demelo, Jonathan; Parsons, Paul; Sedig, Kamran

2017-02-02

Diverse users need to search health and medical literature to satisfy open-ended goals such as making evidence-based decisions and updating their knowledge. However, doing so is challenging due to at least two major difficulties: (1) articulating information needs using accurate vocabulary and (2) dealing with large document sets returned from searches. Common search interfaces such as PubMed do not provide adequate support for exploratory search tasks. Our objective was to improve support for exploratory search tasks by combining two strategies in the design of an interactive visual interface by (1) using a formal ontology to help users build domain-specific knowledge and vocabulary and (2) providing multi-stage triaging support to help mitigate the information overload problem. We developed a Web-based tool, Ontology-Driven Visual Search and Triage Interface for MEDLINE (OVERT-MED), to test our design ideas. We implemented a custom searchable index of MEDLINE, which comprises approximately 25 million document citations. We chose a popular biomedical ontology, the Human Phenotype Ontology (HPO), to test our solution to the vocabulary problem. We implemented multistage triaging support in OVERT-MED, with the aid of interactive visualization techniques, to help users deal with large document sets returned from searches. Formative evaluation suggests that the design features in OVERT-MED are helpful in addressing the two major difficulties described above. Using a formal ontology seems to help users articulate their information needs with more accurate vocabulary. In addition, multistage triaging combined with interactive visualizations shows promise in mitigating the information overload problem. Our strategies appear to be valuable in addressing the two major problems in exploratory search. Although we tested OVERT-MED with a particular ontology and document collection, we anticipate that our strategies can be transferred successfully to other contexts. �

Trail-Based Search for Efficient Event Report to Mobile Actors in Wireless Sensor and Actor Networks.

Science.gov (United States)

Xu, Zhezhuang; Liu, Guanglun; Yan, Haotian; Cheng, Bin; Lin, Feilong

2017-10-27

In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor's mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its trail composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an effective footprint is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay.

Agent based simulation on the process of human flesh search-From perspective of knowledge and emotion

Science.gov (United States)

Zhu, Hou; Hu, Bin

2017-03-01

Human flesh search as a new net crowed behavior, on the one hand can help us to find some special information, on the other hand may lead to privacy leaking and offending human right. In order to study the mechanism of human flesh search, this paper proposes a simulation model based on agent-based model and complex networks. The computational experiments show some useful results. Discovered information quantity and involved personal ratio are highly correlated, and most of net citizens will take part in the human flesh search or will not take part in the human flesh search. Knowledge quantity does not influence involved personal ratio, but influences whether HFS can find out the target human. When the knowledge concentrates on hub nodes, the discovered information quantity is either perfect or almost zero. Emotion of net citizens influences both discovered information quantity and involved personal ratio. Concretely, when net citizens are calm to face the search topic, it will be hardly to find out the target; But when net citizens are agitated, the target will be found out easily.

Signature-based global searches at CDF

International Nuclear Information System (INIS)

Hocker, James Andrew

2008-01-01

Data collected in Run II of the Fermilab Tevatron are searched for indications of new electroweak scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with respect to the Standard Model prediction. Gross features of the data, mass bumps, and significant excesses of events with large summed transverse momentum are examined in a model-independent and quasi-model-independent approach. This global search for new physics in over three hundred exclusive final states in 2 fb -1 of p(bar p) collisions at √s = 1.96 TeV reveals no significant indication of physics beyond the Standard Model

A Computational Investigation of Heuristic Algorithms for 2-Edge-Connectivity Augmentation

DEFF Research Database (Denmark)

Bang-Jensen, Jørgen; Chiarandini, Marco; Morling, Peter

2010-01-01

an equivalent set covering   formulation.  The results indicate that exact solutions by means of a   basic integer programming model can be obtained in reasonably short   time even on networks with 800 vertices and around 287,000   edges. Alternatively, an advanced heuristic algorithm based on   subgradient...... programming, simple construction heuristics and   metaheuristics. As part of the design of heuristics, we consider   different neighborhood structures for local search, among which is a very   large scale neighborhood. In all cases, we exploit approaches through   the graph formulation as well as through...

Application of genetic algorithms for parameter estimation in liquid chromatography

International Nuclear Information System (INIS)

Hernandez Torres, Reynier; Irizar Mesa, Mirtha; Tavares Camara, Leoncio Diogenes

2012-01-01

In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography

Gains Based Remedies: the misguided search for a doctrine

Directory of Open Access Journals (Sweden)

Tom Stafford

2016-12-01

Full Text Available ADVANCE ACCESSIn this article Tom Stafford (Paralegal at Clyde & Co LLP examines the phenomenon of “Gains Based Remedies”. These are awards that, unlike classical damage awards which are calculated by reference to the loss suffered by the claimant, correlate to the gain made by the defendant. A couple of common examples include an account of profits for breach of trust claims, or the “disgorgement” damages that were awarded in AG v Blake. These awards are however available for a spectrum of varied wrongs. Their seeming lack of unity has often baffled commentators who have tried to search for an underpinning doctrine. One particularly renowned commentary is that of Professor Edelman’s, who suggests that these wrongs can be understood by being broken down into one of two categories: awards which seek to deter wrongdoing, and awards which reverse a wrongful transfer of value. The purpose of this article is to discuss the flaws of this view of the law, and to suggest that in fact, any search for a doctrinal underpinning to Gains Based Remedies is misguided. The cases in which these awards are granted have only one feature common to all: the claimant’s loss is, for whatever reason, difficult or impossible to assess. For that reason, the courts use the only other measure of the wrong available: the defendant’s gain.

Omicseq: a web-based search engine for exploring omics datasets.

Science.gov (United States)

Sun, Xiaobo; Pittard, William S; Xu, Tianlei; Chen, Li; Zwick, Michael E; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S

2017-07-03

The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Three hybridization models based on local search scheme for job shop scheduling problem

Science.gov (United States)

Balbi Fraga, Tatiana

2015-05-01

This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.