An Improved Crow Search Algorithm Applied to Energy Problems
Directory of Open Access Journals (Sweden)
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.
International Nuclear Information System (INIS)
Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Valavi, K.
2013-01-01
Highlights: • SGHS enhanced the convergence rate of LPO using some improvements in comparison to basic HS and GHS. • SGHS optimization algorithm obtained averagely better fitness relative to basic HS and GHS algorithms. • Upshot of the SGHS implementation in the LPO reveals its flexibility, efficiency and reliability. - Abstract: The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems
An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
Directory of Open Access Journals (Sweden)
Cheng-Yu Yeh
2017-04-01
Full Text Available The adaptive multi-rate wideband (AMR-WB speech codec is widely used in modern mobile communication systems for high speech quality in handheld devices. Nonetheless, a major disadvantage is that vector quantization (VQ of immittance spectral frequency (ISF coefficients takes a considerable computational load in the AMR-WB coding. Accordingly, a binary search space-structured VQ (BSS-VQ algorithm is adopted to efficiently reduce the complexity of ISF quantization in AMR-WB. This search algorithm is done through a fast locating technique combined with lookup tables, such that an input vector is efficiently assigned to a subspace where relatively few codeword searches are required to be executed. In terms of overall search performance, this work is experimentally validated as a superior search algorithm relative to a multiple triangular inequality elimination (MTIE, a TIE with dynamic and intersection mechanisms (DI-TIE, and an equal-average equal-variance equal-norm nearest neighbor search (EEENNS approach. With a full search algorithm as a benchmark for overall search load comparison, this work provides an 87% search load reduction at a threshold of quantization accuracy of 0.96, a figure far beyond 55% in the MTIE, 76% in the EEENNS approach, and 83% in the DI-TIE approach.
A hybrid adaptive large neighborhood search algorithm applied to a lot-sizing problem
DEFF Research Database (Denmark)
Muller, Laurent Flindt; Spoorendonk, Simon
This paper presents a hybrid of a general heuristic framework that has been successfully applied to vehicle routing problems and a general purpose MIP solver. The framework uses local search and an adaptive procedure which choses between a set of large neighborhoods to be searched. A mixed integer...... of a solution and to investigate the feasibility of elements in such a neighborhood. The hybrid heuristic framework is applied to the multi-item capacitated lot sizing problem with dynamic lot sizes, where experiments have been conducted on a series of instances from the literature. On average the heuristic...
Composite Differential Search Algorithm
Directory of Open Access Journals (Sweden)
Bo Liu
2014-01-01
Full Text Available Differential search algorithm (DS is a relatively new evolutionary algorithm inspired by the Brownian-like random-walk movement which is used by an organism to migrate. It has been verified to be more effective than ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES. In this paper, we propose four improved solution search algorithms, namely “DS/rand/1,” “DS/rand/2,” “DS/current to rand/1,” and “DS/current to rand/2” to search the new space and enhance the convergence rate for the global optimization problem. In order to verify the performance of different solution search methods, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, the original algorithm when considering the quality of the solution obtained. However, these schemes cannot still achieve the best solution for all functions. In order to further enhance the convergence rate and the diversity of the algorithm, a composite differential search algorithm (CDS is proposed in this paper. This new algorithm combines three new proposed search schemes including “DS/rand/1,” “DS/rand/2,” and “DS/current to rand/1” with three control parameters using a random method to generate the offspring. Experiment results show that CDS has a faster convergence rate and better search ability based on the 23 benchmark functions.
Searching dependency between algebraic equations: An algorithm applied to automated reasoning
International Nuclear Information System (INIS)
Yang Lu; Zhang Jingzhong
1990-01-01
An efficient computer algorithm is given to decide how many branches of the solution to a system of algebraic also solve another equation. As one of the applications, this can be used in practice to verify a conjecture with hypotheses and conclusion expressed by algebraic equations, despite the variety of reducible or irreducible. (author). 10 refs
Optimal Fungal Space Searching Algorithms.
Asenova, Elitsa; Lin, Hsin-Yu; Fu, Eileen; Nicolau, Dan V; Nicolau, Dan V
2016-10-01
Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.
Goldengorin, B.; Ghosh, D.
Maximization of submodular functions on a ground set is a NP-hard combinatorial optimization problem. Data correcting algorithms are among the several algorithms suggested for solving this problem exactly and approximately. From the point of view of Hasse diagrams data correcting algorithms use
Adaptive switching gravitational search algorithm: an attempt to ...
Indian Academy of Sciences (India)
Nor Azlina Ab Aziz
An adaptive gravitational search algorithm (GSA) that switches between synchronous and ... genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). ...... heuristic with applications in applied electromagnetics. Prog.
Searching Process with Raita Algorithm and its Application
Rahim, Robbi; Saleh Ahmar, Ansari; Abdullah, Dahlan; Hartama, Dedy; Napitupulu, Darmawan; Putera Utama Siahaan, Andysah; Hasan Siregar, Muhammad Noor; Nasution, Nurliana; Sundari, Siti; Sriadhi, S.
2018-04-01
Searching is a common process performed by many computer users, Raita algorithm is one algorithm that can be used to match and find information in accordance with the patterns entered. Raita algorithm applied to the file search application using java programming language and the results obtained from the testing process of the file search quickly and with accurate results and support many data types.
Quantum walks and search algorithms
Portugal, Renato
2013-01-01
This book addresses an interesting area of quantum computation called quantum walks, which play an important role in building quantum algorithms, in particular search algorithms. Quantum walks are the quantum analogue of classical random walks. It is known that quantum computers have great power for searching unsorted databases. This power extends to many kinds of searches, particularly to the problem of finding a specific location in a spatial layout, which can be modeled by a graph. The goal is to find a specific node knowing that the particle uses the edges to jump from one node to the next. This book is self-contained with main topics that include: Grover's algorithm, describing its geometrical interpretation and evolution by means of the spectral decomposition of the evolution operater Analytical solutions of quantum walks on important graphs like line, cycles, two-dimensional lattices, and hypercubes using Fourier transforms Quantum walks on generic graphs, describing methods to calculate the limiting d...
Nuclear expert web search and crawler algorithm
International Nuclear Information System (INIS)
Reis, Thiago; Barroso, Antonio C.O.; Baptista, Benedito Filho D.
2013-01-01
In this paper we present preliminary research on web search and crawling algorithm applied specifically to nuclear-related web information. We designed a web-based nuclear-oriented expert system guided by a web crawler algorithm and a neural network able to search and retrieve nuclear-related hyper textual web information in autonomous and massive fashion. Preliminary experimental results shows a retrieval precision of 80% for web pages related to any nuclear theme and a retrieval precision of 72% for web pages related only to nuclear power theme. (author)
Nuclear expert web search and crawler algorithm
Energy Technology Data Exchange (ETDEWEB)
Reis, Thiago; Barroso, Antonio C.O.; Baptista, Benedito Filho D., E-mail: thiagoreis@usp.br, E-mail: barroso@ipen.br, E-mail: bdbfilho@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2013-07-01
In this paper we present preliminary research on web search and crawling algorithm applied specifically to nuclear-related web information. We designed a web-based nuclear-oriented expert system guided by a web crawler algorithm and a neural network able to search and retrieve nuclear-related hyper textual web information in autonomous and massive fashion. Preliminary experimental results shows a retrieval precision of 80% for web pages related to any nuclear theme and a retrieval precision of 72% for web pages related only to nuclear power theme. (author)
A review on quantum search algorithms
Giri, Pulak Ranjan; Korepin, Vladimir E.
2017-12-01
The use of superposition of states in quantum computation, known as quantum parallelism, has significant advantage in terms of speed over the classical computation. It is evident from the early invented quantum algorithms such as Deutsch's algorithm, Deutsch-Jozsa algorithm and its variation as Bernstein-Vazirani algorithm, Simon algorithm, Shor's algorithms, etc. Quantum parallelism also significantly speeds up the database search algorithm, which is important in computer science because it comes as a subroutine in many important algorithms. Quantum database search of Grover achieves the task of finding the target element in an unsorted database in a time quadratically faster than the classical computer. We review Grover's quantum search algorithms for a singe and multiple target elements in a database. The partial search algorithm of Grover and Radhakrishnan and its optimization by Korepin called GRK algorithm are also discussed.
Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms
2017-09-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and
Deconvolution algorithms applied in ultrasonics
International Nuclear Information System (INIS)
Perrot, P.
1993-12-01
In a complete system of acquisition and processing of ultrasonic signals, it is often necessary at one stage to use some processing tools to get rid of the influence of the different elements of that system. By that means, the final quality of the signals in terms of resolution is improved. There are two main characteristics of ultrasonic signals which make this task difficult. Firstly, the signals generated by transducers are very often non-minimum phase. The classical deconvolution algorithms are unable to deal with such characteristics. Secondly, depending on the medium, the shape of the propagating pulse is evolving. The spatial invariance assumption often used in classical deconvolution algorithms is rarely valid. Many classical algorithms, parametric and non-parametric, have been investigated: the Wiener-type, the adaptive predictive techniques, the Oldenburg technique in the frequency domain, the minimum variance deconvolution. All the algorithms have been firstly tested on simulated data. One specific experimental set-up has also been analysed. Simulated and real data has been produced. This set-up demonstrated the interest in applying deconvolution, in terms of the achieved resolution. (author). 32 figs., 29 refs
Modified Parameters of Harmony Search Algorithm for Better Searching
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.
Algebraic Algorithm Design and Local Search
National Research Council Canada - National Science Library
Graham, Robert
1996-01-01
.... Algebraic techniques have been applied successfully to algorithm synthesis by the use of algorithm theories and design tactics, an approach pioneered in the Kestrel Interactive Development System (KIDS...
Hybridizing Evolutionary Algorithms with Opportunistic Local Search
DEFF Research Database (Denmark)
Gießen, Christian
2013-01-01
There is empirical evidence that memetic algorithms (MAs) can outperform plain evolutionary algorithms (EAs). Recently the first runtime analyses have been presented proving the aforementioned conjecture rigorously by investigating Variable-Depth Search, VDS for short (Sudholt, 2008). Sudholt...
An Improved Harmony Search Algorithm for Power Distribution Network Planning
Directory of Open Access Journals (Sweden)
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.
Adiabatic quantum search algorithm for structured problems
International Nuclear Information System (INIS)
Roland, Jeremie; Cerf, Nicolas J.
2003-01-01
The study of quantum computation has been motivated by the hope of finding efficient quantum algorithms for solving classically hard problems. In this context, quantum algorithms by local adiabatic evolution have been shown to solve an unstructured search problem with a quadratic speedup over a classical search, just as Grover's algorithm. In this paper, we study how the structure of the search problem may be exploited to further improve the efficiency of these quantum adiabatic algorithms. We show that by nesting a partial search over a reduced set of variables into a global search, it is possible to devise quantum adiabatic algorithms with a complexity that, although still exponential, grows with a reduced order in the problem size
Searching Algorithms Implemented on Probabilistic Systolic Arrays
Czech Academy of Sciences Publication Activity Database
Kramosil, Ivan
1996-01-01
Roč. 25, č. 1 (1996), s. 7-45 ISSN 0308-1079 R&D Projects: GA ČR GA201/93/0781 Keywords : searching algorithms * probabilistic algorithms * systolic arrays * parallel algorithms Impact factor: 0.214, year: 1996
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.
Quantum random-walk search algorithm
International Nuclear Information System (INIS)
Shenvi, Neil; Whaley, K. Birgitta; Kempe, Julia
2003-01-01
Quantum random walks on graphs have been shown to display many interesting properties, including exponentially fast hitting times when compared with their classical counterparts. However, it is still unclear how to use these novel properties to gain an algorithmic speedup over classical algorithms. In this paper, we present a quantum search algorithm based on the quantum random-walk architecture that provides such a speedup. It will be shown that this algorithm performs an oracle search on a database of N items with O(√(N)) calls to the oracle, yielding a speedup similar to other quantum search algorithms. It appears that the quantum random-walk formulation has considerable flexibility, presenting interesting opportunities for development of other, possibly novel quantum algorithms
Searching Algorithm Using Bayesian Updates
Caudle, Kyle
2010-01-01
In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…
Search algorithms, hidden labour and information control
Directory of Open Access Journals (Sweden)
Paško Bilić
2016-06-01
Full Text Available The paper examines some of the processes of the closely knit relationship between Google’s ideologies of neutrality and objectivity and global market dominance. Neutrality construction comprises an important element sustaining the company’s economic position and is reflected in constant updates, estimates and changes to utility and relevance of search results. Providing a purely technical solution to these issues proves to be increasingly difficult without a human hand in steering algorithmic solutions. Search relevance fluctuates and shifts through continuous tinkering and tweaking of the search algorithm. The company also uses third parties to hire human raters for performing quality assessments of algorithmic updates and adaptations in linguistically and culturally diverse global markets. The adaptation process contradicts the technical foundations of the company and calculations based on the initial Page Rank algorithm. Annual market reports, Google’s Search Quality Rating Guidelines, and reports from media specialising in search engine optimisation business are analysed. The Search Quality Rating Guidelines document provides a rare glimpse into the internal architecture of search algorithms and the notions of utility and relevance which are presented and structured as neutral and objective. Intertwined layers of ideology, hidden labour of human raters, advertising revenues, market dominance and control are discussed throughout the paper.
6. Algorithms for Sorting and Searching
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 3. Algorithms - Algorithms for Sorting and Searching. R K Shyamasundar. Series Article ... Author Affiliations. R K Shyamasundar1. Computer Science Group, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India ...
Learning Search Algorithms: An Educational View
Directory of Open Access Journals (Sweden)
Ales Janota
2014-12-01
Full Text Available Artificial intelligence methods find their practical usage in many applications including maritime industry. The paper concentrates on the methods of uninformed and informed search, potentially usable in solving of complex problems based on the state space representation. The problem of introducing the search algorithms to newcomers has its technical and psychological dimensions. The authors show how it is possible to cope with both of them through design and use of specialized authoring systems. A typical example of searching a path through the maze is used to demonstrate how to test, observe and compare properties of various search strategies. Performance of search methods is evaluated based on the common criteria.
A Novel Self-Adaptive Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
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.
Modified cuckoo search: A new gradient free optimisation algorithm
International Nuclear Information System (INIS)
Walton, S.; Hassan, O.; Morgan, K.; Brown, M.R.
2011-01-01
Highlights: → Modified cuckoo search (MCS) is a new gradient free optimisation algorithm. → MCS shows a high convergence rate, able to outperform other optimisers. → MCS is particularly strong at high dimension objective functions. → MCS performs well when applied to engineering problems. - Abstract: A new robust optimisation algorithm, which can be regarded as a modification of the recently developed cuckoo search, is presented. The modification involves the addition of information exchange between the top eggs, or the best solutions. Standard optimisation benchmarking functions are used to test the effects of these modifications and it is demonstrated that, in most cases, the modified cuckoo search performs as well as, or better than, the standard cuckoo search, a particle swarm optimiser, and a differential evolution strategy. In particular the modified cuckoo search shows a high convergence rate to the true global minimum even at high numbers of dimensions.
A Direct Search Algorithm for Global Optimization
Directory of Open Access Journals (Sweden)
Enrique Baeyens
2016-06-01
Full Text Available A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.
Smoothed Analysis of Local Search Algorithms
Manthey, Bodo; Dehne, Frank; Sack, Jörg-Rüdiger; Stege, Ulrike
2015-01-01
Smoothed analysis is a method for analyzing the performance of algorithms for which classical worst-case analysis fails to explain the performance observed in practice. Smoothed analysis has been applied to explain the performance of a variety of algorithms in the last years. One particular class of
Dynamic Vehicle Routing Using an Improved Variable Neighborhood Search Algorithm
Directory of Open Access Journals (Sweden)
Yingcheng Xu
2013-01-01
Full Text Available In order to effectively solve the dynamic vehicle routing problem with time windows, the mathematical model is established and an improved variable neighborhood search algorithm is proposed. In the algorithm, allocation customers and planning routes for the initial solution are completed by the clustering method. Hybrid operators of insert and exchange are used to achieve the shaking process, the later optimization process is presented to improve the solution space, and the best-improvement strategy is adopted, which make the algorithm can achieve a better balance in the solution quality and running time. The idea of simulated annealing is introduced to take control of the acceptance of new solutions, and the influences of arrival time, distribution of geographical location, and time window range on route selection are analyzed. In the experiment, the proposed algorithm is applied to solve the different sizes' problems of DVRP. Comparing to other algorithms on the results shows that the algorithm is effective and feasible.
Algorithms for Academic Search and Recommendation Systems
DEFF Research Database (Denmark)
Amolochitis, Emmanouil
2014-01-01
are part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. In the third part of the work we present the design of a quantitative association rule mining algorithm. The introduced mining algorithm processes......In this work we present novel algorithms for academic search, recommendation and association rules mining. In the first part of the work we introduce a novel hierarchical heuristic scheme for re-ranking academic publications. The scheme is based on the hierarchical combination of a custom...... implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. On the second part we describe the design of hybrid recommender ensemble (user, item and content based). The newly introduced algorithms...
THE QUASIPERIODIC AUTOMATED TRANSIT SEARCH ALGORITHM
International Nuclear Information System (INIS)
Carter, Joshua A.; Agol, Eric
2013-01-01
We present a new algorithm for detecting transiting extrasolar planets in time-series photometry. The Quasiperiodic Automated Transit Search (QATS) algorithm relaxes the usual assumption of strictly periodic transits by permitting a variable, but bounded, interval between successive transits. We show that this method is capable of detecting transiting planets with significant transit timing variations without any loss of significance— s mearing — as would be incurred with traditional algorithms; however, this is at the cost of a slightly increased stochastic background. The approximate times of transit are standard products of the QATS search. Despite the increased flexibility, we show that QATS has a run-time complexity that is comparable to traditional search codes and is comparably easy to implement. QATS is applicable to data having a nearly uninterrupted, uniform cadence and is therefore well suited to the modern class of space-based transit searches (e.g., Kepler, CoRoT). Applications of QATS include transiting planets in dynamically active multi-planet systems and transiting planets in stellar binary systems.
Efficient algorithm for binary search enhancement | Bennett | Journal ...
African Journals Online (AJOL)
Log in or Register to get access to full text downloads. ... This paper presents an Enhanced Binary Search algorithm that ensures that search is performed if ... search region of the list, therefore enabling search to be performed in reduced time.
A Cooperative Harmony Search Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Gang Li
2014-01-01
Full Text Available Harmony search algorithm (HS is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs and particle swarm optimizers (PSOs. It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.
Transitionless driving on adiabatic search algorithm
Energy Technology Data Exchange (ETDEWEB)
Oh, Sangchul, E-mail: soh@qf.org.qa [Qatar Environment and Energy Research Institute, Qatar Foundation, Doha (Qatar); Kais, Sabre, E-mail: kais@purdue.edu [Qatar Environment and Energy Research Institute, Qatar Foundation, Doha (Qatar); Department of Chemistry, Department of Physics and Birck Nanotechnology Center, Purdue University, West Lafayette, Indiana 47907 (United States)
2014-12-14
We study quantum dynamics of the adiabatic search algorithm with the equivalent two-level system. Its adiabatic and non-adiabatic evolution is studied and visualized as trajectories of Bloch vectors on a Bloch sphere. We find the change in the non-adiabatic transition probability from exponential decay for the short running time to inverse-square decay in asymptotic running time. The scaling of the critical running time is expressed in terms of the Lambert W function. We derive the transitionless driving Hamiltonian for the adiabatic search algorithm, which makes a quantum state follow the adiabatic path. We demonstrate that a uniform transitionless driving Hamiltonian, approximate to the exact time-dependent driving Hamiltonian, can alter the non-adiabatic transition probability from the inverse square decay to the inverse fourth power decay with the running time. This may open up a new but simple way of speeding up adiabatic quantum dynamics.
Generalized Jaynes-Cummings model as a quantum search algorithm
International Nuclear Information System (INIS)
Romanelli, A.
2009-01-01
We propose a continuous time quantum search algorithm using a generalization of the Jaynes-Cummings model. In this model the states of the atom are the elements among which the algorithm realizes the search, exciting resonances between the initial and the searched states. This algorithm behaves like Grover's algorithm; the optimal search time is proportional to the square root of the size of the search set and the probability to find the searched state oscillates periodically in time. In this frame, it is possible to reinterpret the usual Jaynes-Cummings model as a trivial case of the quantum search algorithm.
Oscillating feature subset search algorithm for text categorization
Czech Academy of Sciences Publication Activity Database
Novovičová, Jana; Somol, Petr; Pudil, Pavel
2006-01-01
Roč. 44, č. 4225 (2006), s. 578-587 ISSN 0302-9743 R&D Projects: GA AV ČR IAA2075302; GA MŠk 2C06019 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : text classification * feature selection * oscillating search algorithm * Bhattacharyya distance Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.402, year: 2005
Reactive power planning with FACTS devices using gravitational search algorithm
Directory of Open Access Journals (Sweden)
Biplab Bhattacharyya
2015-09-01
Full Text Available In this paper, Gravitational Search Algorithm (GSA is used as optimization method in reactive power planning using FACTS (Flexible AC transmission system devices. The planning problem is formulated as a single objective optimization problem where the real power loss and bus voltage deviations are minimized under different loading conditions. GSA based optimization algorithm and particle swarm optimization techniques (PSO are applied on IEEE 30 bus system. Results show that GSA can also be a very effective tool for reactive power planning.
Algorithm for shortest path search in Geographic Information Systems by using reduced graphs.
Rodríguez-Puente, Rafael; Lazo-Cortés, Manuel S
2013-01-01
The use of Geographic Information Systems has increased considerably since the eighties and nineties. As one of their most demanding applications we can mention shortest paths search. Several studies about shortest path search show the feasibility of using graphs for this purpose. Dijkstra's algorithm is one of the classic shortest path search algorithms. This algorithm is not well suited for shortest path search in large graphs. This is the reason why various modifications to Dijkstra's algorithm have been proposed by several authors using heuristics to reduce the run time of shortest path search. One of the most used heuristic algorithms is the A* algorithm, the main goal is to reduce the run time by reducing the search space. This article proposes a modification of Dijkstra's shortest path search algorithm in reduced graphs. It shows that the cost of the path found in this work, is equal to the cost of the path found using Dijkstra's algorithm in the original graph. The results of finding the shortest path, applying the proposed algorithm, Dijkstra's algorithm and A* algorithm, are compared. This comparison shows that, by applying the approach proposed, it is possible to obtain the optimal path in a similar or even in less time than when using heuristic algorithms.
Quantum-circuit model of Hamiltonian search algorithms
International Nuclear Information System (INIS)
Roland, Jeremie; Cerf, Nicolas J.
2003-01-01
We analyze three different quantum search algorithms, namely, the traditional circuit-based Grover's algorithm, its continuous-time analog by Hamiltonian evolution, and the quantum search by local adiabatic evolution. We show that these algorithms are closely related in the sense that they all perform a rotation, at a constant angular velocity, from a uniform superposition of all states to the solution state. This makes it possible to implement the two Hamiltonian-evolution algorithms on a conventional quantum circuit, while keeping the quadratic speedup of Grover's original algorithm. It also clarifies the link between the adiabatic search algorithm and Grover's algorithm
Genetic algorithms applied to nuclear reactor design optimization
International Nuclear Information System (INIS)
Pereira, C.M.N.A.; Schirru, R.; Martinez, A.S.
2000-01-01
A genetic algorithm is a powerful search technique that simulates natural evolution in order to fit a population of computational structures to the solution of an optimization problem. This technique presents several advantages over classical ones such as linear programming based techniques, often used in nuclear engineering optimization problems. However, genetic algorithms demand some extra computational cost. Nowadays, due to the fast computers available, the use of genetic algorithms has increased and its practical application has become a reality. In nuclear engineering there are many difficult optimization problems related to nuclear reactor design. Genetic algorithm is a suitable technique to face such kind of problems. This chapter presents applications of genetic algorithms for nuclear reactor core design optimization. A genetic algorithm has been designed to optimize the nuclear reactor cell parameters, such as array pitch, isotopic enrichment, dimensions and cells materials. Some advantages of this genetic algorithm implementation over a classical method based on linear programming are revealed through the application of both techniques to a simple optimization problem. In order to emphasize the suitability of genetic algorithms for design optimization, the technique was successfully applied to a more complex problem, where the classical method is not suitable. Results and comments about the applications are also presented. (orig.)
Effects of a random noisy oracle on search algorithm complexity
International Nuclear Information System (INIS)
Shenvi, Neil; Brown, Kenneth R.; Whaley, K. Birgitta
2003-01-01
Grover's algorithm provides a quadratic speed-up over classical algorithms for unstructured database or library searches. This paper examines the robustness of Grover's search algorithm to a random phase error in the oracle and analyzes the complexity of the search process as a function of the scaling of the oracle error with database or library size. Both the discrete- and continuous-time implementations of the search algorithm are investigated. It is shown that unless the oracle phase error scales as O(N -1/4 ), neither the discrete- nor the continuous-time implementation of Grover's algorithm is scalably robust to this error in the absence of error correction
Wolf Search Algorithm for Solving Optimal Reactive Power Dispatch Problem
Directory of Open Access Journals (Sweden)
Kanagasabai Lenin
2015-03-01
Full Text Available This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio – inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm .The proposed (WSA algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm .
A Harmony Search Algorithm approach for optimizing traffic signal timings
Directory of Open Access Journals (Sweden)
Mauro Dell'Orco
2013-07-01
Full Text Available In this study, a bi-level formulation is presented for solving the Equilibrium Network Design Problem (ENDP. The optimisation of the signal timing has been carried out at the upper-level using the Harmony Search Algorithm (HSA, whilst the traffic assignment has been carried out through the Path Flow Estimator (PFE at the lower level. The results of HSA have been first compared with those obtained using the Genetic Algorithm, and the Hill Climbing on a two-junction network for a fixed set of link flows. Secondly, the HSA with PFE has been applied to the medium-sized network to show the applicability of the proposed algorithm in solving the ENDP. Additionally, in order to test the sensitivity of perceived travel time error, we have used the HSA with PFE with various level of perceived travel time. The results showed that the proposed method is quite simple and efficient in solving the ENDP.
Ant colony search algorithm for optimal reactive power optimization
Directory of Open Access Journals (Sweden)
Lenin K.
2006-01-01
Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.
A hybrid search algorithm for swarm robots searching in an unknown environment.
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
2014-01-01
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
National Research Council Canada - National Science Library
Abramson, Mark A; Audet, Charles; Dennis, Jr, J. E
2004-01-01
.... This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints...
Progressive-Search Algorithms for Large-Vocabulary Speech Recognition
National Research Council Canada - National Science Library
Murveit, Hy; Butzberger, John; Digalakis, Vassilios; Weintraub, Mitch
1993-01-01
.... An algorithm, the "Forward-Backward Word-Life Algorithm," is described. It can generate a word lattice in a progressive search that would be used as a language model embedded in a succeeding recognition pass to reduce computation requirements...
Directory of Open Access Journals (Sweden)
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.
International Nuclear Information System (INIS)
Pothiya, Saravuth; Ngamroo, Issarachai; Kongprawechnon, Waree
2008-01-01
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches
Improved Degree Search Algorithms in Unstructured P2P Networks
Directory of Open Access Journals (Sweden)
Guole Liu
2012-01-01
Full Text Available Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P networks. Breadth-first search (BFS and depth-first search (DFS are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD and memory function preference degree algorithm (PD. We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.
Phase matching in quantum searching and the improved Grover algorithm
International Nuclear Information System (INIS)
Long Guilu; Li Yansong; Xiao Li; Tu Changcun; Sun Yang
2004-01-01
The authors briefly introduced some of our recent work related to the phase matching condition in quantum searching algorithms and the improved Grover algorithm. When one replaces the two phase inversions in the Grover algorithm with arbitrary phase rotations, the modified algorithm usually fails in searching the marked state unless a phase matching condition is satisfied between the two phases. the Grover algorithm is not 100% in success rate, an improved Grover algorithm with zero-failure rate is given by replacing the phase inversions with angles that depends on the size of the database. Other aspects of the Grover algorithm such as the SO(3) picture of quantum searching, the dominant gate imperfections in the Grover algorithm are also mentioned. (author)
Multiobjective Genetic Algorithm applied to dengue control.
Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F
2014-12-01
Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. Copyright © 2014 Elsevier Inc. All rights reserved.
2nd International Conference on Harmony Search Algorithm
Geem, Zong
2016-01-01
The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community. This volume, the proceedings of the 2nd International Conference on Harmony Search Algorithm 2015 (ICHSA 2015), brings together contributions describing the latest developments in the field of soft computing with a special focus on HSA techniques. It includes coverage of new methods that have potentially immense application in various fields. Contributed articles cover aspects of the following topics related to the Harmony Search Algorithm: analytical studies; improved, hybrid and multi-objective variants; parameter tuning; and large-scale applications. The book also contains papers discussing recent advances on the following topics: genetic algorithms; evolutionary strategies; the firefly algorithm and cuckoo search; particle swarm optimization and ant colony optimization; simulated annealing; and local search techniques. This book ...
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.
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)
Cuckoo search and firefly algorithm theory and applications
2014-01-01
Nature-inspired algorithms such as cuckoo search and firefly algorithm have become popular and widely used in recent years in many applications. These algorithms are flexible, efficient and easy to implement. New progress has been made in the last few years, and it is timely to summarize the latest developments of cuckoo search and firefly algorithm and their diverse applications. This book will review both theoretical studies and applications with detailed algorithm analysis, implementation and case studies so that readers can benefit most from this book. Application topics are contributed by many leading experts in the field. Topics include cuckoo search, firefly algorithm, algorithm analysis, feature selection, image processing, travelling salesman problem, neural network, GPU optimization, scheduling, queuing, multi-objective manufacturing optimization, semantic web service, shape optimization, and others. This book can serve as an ideal reference for both graduates and researchers in computer scienc...
Directory of Open Access Journals (Sweden)
Orhan TÜRKBEY
2002-02-01
Full Text Available Memetic algorithms, which use local search techniques, are hybrid structured algorithms like genetic algorithms among evolutionary algorithms. In this study, for Quadratic Assignment Problem (QAP, a memetic structured algorithm using a local search heuristic like 2-opt is developed. Developed in the algorithm, a crossover operator that has not been used before for QAP is applied whereas, Eshelman procedure is used in order to increase thesolution variability. The developed memetic algorithm is applied on test problems taken from QAP-LIB, the results are compared with the present techniques in the literature.
State-of-the-Art Review on Relevance of Genetic Algorithm to Internet Web Search
Directory of Open Access Journals (Sweden)
Kehinde Agbele
2012-01-01
Full Text Available People use search engines to find information they desire with the aim that their information needs will be met. Information retrieval (IR is a field that is concerned primarily with the searching and retrieving of information in the documents and also searching the search engine, online databases, and Internet. Genetic algorithms (GAs are robust, efficient, and optimizated methods in a wide area of search problems motivated by Darwin’s principles of natural selection and survival of the fittest. This paper describes information retrieval systems (IRS components. This paper looks at how GAs can be applied in the field of IR and specifically the relevance of genetic algorithms to internet web search. Finally, from the proposals surveyed it turns out that GA is applied to diverse problem fields of internet web search.
Combinatorial search from algorithms to systems
Hamadi, Youssef
2013-01-01
This book details key techniques in constraint networks, dealing in particular with constraint satisfaction, search, satisfiability, and applications in machine learning and constraint programming. Includes case studies.
Induction Motor Parameter Identification Using a Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Omar Avalos
2016-04-01
Full Text Available The efficient use of electrical energy is a topic that has attracted attention for its environmental consequences. On the other hand, induction motors represent the main component in most industries. They consume the highest energy percentages in industrial facilities. This energy consumption depends on the operation conditions of the induction motor imposed by its internal parameters. Since the internal parameters of an induction motor are not directly measurable, an identification process must be conducted to obtain them. In the identification process, the parameter estimation is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables. Under this approach, the complexity of the optimization problem tends to produce multimodal error surfaces for which their cost functions are significantly difficult to minimize. Several algorithms based on evolutionary computation principles have been successfully applied to identify the optimal parameters of induction motors. However, most of them maintain an important limitation: They frequently obtain sub-optimal solutions as a result of an improper equilibrium between exploitation and exploration in their search strategies. This paper presents an algorithm for the optimal parameter identification of induction motors. To determine the parameters, the proposed method uses a recent evolutionary method called the gravitational search algorithm (GSA. Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions. Numerical simulations have been conducted on several models to show the effectiveness of the proposed scheme.
Conditionally-uniform Feasible Grid Search Algorithm
DEFF Research Database (Denmark)
Dziubinski, Matt P.
We present and evaluate a numerical optimization method (together with an algorithm for choosing the starting values) pertinent to the constrained optimization problem arising in the estimation of the GARCH models with inequality constraints, in particular the Simplied Component GARCH Model...... (SCGARCH), together with algorithms for the objective function and analytical gradient computation for SCGARCH....
Quantum algorithms for the ordered search problem via semidefinite programming
International Nuclear Information System (INIS)
Childs, Andrew M.; Landahl, Andrew J.; Parrilo, Pablo A.
2007-01-01
One of the most basic computational problems is the task of finding a desired item in an ordered list of N items. While the best classical algorithm for this problem uses log 2 N queries to the list, a quantum computer can solve the problem using a constant factor fewer queries. However, the precise value of this constant is unknown. By characterizing a class of quantum query algorithms for the ordered search problem in terms of a semidefinite program, we find quantum algorithms for small instances of the ordered search problem. Extending these algorithms to arbitrarily large instances using recursion, we show that there is an exact quantum ordered search algorithm using 4 log 605 N≅0.433 log 2 N queries, which improves upon the previously best known exact algorithm
Decoherence in optimized quantum random-walk search algorithm
International Nuclear Information System (INIS)
Zhang Yu-Chao; Bao Wan-Su; Wang Xiang; Fu Xiang-Qun
2015-01-01
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative. (paper)
A Fuzzy Gravitational Search Algorithm to Design Optimal IIR Filters
Directory of Open Access Journals (Sweden)
Danilo Pelusi
2018-03-01
Full Text Available The goodness of Infinite Impulse Response (IIR digital filters design depends on pass band ripple, stop band ripple and transition band values. The main problem is defining a suitable error fitness function that depends on these parameters. This fitness function can be optimized by search algorithms such as evolutionary algorithms. This paper proposes an intelligent algorithm for the design of optimal 8th order IIR filters. The main contribution is the design of Fuzzy Inference Systems able to tune key parameters of a revisited version of the Gravitational Search Algorithm (GSA. In this way, a Fuzzy Gravitational Search Algorithm (FGSA is designed. The optimization performances of FGSA are compared with those of Differential Evolution (DE and GSA. The results show that FGSA is the algorithm that gives the best compromise between goodness, robustness and convergence rate for the design of 8th order IIR filters. Moreover, FGSA assures a good stability of the designed filters.
Merged Search Algorithms for Radio Frequency Identification Anticollision
Directory of Open Access Journals (Sweden)
Bih-Yaw Shih
2012-01-01
The arbitration algorithm for RFID system is used to arbitrate all the tags to avoid the collision problem with the existence of multiple tags in the interrogation field of a transponder. A splitting algorithm which is called Binary Search Tree (BST is well known for multitags arbitration. In the current study, a splitting-based schema called Merged Search Tree is proposed to capture identification codes correctly for anticollision. Performance of the proposed algorithm is compared with the original BST according to time and power consumed during the arbitration process. The results show that the proposed model can reduce searching time and power consumed to achieve a better performance arbitration.
Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
Directory of Open Access Journals (Sweden)
Yu Feng
2017-01-01
Full Text Available This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
Genetic algorithms applied to nonlinear and complex domains; TOPICAL
International Nuclear Information System (INIS)
Barash, D; Woodin, A E
1999-01-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means
Genetic algorithms applied to nonlinear and complex domains
International Nuclear Information System (INIS)
Barash, D; Woodin, A E
1999-01-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means
HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...
Journal of Applied Biosciences: Advanced Search
African Journals Online (AJOL)
Search tips: Search terms are case-insensitive; Common words are ignored; By default only articles containing all terms in the query are returned (i.e., AND is implied); Combine multiple words with OR to find articles containing either term; e.g., education OR research; Use parentheses to create more complex queries; e.g., ...
The primary advantage of Dynamically Dimensioned Search algorithm (DDS) is that it outperforms many other optimization techniques in both convergence speed and the ability in searching for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation f...
Differential harmony search algorithm to optimize PWRs loading pattern
Energy Technology Data Exchange (ETDEWEB)
Poursalehi, N., E-mail: npsalehi@yahoo.com [Engineering Department, Shahid Beheshti University, G.C, P.O.Box: 1983963113, Tehran (Iran, Islamic Republic of); Zolfaghari, A.; Minuchehr, A. [Engineering Department, Shahid Beheshti University, G.C, P.O.Box: 1983963113, Tehran (Iran, Islamic Republic of)
2013-04-15
Highlights: ► Exploit of DHS algorithm in LP optimization reveals its flexibility, robustness and reliability. ► Upshot of our experiments with DHS shows that the search approach to optimal LP is quickly. ► On the average, the final band width of DHS fitness values is narrow relative to HS and GHS. -- Abstract: The objective of this work is to develop a core loading optimization technique using differential harmony search algorithm in the context of obtaining an optimal configuration of fuel assemblies in pressurized water reactors. To implement and evaluate the proposed technique, differential harmony search nodal expansion package for 2-D geometry, DHSNEP-2D, is developed. The package includes two modules; in the first modules differential harmony search (DHS) is implemented and nodal expansion code which solves two dimensional-multi group neutron diffusion equations using fourth degree flux expansion with one node per a fuel assembly is in the second module. For evaluation of DHS algorithm, classical harmony search (HS) and global-best harmony search (GHS) algorithms are also included in DHSNEP-2D in order to compare the outcome of techniques together. For this purpose, two PWR test cases have been investigated to demonstrate the DHS algorithm capability in obtaining near optimal loading pattern. Results show that the convergence rate of DHS and execution times are quite promising and also is reliable for the fuel management operation. Moreover, numerical results show the good performance of DHS relative to other competitive algorithms such as genetic algorithm (GA), classical harmony search (HS) and global-best harmony search (GHS) algorithms.
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)
Differential harmony search algorithm to optimize PWRs loading pattern
International Nuclear Information System (INIS)
Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.
2013-01-01
Highlights: ► Exploit of DHS algorithm in LP optimization reveals its flexibility, robustness and reliability. ► Upshot of our experiments with DHS shows that the search approach to optimal LP is quickly. ► On the average, the final band width of DHS fitness values is narrow relative to HS and GHS. -- Abstract: The objective of this work is to develop a core loading optimization technique using differential harmony search algorithm in the context of obtaining an optimal configuration of fuel assemblies in pressurized water reactors. To implement and evaluate the proposed technique, differential harmony search nodal expansion package for 2-D geometry, DHSNEP-2D, is developed. The package includes two modules; in the first modules differential harmony search (DHS) is implemented and nodal expansion code which solves two dimensional-multi group neutron diffusion equations using fourth degree flux expansion with one node per a fuel assembly is in the second module. For evaluation of DHS algorithm, classical harmony search (HS) and global-best harmony search (GHS) algorithms are also included in DHSNEP-2D in order to compare the outcome of techniques together. For this purpose, two PWR test cases have been investigated to demonstrate the DHS algorithm capability in obtaining near optimal loading pattern. Results show that the convergence rate of DHS and execution times are quite promising and also is reliable for the fuel management operation. Moreover, numerical results show the good performance of DHS relative to other competitive algorithms such as genetic algorithm (GA), classical harmony search (HS) and global-best harmony search (GHS) algorithms
Optimization of search algorithms for a mass spectra library
International Nuclear Information System (INIS)
Domokos, L.; Henneberg, D.; Weimann, B.
1983-01-01
The SISCOM mass spectra library search is mainly an interpretative system producing a ''hit list'' of similar spectra based on six comparison factors. This paper deals with extension of the system; the aim is exact identification (retrieval) of those reference spectra in the SISCOM hit list that correspond to the unknown compounds or components of the mixture. Thus, instead of a similarity measure, a decision (retrieval) function is needed to establish the identity of reference and unknown compounds by comparison of their spectra. To facilitate estimation of the weightings of the different variables in the retrieval function, pattern recognition algorithms were applied. Numerous statistical evaluations of three different library collections were made to check the quality of data bases and to derive appropriate variables for the retrieval function. (Auth.)
A Functional Programming Approach to AI Search Algorithms
Panovics, Janos
2012-01-01
The theory and practice of search algorithms related to state-space represented problems form the major part of the introductory course of Artificial Intelligence at most of the universities and colleges offering a degree in the area of computer science. Students usually meet these algorithms only in some imperative or object-oriented language…
Reasoning about Grover's Quantum Search Algorithm using Probabilistic wp
Butler, M.J.; Hartel, Pieter H.
Grover's search algorithm is designed to be executed on a quantum mechanical computer. In this paper, the probabilistic wp-calculus is used to model and reason about Grover's algorithm. It is demonstrated that the calculus provides a rigorous programming notation for modelling this and other quantum
Nature-inspired novel Cuckoo Search Algorithm for genome
Indian Academy of Sciences (India)
This study aims to produce a novel optimization algorithm, called the Cuckoo Search Algorithm (CS), for solving the genome sequence assembly problem. ... Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore 641 014, India; Department of Information Technology, ...
International Timetabling Competition 2011: An Adaptive Large Neighborhood Search algorithm
DEFF Research Database (Denmark)
Sørensen, Matias; Kristiansen, Simon; Stidsen, Thomas Riis
2012-01-01
An algorithm based on Adaptive Large Neighborhood Search (ALNS) for solving the generalized High School Timetabling problem in XHSTT-format (Post et al (2012a)) is presented. This algorithm was among the nalists of round 2 of the International Timetabling Competition 2011 (ITC2011). For problem...
Improved Harmony Search Algorithm with Chaos for Absolute Value Equation
Directory of Open Access Journals (Sweden)
Shouheng Tuo
2013-11-01
Full Text Available In this paper, an improved harmony search with chaos (HSCH is presented for solving NP-hard absolute value equation (AVE Ax - |x| = b, where A is an arbitrary square matrix whose singular values exceed one. The simulation results in solving some given AVE problems demonstrate that the HSCH algorithm is valid and outperforms the classical HS algorithm (CHS and HS algorithm with differential mutation operator (HSDE.
A novel line segment detection algorithm based on graph search
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).
A New Approximate Chimera Donor Cell Search Algorithm
Holst, Terry L.; Nixon, David (Technical Monitor)
1998-01-01
The objectives of this study were to develop chimera-based full potential methodology which is compatible with overflow (Euler/Navier-Stokes) chimera flow solver and to develop a fast donor cell search algorithm that is compatible with the chimera full potential approach. Results of this work included presenting a new donor cell search algorithm suitable for use with a chimera-based full potential solver. This algorithm was found to be extremely fast and simple producing donor cells as fast as 60,000 per second.
Fault-tolerant search algorithms reliable computation with unreliable information
Cicalese, Ferdinando
2013-01-01
Why a book on fault-tolerant search algorithms? Searching is one of the fundamental problems in computer science. Time and again algorithmic and combinatorial issues originally studied in the context of search find application in the most diverse areas of computer science and discrete mathematics. On the other hand, fault-tolerance is a necessary ingredient of computing. Due to their inherent complexity, information systems are naturally prone to errors, which may appear at any level - as imprecisions in the data, bugs in the software, or transient or permanent hardware failures. This book pr
Improved Bat Algorithm Applied to Multilevel Image Thresholding
Directory of Open Access Journals (Sweden)
Adis Alihodzic
2014-01-01
Full Text Available Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
Computer Algorithms in the Search for Unrelated Stem Cell Donors
Directory of Open Access Journals (Sweden)
David Steiner
2012-01-01
Full Text Available Hematopoietic stem cell transplantation (HSCT is a medical procedure in the field of hematology and oncology, most often performed for patients with certain cancers of the blood or bone marrow. A lot of patients have no suitable HLA-matched donor within their family, so physicians must activate a “donor search process” by interacting with national and international donor registries who will search their databases for adult unrelated donors or cord blood units (CBU. Information and communication technologies play a key role in the donor search process in donor registries both nationally and internationaly. One of the major challenges for donor registry computer systems is the development of a reliable search algorithm. This work discusses the top-down design of such algorithms and current practice. Based on our experience with systems used by several stem cell donor registries, we highlight typical pitfalls in the implementation of an algorithm and underlying data structure.
Umam, M. I. H.; Santosa, B.
2018-04-01
Combinatorial optimization has been frequently used to solve both problems in science, engineering, and commercial applications. One combinatorial problems in the field of transportation is to find a shortest travel route that can be taken from the initial point of departure to point of destination, as well as minimizing travel costs and travel time. When the distance from one (initial) node to another (destination) node is the same with the distance to travel back from destination to initial, this problems known to the Traveling Salesman Problem (TSP), otherwise it call as an Asymmetric Traveling Salesman Problem (ATSP). The most recent optimization techniques is Symbiotic Organisms Search (SOS). This paper discuss how to hybrid the SOS algorithm with variable neighborhoods search (SOS-VNS) that can be applied to solve the ATSP problem. The proposed mechanism to add the variable neighborhoods search as a local search is to generate the better initial solution and then we modify the phase of parasites with adapting mechanism of mutation. After modification, the performance of the algorithm SOS-VNS is evaluated with several data sets and then the results is compared with the best known solution and some algorithm such PSO algorithm and SOS original algorithm. The SOS-VNS algorithm shows better results based on convergence, divergence and computing time.
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.
Directory of Open Access Journals (Sweden)
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.
Adiabatic quantum algorithm for search engine ranking.
Garnerone, Silvano; Zanardi, Paolo; Lidar, Daniel A
2012-06-08
We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web pages. We argue that the main topological feature of the underlying web graph allowing for such a scaling is the out-degree distribution. The top-ranked log(n) entries of the quantum PageRank state can then be estimated with a polynomial quantum speed-up. Moreover, the quantum PageRank state can be used in "q-sampling" protocols for testing properties of distributions, which require exponentially fewer measurements than all classical schemes designed for the same task. This can be used to decide whether to run a classical update of the PageRank.
Grover's quantum search algorithm for an arbitrary initial mixed state
International Nuclear Information System (INIS)
Biham, Eli; Kenigsberg, Dan
2002-01-01
The Grover quantum search algorithm is generalized to deal with an arbitrary mixed initial state. The probability to measure a marked state as a function of time is calculated, and found to depend strongly on the specific initial state. The form of the function, though, remains as it is in the case of initial pure state. We study the role of the von Neumann entropy of the initial state, and show that the entropy cannot be a measure for the usefulness of the algorithm. We give few examples and show that for some extremely mixed initial states (carrying high entropy), the generalized Grover algorithm is considerably faster than any classical algorithm
PWR loading pattern optimization using Harmony Search algorithm
International Nuclear Information System (INIS)
Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.
2013-01-01
Highlights: ► Numerical results reveal that the HS method is reliable. ► The great advantage of HS is significant gain in computational cost. ► On the average, the final band width of search fitness values is narrow. ► Our experiments show that the search approaches the optimal value fast. - Abstract: In this paper a core reloading technique using Harmony Search, HS, is presented in the context of finding an optimal configuration of fuel assemblies, FA, in pressurized water reactors. To implement and evaluate the proposed technique a Harmony Search along Nodal Expansion Code for 2-D geometry, HSNEC2D, is developed to obtain nearly optimal arrangement of fuel assemblies in PWR cores. This code consists of two sections including Harmony Search algorithm and Nodal Expansion modules using fourth degree flux expansion which solves two dimensional-multi group diffusion equations with one node per fuel assembly. Two optimization test problems are investigated to demonstrate the HS algorithm capability in converging to near optimal loading pattern in the fuel management field and other subjects. Results, convergence rate and reliability of the method are quite promising and show the HS algorithm performs very well and is comparable to other competitive algorithms such as Genetic Algorithm and Particle Swarm Intelligence. Furthermore, implementation of nodal expansion technique along HS causes considerable reduction of computational time to process and analysis optimization in the core fuel management problems
Searching for the majority: algorithms of voluntary control.
Directory of Open Access Journals (Sweden)
Jin Fan
Full Text Available Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most important under a given situation; the algorithms underlying such control, however, are often not clear. We investigated possible algorithms of control for the performance of the majority function, in which participants searched for and identified one of two alternative categories (left or right pointing arrows as composing the majority in each stimulus set. We manipulated the amount (set size of 1, 3, and 5 and content (ratio of left and right pointing arrows within a set of the inputs to test competing hypotheses regarding mental operations for information processing. Using a novel measure based on computational load, we found that reaction time was best predicted by a grouping search algorithm as compared to alternative algorithms (i.e., exhaustive or self-terminating search. The grouping search algorithm involves sampling and resampling of the inputs before a decision is reached. These findings highlight the importance of investigating the implications of voluntary control via algorithms of mental operations.
Directory of Open Access Journals (Sweden)
Apurva Samdurkar
2018-06-01
Full Text Available Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS and cross diamond search algorithms (CDS are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time.
Gradient algorithm applied to laboratory quantum control
International Nuclear Information System (INIS)
Roslund, Jonathan; Rabitz, Herschel
2009-01-01
The exploration of a quantum control landscape, which is the physical observable as a function of the control variables, is fundamental for understanding the ability to perform observable optimization in the laboratory. For high control variable dimensions, trajectory-based methods provide a means for performing such systematic explorations by exploiting the measured gradient of the observable with respect to the control variables. This paper presents a practical, robust, easily implemented statistical method for obtaining the gradient on a general quantum control landscape in the presence of noise. In order to demonstrate the method's utility, the experimentally measured gradient is utilized as input in steepest-ascent trajectories on the landscapes of three model quantum control problems: spectrally filtered and integrated second harmonic generation as well as excitation of atomic rubidium. The gradient algorithm achieves efficiency gains of up to approximately three times that of the standard genetic algorithm and, as such, is a promising tool for meeting quantum control optimization goals as well as landscape analyses. The landscape trajectories directed by the gradient should aid in the continued investigation and understanding of controlled quantum phenomena.
A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
Directory of Open Access Journals (Sweden)
Cinthia Peraza
2016-10-01
Full Text Available In this paper, a new fuzzy harmony search algorithm (FHS for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR and pitch adjustment (PArate parameters that improve the convergence rate of traditional harmony search algorithm (HS. The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.
Energy Technology Data Exchange (ETDEWEB)
Chaoshun Li; Jianzhong Zhou [College of Hydroelectric Digitization Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)
2011-01-15
Parameter identification of hydraulic turbine governing system (HTGS) is crucial in precise modeling of hydropower plant and provides support for the analysis of stability of power system. In this paper, a newly developed optimization algorithm, called gravitational search algorithm (GSA), is introduced and applied in parameter identification of HTGS, and the GSA is improved by combination of the search strategy of particle swarm optimization. Furthermore, a new weighted objective function is proposed in the identification frame. The improved gravitational search algorithm (IGSA), together with genetic algorithm, particle swarm optimization and GSA, is employed in parameter identification experiments and the procedure is validated by comparing experimental and simulated results. Consequently, IGSA is shown to locate more precise parameter values than the compared methods with higher efficiency. (author)
International Nuclear Information System (INIS)
Li Chaoshun; Zhou Jianzhong
2011-01-01
Parameter identification of hydraulic turbine governing system (HTGS) is crucial in precise modeling of hydropower plant and provides support for the analysis of stability of power system. In this paper, a newly developed optimization algorithm, called gravitational search algorithm (GSA), is introduced and applied in parameter identification of HTGS, and the GSA is improved by combination of the search strategy of particle swarm optimization. Furthermore, a new weighted objective function is proposed in the identification frame. The improved gravitational search algorithm (IGSA), together with genetic algorithm, particle swarm optimization and GSA, is employed in parameter identification experiments and the procedure is validated by comparing experimental and simulated results. Consequently, IGSA is shown to locate more precise parameter values than the compared methods with higher efficiency.
Construction Example for Algebra System Using Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
FangAn Deng
2015-01-01
Full Text Available The construction example of algebra system is to verify the existence of a complex algebra system, and it is a NP-hard problem. In this paper, to solve this kind of problems, firstly, a mathematical optimization model for construction example of algebra system is established. Secondly, an improved harmony search algorithm based on NGHS algorithm (INGHS is proposed to find as more solutions as possible for the optimization model; in the proposed INGHS algorithm, to achieve the balance between exploration power and exploitation power in the search process, a global best strategy and parameters dynamic adjustment method are present. Finally, nine construction examples of algebra system are used to evaluate the optimization model and performance of INGHS. The experimental results show that the proposed algorithm has strong performance for solving complex construction example problems of algebra system.
A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution
Directory of Open Access Journals (Sweden)
Lijin Wang
2015-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
Noise propagation in iterative reconstruction algorithms with line searches
International Nuclear Information System (INIS)
Qi, Jinyi
2003-01-01
In this paper we analyze the propagation of noise in iterative image reconstruction algorithms. We derive theoretical expressions for the general form of preconditioned gradient algorithms with line searches. The results are applicable to a wide range of iterative reconstruction problems, such as emission tomography, transmission tomography, and image restoration. A unique contribution of this paper comparing to our previous work [1] is that the line search is explicitly modeled and we do not use the approximation that the gradient of the objective function is zero. As a result, the error in the estimate of noise at early iterations is significantly reduced
Efficient sequential and parallel algorithms for planted motif search.
Nicolae, Marius; Rajasekaran, Sanguthevar
2014-01-31
Motif searching is an important step in the detection of rare events occurring in a set of DNA or protein sequences. One formulation of the problem is known as (l,d)-motif search or Planted Motif Search (PMS). In PMS we are given two integers l and d and n biological sequences. We want to find all sequences of length l that appear in each of the input sequences with at most d mismatches. The PMS problem is NP-complete. PMS algorithms are typically evaluated on certain instances considered challenging. Despite ample research in the area, a considerable performance gap exists because many state of the art algorithms have large runtimes even for moderately challenging instances. This paper presents a fast exact parallel PMS algorithm called PMS8. PMS8 is the first algorithm to solve the challenging (l,d) instances (25,10) and (26,11). PMS8 is also efficient on instances with larger l and d such as (50,21). We include a comparison of PMS8 with several state of the art algorithms on multiple problem instances. This paper also presents necessary and sufficient conditions for 3 l-mers to have a common d-neighbor. The program is freely available at http://engr.uconn.edu/~man09004/PMS8/. We present PMS8, an efficient exact algorithm for Planted Motif Search. PMS8 introduces novel ideas for generating common neighborhoods. We have also implemented a parallel version for this algorithm. PMS8 can solve instances not solved by any previous algorithms.
Stochastic search in structural optimization - Genetic algorithms and simulated annealing
Hajela, Prabhat
1993-01-01
An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.
The quantum walk search algorithm: Factors affecting efficiency
Lovett, Neil B.; Everitt, Matthew; Heath, Robert M.; Kendon, Viv
2011-01-01
We numerically study the quantum walk search algorithm of Shenvi, Kempe and Whaley [PRA \\textbf{67} 052307] and the factors which affect its efficiency in finding an individual state from an unsorted set. Previous work has focused purely on the effects of the dimensionality of the dataset to be searched. Here, we consider the effects of interpolating between dimensions, connectivity of the dataset, and the possibility of disorder in the underlying substrate: all these factors affect the effic...
Bio-inspired algorithms applied to molecular docking simulations.
Heberlé, G; de Azevedo, W F
2011-01-01
Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.
Connectivity algorithm with depth first search (DFS) on simple graphs
Riansanti, O.; Ihsan, M.; Suhaimi, D.
2018-01-01
This paper discusses an algorithm to detect connectivity of a simple graph using Depth First Search (DFS). The DFS implementation in this paper differs than other research, that is, on counting the number of visited vertices. The algorithm obtains s from the number of vertices and visits source vertex, following by its adjacent vertices until the last vertex adjacent to the previous source vertex. Any simple graph is connected if s equals 0 and disconnected if s is greater than 0. The complexity of the algorithm is O(n2).
International Nuclear Information System (INIS)
Chen, Zhihuan; Yuan, Xiaohui; Tian, Hao; Ji, Bin
2014-01-01
Highlights: • We propose an improved gravitational search algorithm (IGSA). • IGSA is applied to parameter identification of water turbine regulation system (WTRS). • WTRS is modeled by considering the impact of turbine speed on torque and water flow. • Weighted objective function strategy is applied to parameter identification of WTRS. - Abstract: Parameter identification of water turbine regulation system (WTRS) is crucial in precise modeling hydropower generating unit (HGU) and provides support for the adaptive control and stability analysis of power system. In this paper, an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for WTRS system under load and no-load running conditions. This newly algorithm which is based on standard gravitational search algorithm (GSA) accelerates convergence speed with combination of the search strategy of particle swarm optimization and elastic-ball method. Chaotic mutation which is devised to stepping out the local optimal with a certain probability is also added into the algorithm to avoid premature. Furthermore, a new kind of model associated to the engineering practices is built and analyzed in the simulation tests. An illustrative example for parameter identification of WTRS is used to verify the feasibility and effectiveness of the proposed IGSA, as compared with standard GSA and particle swarm optimization in terms of parameter identification accuracy and convergence speed. The simulation results show that IGSA performs best for all identification indicators
Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm
Directory of Open Access Journals (Sweden)
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.
Performance of genetic algorithms in search for water splitting perovskites
DEFF Research Database (Denmark)
Jain, A.; Castelli, Ivano Eligio; Hautier, G.
2013-01-01
We examine the performance of genetic algorithms (GAs) in uncovering solar water light splitters over a space of almost 19,000 perovskite materials. The entire search space was previously calculated using density functional theory to determine solutions that fulfill constraints on stability, band...
Nature-inspired novel Cuckoo Search Algorithm for genome ...
Indian Academy of Sciences (India)
compared their results with other methods such as the genetic algorithm. ... It is a population-based search procedure used as an optimization tool, in ... In this section, the problem formulation, fitness evaluation, flowchart and implementation of the ..... Machine Learning 21: 11–33 ... Numerical Optimization 1: 330–343.
Computing gap free Pareto front approximations with stochastic search algorithms.
Schütze, Oliver; Laumanns, Marco; Tantar, Emilia; Coello, Carlos A Coello; Talbi, El-Ghazali
2010-01-01
Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though bounds on the quality of the limit approximation-which are entirely determined by the archiving strategy and the value of epsilon-have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that points f in the Pareto front can exist such that the distance of f to any image point F(a), a epsilon A, is "large." Since such gap free approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategy-multi-objective continuation methods-by showing that the concept of epsilon-dominance can be integrated into this approach in a suitable way.
An Elite Decision Making Harmony Search Algorithm for Optimization Problem
Directory of Open Access Journals (Sweden)
Lipu Zhang
2012-01-01
Full Text Available This paper describes a new variant of harmony search algorithm which is inspired by a well-known item “elite decision making.” In the new algorithm, the good information captured in the current global best and the second best solutions can be well utilized to generate new solutions, following some probability rule. The generated new solution vector replaces the worst solution in the solution set, only if its fitness is better than that of the worst solution. The generating and updating steps and repeated until the near-optimal solution vector is obtained. Extensive computational comparisons are carried out by employing various standard benchmark optimization problems, including continuous design variables and integer variables minimization problems from the literature. The computational results show that the proposed new algorithm is competitive in finding solutions with the state-of-the-art harmony search variants.
Energy Technology Data Exchange (ETDEWEB)
Toda-Caraballo, I.; Garcia-Mateo, C.; Capdevila, C.
2010-07-01
At the beginning of the decade of the nineties, the industrial interest for TRIP steels leads to a significant increase of the investigation and application in this field. In this work, the flexibility of neural networks for the modelling of complex properties is used to tackle the problem of determining the retained austenite content in TRIP-steel. Applying a combination of two learning algorithms (backpropagation and creeping-random-search) for the neural network, a model has been created that enables the prediction of retained austenite in low-Si / low-Al multiphase steels as a function of processing parameters. (Author). 34 refs.
Unrelated Hematopoietic Stem Cell Donor Matching Probability and Search Algorithm
Directory of Open Access Journals (Sweden)
J.-M. Tiercy
2012-01-01
Full Text Available In transplantation of hematopoietic stem cells (HSCs from unrelated donors a high HLA compatibility level decreases the risk of acute graft-versus-host disease and mortality. The diversity of the HLA system at the allelic and haplotypic level and the heterogeneity of HLA typing data of the registered donors render the search process a complex task. This paper summarizes our experience with a search algorithm that includes at the start of the search a probability estimate (high/intermediate/low to identify a HLA-A, B, C, DRB1, DQB1-compatible donor (a 10/10 match. Based on 2002–2011 searches about 30% of patients have a high, 30% an intermediate, and 40% a low probability search. Search success rate and duration are presented and discussed in light of the experience of other centers. Overall a 9-10/10 matched HSC donor can now be identified for 60–80% of patients of European descent. For high probability searches donors can be selected on the basis of DPB1-matching with an estimated success rate of >40%. For low probability searches there is no consensus on which HLA incompatibilities are more permissive, although HLA-DQB1 mismatches are generally considered as acceptable. Models for the discrimination of more detrimental mismatches based on specific amino acid residues rather than specific HLA alleles are presented.
Directory of Open Access Journals (Sweden)
Oguz Emrah Turgut
2014-12-01
Full Text Available This study explores the thermal design of shell and tube heat exchangers by using Improved Intelligent Tuned Harmony Search (I-ITHS algorithm. Intelligent Tuned Harmony Search (ITHS is an upgraded version of harmony search algorithm which has an advantage of deciding intensification and diversification processes by applying proper pitch adjusting strategy. In this study, we aim to improve the search capacity of ITHS algorithm by utilizing chaotic sequences instead of uniformly distributed random numbers and applying alternative search strategies inspired by Artificial Bee Colony algorithm and Opposition Based Learning on promising areas (best solutions. Design variables including baffle spacing, shell diameter, tube outer diameter and number of tube passes are used to minimize total cost of heat exchanger that incorporates capital investment and the sum of discounted annual energy expenditures related to pumping and heat exchanger area. Results show that I-ITHS can be utilized in optimizing shell and tube heat exchangers.
Evolutionary algorithms applied to Landau-gauge fixing
International Nuclear Information System (INIS)
Markham, J.F.
1998-01-01
Current algorithms used to put a lattice gauge configuration into Landau gauge either suffer from the problem of critical slowing-down or involve an additions computational expense to overcome it. Evolutionary Algorithms (EAs), which have been widely applied to other global optimisation problems, may be of use in gauge fixing. Also, being global, they should not suffer from critical slowing-down as do local gradient based algorithms. We apply EA'S and also a Steepest Descent (SD) based method to the problem of Landau Gauge Fixing and compare their performance. (authors)
Moon Search Algorithms for NASA's Dawn Mission to Asteroid Vesta
Memarsadeghi, Nargess; Mcfadden, Lucy A.; Skillman, David R.; McLean, Brian; Mutchler, Max; Carsenty, Uri; Palmer, Eric E.
2012-01-01
A moon or natural satellite is a celestial body that orbits a planetary body such as a planet, dwarf planet, or an asteroid. Scientists seek understanding the origin and evolution of our solar system by studying moons of these bodies. Additionally, searches for satellites of planetary bodies can be important to protect the safety of a spacecraft as it approaches or orbits a planetary body. If a satellite of a celestial body is found, the mass of that body can also be calculated once its orbit is determined. Ensuring the Dawn spacecraft's safety on its mission to the asteroid Vesta primarily motivated the work of Dawn's Satellite Working Group (SWG) in summer of 2011. Dawn mission scientists and engineers utilized various computational tools and techniques for Vesta's satellite search. The objectives of this paper are to 1) introduce the natural satellite search problem, 2) present the computational challenges, approaches, and tools used when addressing this problem, and 3) describe applications of various image processing and computational algorithms for performing satellite searches to the electronic imaging and computer science community. Furthermore, we hope that this communication would enable Dawn mission scientists to improve their satellite search algorithms and tools and be better prepared for performing the same investigation in 2015, when the spacecraft is scheduled to approach and orbit the dwarf planet Ceres.
Implementation of the Grover search algorithm with Josephson charge qubits
International Nuclear Information System (INIS)
Zheng Xiaohu; Dong Ping; Xue Zhengyuan; Cao Zhuoliang
2007-01-01
A scheme of implementing the Grover search algorithm based on Josephson charge qubits has been proposed, which would be a key step to scale more complex quantum algorithms and very important for constructing a real quantum computer via Josephson charge qubits. The present scheme is simple but fairly efficient, and easily manipulated because any two-charge-qubit can be selectively and effectively coupled by a common inductance. More manipulations can be carried out before decoherence sets in. Our scheme can be realized within the current technology
Jiang, Y.; Xing, H. L.
2016-12-01
Micro-seismic events induced by water injection, mining activity or oil/gas extraction are quite informative, the interpretation of which can be applied for the reconstruction of underground stress and monitoring of hydraulic fracturing progress in oil/gas reservoirs. The source characterises and locations are crucial parameters that required for these purposes, which can be obtained through the waveform matching inversion (WMI) method. Therefore it is imperative to develop a WMI algorithm with high accuracy and convergence speed. Heuristic algorithm, as a category of nonlinear method, possesses a very high convergence speed and good capacity to overcome local minimal values, and has been well applied for many areas (e.g. image processing, artificial intelligence). However, its effectiveness for micro-seismic WMI is still poorly investigated; very few literatures exits that addressing this subject. In this research an advanced heuristic algorithm, gravitational search algorithm (GSA) , is proposed to estimate the focal mechanism (angle of strike, dip and rake) and source locations in three dimension. Unlike traditional inversion methods, the heuristic algorithm inversion does not require the approximation of green function. The method directly interacts with a CPU parallelized finite difference forward modelling engine, and updating the model parameters under GSA criterions. The effectiveness of this method is tested with synthetic data form a multi-layered elastic model; the results indicate GSA can be well applied on WMI and has its unique advantages. Keywords: Micro-seismicity, Waveform matching inversion, gravitational search algorithm, parallel computation
The Great Deluge Algorithm 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
The Great Deluge Algorithm (GDA) is a local search algorithm introduced by Dueck. It is an analogy with a flood: the 'water level' rises continuously and the proposed solution must lie above the 'surface' in order to survive. The crucial parameter is the 'rain speed', which controls convergence of the algorithm similarly to Simulated Annealing's annealing schedule. This algorithm is applied to the reactor core design optimization problem, which consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. This problem was previously attacked by the canonical genetic algorithm (GA) and by a Niching Genetic Algorithm (NGA). NGAs were designed to force the genetic algorithm to maintain a heterogeneous population throughout the evolutionary process, avoiding the phenomenon known as genetic drift, where all the individuals converge to a single solution. The results obtained by the Great Deluge Algorithm are compared to those obtained by both algorithms mentioned above. The three algorithms are submitted to the same computational effort and GDA reaches the best results, showing its potential for other applications in the nuclear engineering field as, for instance, the nuclear core reload optimization problem. One of the great advantages of this algorithm over the GA is that it does not require special operators for discrete optimization. (author)
Performance Analysis of Binary Search Algorithm in RFID
Directory of Open Access Journals (Sweden)
Xiangmei SONG
2014-12-01
Full Text Available Binary search algorithm (BS is a kind of important anti-collision algorithm in the Radio Frequency Identification (RFID, is also one of the key technologies which determine whether the information in the tag is identified by the reader-writer fast and reliably. The performance of BS directly affects the quality of service in Internet of Things. This paper adopts an automated formal technology: probabilistic model checking to analyze the performance of BS algorithm formally. Firstly, according to the working principle of BS algorithm, its dynamic behavior is abstracted into a Discrete Time Markov Chains which can describe deterministic, discrete time and the probability selection. And then on the model we calculate the probability of the data sent successfully and the expected time of tags completing the data transmission. Compared to the another typical anti-collision protocol S-ALOHA in RFID, experimental results show that with an increase in the number of tags the BS algorithm has a less space and time consumption, the average number of conflicts increases slower than the S-ALOHA protocol standard, BS algorithm needs fewer expected time to complete the data transmission, and the average speed of the data transmission in BS is as 1.6 times as the S-ALOHA protocol.
3rd International Conference on Harmony Search Algorithm
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...
Concise quantum associative memories with nonlinear search algorithm
International Nuclear Information System (INIS)
Tchapet Njafa, J.P.; Nana Engo, S.G.
2016-01-01
The model of Quantum Associative Memories (QAM) we propose here consists in simplifying and generalizing that of Rigui Zhou et al. [1] which uses the quantum matrix with the binary decision diagram put forth by David Rosenbaum [2] and the Abrams and Lloyd's nonlinear search algorithm [3]. Our model gives the possibility to retrieve one of the sought states in multi-values retrieving scheme when a measurement is done on the first register in O(c-r) time complexity. It is better than Grover's algorithm and its modified form which need O(√((2 n )/(m))) steps when they are used as the retrieval algorithm. n is the number of qubits of the first register and m the number of x values for which f(x) = 1. As the nonlinearity makes the system highly susceptible to the noise, an analysis of the influence of the single qubit noise channels on the Nonlinear Search Algorithm of our model of QAM shows a fidelity of about 0.7 whatever the number of qubits existing in the first register, thus demonstrating the robustness of our model. (copyright 2016 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.
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.
MRS algorithm: a new method for searching myocardial region in SPECT myocardial perfusion images.
He, Yuan-Lie; Tian, Lian-Fang; Chen, Ping; Li, Bin; Mao, Zhong-Yuan
2005-10-01
First, the necessity of automatically segmenting myocardium from myocardial SPECT image is discussed in Section 1. To eliminate the influence of the background, the optimal threshold segmentation method modified for the MRS algorithm is explained in Section 2. Then, the image erosion structure is applied to identify the myocardium region and the liver region. The contour tracing method is introduced to extract the myocardial contour. To locate the centriod of the myocardium, the myocardial centriod searching method is developed. The protocol of the MRS algorithm is summarized in Section 6. The performance of the MRS algorithm is investigated and the conclusion is drawn in Section 7. Finally, the importance of the MRS algorithm and the improvement of the MRS algorithm are discussed.
Ramazani, Saba; Jackson, Delvin L.; Selmic, Rastko R.
2013-05-01
In search and surveillance operations, deploying a team of mobile agents provides a robust solution that has multiple advantages over using a single agent in efficiency and minimizing exploration time. This paper addresses the challenge of identifying a target in a given environment when using a team of mobile agents by proposing a novel method of mapping and movement of agent teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, mobile agents have an efficient travel path while performing searches due to this partitioning approach. Second, we use a team of mobile agents that move in a cooperative manner and utilize the Tabu Random algorithm to search for the target. Due to the ever-increasing use of robotics and Unmanned Aerial Vehicle (UAV) platforms, the field of cooperative multi-agent search has developed many applications recently that would benefit from the use of the approach presented in this work, including: search and rescue operations, surveillance, data collection, and border patrol. In this paper, the increased efficiency of the Tabu Random Search algorithm method in combination with hexagonal partitioning is simulated, analyzed, and advantages of this approach are presented and discussed.
Arc-Search Infeasible Interior-Point Algorithm for Linear Programming
Yang, Yaguang
2014-01-01
Mehrotra's algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra's algorithm. This paper proposes an alternative algorithm, arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by arc-search infeasible interior-point algorithm and Mehrotra's algorithm, that the propo...
National Research Council Canada - National Science Library
Homaifar, Abdollah; Esterline, Albert; Kimiaghalam, Bahram
2005-01-01
The Hybrid Projected Gradient-Evolutionary Search Algorithm (HPGES) algorithm uses a specially designed evolutionary-based global search strategy to efficiently create candidate solutions in the solution space...
Application of Tabu Search Algorithm in Job Shop Scheduling
Directory of Open Access Journals (Sweden)
Betrianis Betrianis
2010-10-01
Full Text Available Tabu Search is one of local search methods which is used to solve the combinatorial optimization problem. This method aimed is to make the searching process of the best solution in a complex combinatorial optimization problem(np hard, ex : job shop scheduling problem, became more effective, in a less computational time but with no guarantee to optimum solution.In this paper, tabu search is used to solve the job shop scheduling problem consists of 3 (three cases, which is ordering package of September, October and November with objective of minimizing makespan (Cmax. For each ordering package, there is a combination for initial solution and tabu list length. These result then compared with 4 (four other methods using basic dispatching rules such as Shortest Processing Time (SPT, Earliest Due Date (EDD, Most Work Remaining (MWKR dan First Come First Served (FCFS. Scheduling used Tabu Search Algorithm is sensitive for variables changes and gives makespan shorter than scheduling used by other four methods.
Car painting process scheduling with harmony search algorithm
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.
Swarm, genetic and evolutionary programming algorithms applied to multiuser detection
Directory of Open Access Journals (Sweden)
Paul Jean Etienne Jeszensky
2005-02-01
Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.
A Sustainable City Planning Algorithm Based on TLBO and Local Search
Zhang, Ke; Lin, Li; Huang, Xuanxuan; Liu, Yiming; Zhang, Yonggang
2017-09-01
Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.
Energy Technology Data Exchange (ETDEWEB)
Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
International Nuclear Information System (INIS)
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-01-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm
Memoryless cooperative graph search based on the simulated annealing algorithm
International Nuclear Information System (INIS)
Hou Jian; Yan Gang-Feng; Fan Zhen
2011-01-01
We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment. (interdisciplinary physics and related areas of science and technology)
A Hybrid Harmony Search Algorithm Approach for Optimal Power Flow
Directory of Open Access Journals (Sweden)
Mimoun YOUNES
2012-08-01
Full Text Available Optimal Power Flow (OPF is one of the main functions of Power system operation. It determines the optimal settings of generating units, bus voltage, transformer tap and shunt elements in Power System with the objective of minimizing total production costs or losses while the system is operating within its security limits. The aim of this paper is to propose a novel methodology (BCGAs-HSA that solves OPF including both active and reactive power dispatch It is based on combining the binary-coded genetic algorithm (BCGAs and the harmony search algorithm (HSA to determine the optimal global solution. This method was tested on the modified IEEE 30 bus test system. The results obtained by this method are compared with those obtained with BCGAs or HSA separately. The results show that the BCGAs-HSA approach can converge to the optimum solution with accuracy compared to those reported recently in the literature.
International Nuclear Information System (INIS)
Li, Chunlong; Zhou, Jianzhong; Lu, Peng; Wang, Chao
2015-01-01
Highlights: • Improved multi-objective gravitational search algorithm. • An elite archive set is proposed to guide evolutionary process. • Neighborhood searching mechanism to improve local search ability. • Adopt chaotic mutation for avoiding premature convergence. • Propose feasible space method to handle hydro plant constrains. - Abstract: With growing concerns about energy and environment, short-term economic environmental hydrothermal scheduling (SEEHS) plays a more and more important role in power system. Because of the two objectives and various constraints, SEEHS is a complex multi-objective optimization problem (MOOP). In order to solve the problem, we propose an improved multi-objective gravitational search algorithm (IMOGSA) in this paper. In IMOGSA, the mass of the agent is redefined by multiple objectives to make it suitable for MOOP. An elite archive set is proposed to keep Pareto optimal solutions and guide evolutionary process. For balancing exploration and exploitation, a neighborhood searching mechanism is presented to cooperate with chaotic mutation. Moreover, a novel method based on feasible space is proposed to handle hydro plant constraints during SEEHS, and a violation adjustment method is adopted to handle power balance constraint. For verifying its effectiveness, the proposed IMOGSA is applied to a hydrothermal system in two different case studies. The simulation results show that IMOGSA has a competitive performance in SEEHS when compared with other established algorithms
Tractable Algorithms for Proximity Search on Large Graphs
2010-07-01
Education never ends, Watson. It is a series of lessons with the greatest for the last. — Sir Arthur Conan Doyle’s Sherlock Holmes . 2.1 Introduction A...Doyle’s Sherlock Holmes . 5.1 Introduction In this thesis, our main goal is to design fast algorithms for proximity search in large graphs. In chapter 3...Conan Doyle’s Sherlock Holmes . In this thesis our main focus is on investigating some useful random walk based prox- imity measures. We have started
Path searching in switching networks using cellular algorithm
Energy Technology Data Exchange (ETDEWEB)
Koczy, L T; Langer, J; Legendi, T
1981-01-01
After a survey of the important statements in the paper A Mathematical Model of Path Searching in General Type Switching Networks (see IBID., vol.25, no.1, p.31-43, 1981) the authors consider the possible implementation for cellular automata of the algorithm introduced there. The cellular field used consists of 5 neighbour 8 state cells. Running times required by a traditional serial processor and by the cellular field, respectively, are compared. By parallel processing this running time can be reduced. 5 references.
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.
Hard Ware Implementation of Diamond Search Algorithm for Motion Estimation and Object Tracking
International Nuclear Information System (INIS)
Hashimaa, S.M.; Mahmoud, I.I.; Elazm, A.A.
2009-01-01
Object tracking is very important task in computer vision. Fast search algorithms emerged as important search technique to achieve real time tracking results. To enhance the performance of these algorithms, we advocate the hardware implementation of such algorithms. Diamond search block matching motion estimation has been proposed recently to reduce the complexity of motion estimation. In this paper we selected the diamond search algorithm (DS) for implementation using FPGA. This is due to its fundamental role in all fast search patterns. The proposed architecture is simulated and synthesized using Xilinix and modelsim soft wares. The results agree with the algorithm implementation in Matlab environment.
Parameterless evolutionary algorithm applied to the nuclear reload problem
International Nuclear Information System (INIS)
Caldas, Gustavo Henrique Flores; Schirru, Roberto
2008-01-01
In this work, an evolutionary algorithm with no parameters called FPBIL (parameter free PBIL) is developed based on PBIL (population-based incremental learning). Moreover, the analysis reveals how the parameters from PBIL can be replaced by self-adaptable mechanisms which appear from the radically different form by which the evolution is processed. Despite the advantages, the FPBIL reveals itself compact and relatively modest in the use of computational resources. The FPBIL is then applied to the nuclear reload problem. The experimental results observed are compared to those of other works and corroborate to affirm the superiority of the new algorithm
Archiving, ordering and searching: search engines, algorithms, databases and deep mediatization
DEFF Research Database (Denmark)
Andersen, Jack
2018-01-01
This article argues that search engines, algorithms, and databases can be considered as a way of understanding deep mediatization (Couldry & Hepp, 2016). They are embedded in a variety of social and cultural practices and as such they change our communicative actions to be shaped by their logic o...... reviewed recent trends in mediatization research, the argument is discussed and unfolded in-between the material and social constructivist-phenomenological interpretations of mediatization. In conclusion, it is discussed how deep this form of mediatization can be taken to be.......This article argues that search engines, algorithms, and databases can be considered as a way of understanding deep mediatization (Couldry & Hepp, 2016). They are embedded in a variety of social and cultural practices and as such they change our communicative actions to be shaped by their logic...
A novel symbiotic organisms search algorithm for congestion management in deregulated environment
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.
Gravitation search algorithm: Application to the optimal IIR filter design
Directory of Open Access Journals (Sweden)
Suman Kumar Saha
2014-01-01
Full Text Available This paper presents a global heuristic search optimization technique known as Gravitation Search Algorithm (GSA for the design of 8th order Infinite Impulse Response (IIR, low pass (LP, high pass (HP, band pass (BP and band stop (BS filters considering various non-linear characteristics of the filter design problems. This paper also adopts a novel fitness function in order to improve the stop band attenuation to a great extent. In GSA, law of gravity and mass interactions among different particles are adopted for handling the non-linear IIR filter design optimization problem. In this optimization technique, searcher agents are the collection of masses and interactions among them are governed by the Newtonian gravity and the laws of motion. The performances of the GSA based IIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA and standard Particle Swarm Optimization (PSO. Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
Categorization and Searching of Color Images Using Mean Shift Algorithm
Directory of Open Access Journals (Sweden)
Prakash PANDEY
2009-07-01
Full Text Available Now a day’s Image Searching is still a challenging problem in content based image retrieval (CBIR system. Most CBIR system operates on all images without pre-sorting the images. The image search result contains many unrelated image. The aim of this research is to propose a new object based indexing system Based on extracting salient region representative from the image, categorizing the image into different types and search images that are similar to given query images.In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique, Dominant objects are obtained by performing region grouping of segmented thumbnails. The category for an image is generated automatically by analyzing the image for the presence of a dominant object. The images in the database are clustered based on region feature similarity using Euclidian distance. Placing an image into a category can help the user to navigate retrieval results more effectively. Extensive experimental results illustrate excellent performance.
Applied economic model development algorithm for electronics company
Directory of Open Access Journals (Sweden)
Mikhailov I.
2017-01-01
Full Text Available The purpose of this paper is to report about received experience in the field of creating the actual methods and algorithms that help to simplify development of applied decision support systems. It reports about an algorithm, which is a result of two years research and have more than one-year practical verification. In a case of testing electronic components, the time of the contract conclusion is crucial point to make the greatest managerial mistake. At this stage, it is difficult to achieve a realistic assessment of time-limit and of wage-fund for future work. The creation of estimating model is possible way to solve this problem. In the article is represented an algorithm for creation of those models. The algorithm is based on example of the analytical model development that serves for amount of work estimation. The paper lists the algorithm’s stages and explains their meanings with participants’ goals. The implementation of the algorithm have made possible twofold acceleration of these models development and fulfilment of management’s requirements. The resulting models have made a significant economic effect. A new set of tasks was identified to be further theoretical study.
Directory of Open Access Journals (Sweden)
Kanjana Charansiriphaisan
2013-01-01
Full Text Available Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
The quadratic speedup in Grover's search algorithm from the entanglement perspective
International Nuclear Information System (INIS)
Rungta, Pranaw
2009-01-01
We show that Grover's algorithm can be described as an iterative change of the bipartite entanglement, which leads to a necessary and sufficient condition for quadratic speedup. This allows us to reestablish, from the entanglement perspective, that Grover's search algorithm is the only optimal pure state search algorithm.
Applying exclusion likelihoods from LHC searches to extended Higgs sectors
International Nuclear Information System (INIS)
Bechtle, Philip; Heinemeyer, Sven; Staal, Oscar; Stefaniak, Tim; Weiglein, Georg
2015-01-01
LHC searches for non-standard Higgs bosons decaying into tau lepton pairs constitute a sensitive experimental probe for physics beyond the Standard Model (BSM), such as supersymmetry (SUSY). Recently, the limits obtained from these searches have been presented by the CMS collaboration in a nearly model-independent fashion - as a narrow resonance model - based on the full 8 TeV dataset. In addition to publishing a 95 % C.L. exclusion limit, the full likelihood information for the narrowresonance model has been released. This provides valuable information that can be incorporated into global BSM fits. We present a simple algorithm that maps an arbitrary model with multiple neutral Higgs bosons onto the narrow resonance model and derives the corresponding value for the exclusion likelihood from the CMS search. This procedure has been implemented into the public computer code HiggsBounds (version 4.2.0 and higher). We validate our implementation by cross-checking against the official CMS exclusion contours in three Higgs benchmark scenarios in the Minimal Supersymmetric Standard Model (MSSM), and find very good agreement. Going beyond validation, we discuss the combined constraints of the ττ search and the rate measurements of the SM-like Higgs at 125 GeV in a recently proposed MSSM benchmark scenario, where the lightest Higgs boson obtains SM-like couplings independently of the decoupling of the heavier Higgs states. Technical details for how to access the likelihood information within HiggsBounds are given in the appendix. The program is available at http:// higgsbounds.hepforge.org. (orig.)
Solving the wind farm layout optimization problem using random search algorithm
DEFF Research Database (Denmark)
Feng, Ju; Shen, Wen Zhong
2015-01-01
, in which better results than the genetic algorithm (GA) and the old version of the RS algorithm are obtained. Second it is applied to the Horns Rev 1 WF, and the optimized layouts obtain a higher power production than its original layout, both for the real scenario and for two constructed scenarios......Wind farm (WF) layout optimization is to find the optimal positions of wind turbines (WTs) inside a WF, so as to maximize and/or minimize a single objective or multiple objectives, while satisfying certain constraints. In this work, a random search (RS) algorithm based on continuous formulation....... In this application, it is also found that in order to get consistent and reliable optimization results, up to 360 or more sectors for wind direction have to be used. Finally, considering the inevitable inter-annual variations in the wind conditions, the robustness of the optimized layouts against wind condition...
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
Kitazono, Jun; Kanai, Ryota; Oizumi, Masafumi
2018-03-01
The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information ($\\Phi$) in the brain is related to the level of consciousness. IIT proposes that to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP). The computational cost for exhaustively searching for the MIP grows exponentially with system size, making it difficult to apply IIT to real neural data. It has been previously shown that if a measure of $\\Phi$ satisfies a mathematical property, submodularity, the MIP can be found in a polynomial order by an optimization algorithm. However, although the first version of $\\Phi$ is submodular, the later versions are not. In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of $\\Phi$ by evaluating the accuracy of the algorithm in simulated data and real neural data. We find that the algorithm identifies the MIP in a nearly perfect manner even for the non-submodular measures. Our results show that the algorithm allows us to measure $\\Phi$ in large systems within a practical amount of time.
Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow.
Zhang, Weilong; Guo, Bingxuan; Li, Ming; Liao, Xuan; Li, Wenzhuo
2018-04-16
Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.
Error tolerance in an NMR implementation of Grover's fixed-point quantum search algorithm
International Nuclear Information System (INIS)
Xiao Li; Jones, Jonathan A.
2005-01-01
We describe an implementation of Grover's fixed-point quantum search algorithm on a nuclear magnetic resonance quantum computer, searching for either one or two matching items in an unsorted database of four items. In this algorithm the target state (an equally weighted superposition of the matching states) is a fixed point of the recursive search operator, so that the algorithm always moves towards the desired state. The effects of systematic errors in the implementation are briefly explored
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)
International Nuclear Information System (INIS)
Park, Tongkyu; Yang, Won Sik; Kim, Sang-Ji
2017-01-01
Highlights: • An enhanced search algorithm for charged fuel enrichment was developed for equilibrium cycle analysis with REBUS-3. • The new search algorithm is not sensitive to the user-specified initial guesses. • The new algorithm reduces the computational time by a factor of 2–3. - Abstract: This paper presents an enhanced search algorithm for the charged fuel enrichment in equilibrium cycle analysis of REBUS-3. The current enrichment search algorithm of REBUS-3 takes a large number of iterations to yield a converged solution or even terminates without a converged solution when the user-specified initial guesses are far from the solution. To resolve the convergence problem and to reduce the computational time, an enhanced search algorithm was developed. The enhanced algorithm is based on the idea of minimizing the number of enrichment estimates by allowing drastic enrichment changes and by optimizing the current search algorithm of REBUS-3. Three equilibrium cycle problems with recycling, without recycling and of high discharge burnup were defined and a series of sensitivity analyses were performed with a wide range of user-specified initial guesses. Test results showed that the enhanced search algorithm is able to produce a converged solution regardless of the initial guesses. In addition, it was able to reduce the number of flux calculations by a factor of 2.9, 1.8, and 1.7 for equilibrium cycle problems with recycling, without recycling, and of high discharge burnup, respectively, compared to the current search algorithm.
International Nuclear Information System (INIS)
Sánchez-Oro, J.; Duarte, A.; Salcedo-Sanz, S.
2016-01-01
Highlights: • The total energy demand in Spain is estimated with a Variable Neighborhood algorithm. • Socio-economic variables are used, and one year ahead prediction horizon is considered. • Improvement of the prediction with an Extreme Learning Machine network is considered. • Experiments are carried out in real data for the case of Spain. - Abstract: Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge.
Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation
Directory of Open Access Journals (Sweden)
S.K. Saha
2015-01-01
Full Text Available This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA and Wavelet Mutation (WM strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM was adopted for the design of an 8th-order infinite impulse response (IIR filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP, high-pass (HP, band-pass (BP and band-stop (BS IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability.
A bio-inspired swarm robot coordination algorithm for multiple target searching
Meng, Yan; Gan, Jing; Desai, Sachi
2008-04-01
The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.
Improved gravitational search algorithm for unit commitment considering uncertainty of wind power
International Nuclear Information System (INIS)
Ji, Bin; Yuan, Xiaohui; Chen, Zhihuan; Tian, Hao
2014-01-01
With increasing wind farm integrations, unit commitment (UC) is more difficult to solve because of the intermittent and fluctuation nature of wind power. In this paper, scenario generation and reduction technique is applied to simulate the impacts of its uncertainty on system operation. And then a model of thermal UC problem with wind power integration (UCW) is established. Combination of quantum-inspired binary gravitational search algorithm (GSA) and scenario analysis method is proposed to solve UCW problem. Meanwhile, heuristic search strategies are used to handle the constraints of thermal unit for each scenario. In addition, a priority list of thermal units based on the weight between average full-load cost and maximal power output is utilized during the optimization process. Moreover, two UC test systems with and without wind power integration are used to verify the feasibility and effectiveness of the proposed method as well as the performance of the algorithm. The results are analyzed in detail, which demonstrate the model and the proposed method is practicable. The comparison with other methods clearly shows that the proposed method has higher efficiency for solving UC problems with and even without wind farm integration. - Highlights: • Impact of wind fluctuation on unit commitment problem (UCW) is investigated. • Quantum-inspired gravitational search algorithm (QBGSA) is used to optimize UC. • A new method combines QBGSA with scenario analysis is proposed to solve UCW. • Heuristic search strategies are applied to handle the constraints of the UCW. • The results verify the proposed method is feasible and efficient for handling UCW
Multi-Objective Optimization of Grillages Applying the Genetic Algorithm
Directory of Open Access Journals (Sweden)
Darius Mačiūnas
2012-01-01
Full Text Available The article analyzes the optimization of grillage-type foundations seeking for the least possible reactive forces in the poles for a given number of poles and for the least possible bending moments of absolute values in the connecting beams of the grillage. Therefore, we suggest using a compromise objective function (to be minimized that consists of the maximum reactive force arising in all poles and the maximum bending moment of the absolute value in connecting beams; both components include the given weights. The variables of task design are pole positions under connecting beams. The optimization task is solved applying the algorithm containing all the initial data of the problem. Reactive forces and bending moments are calculated using an original program (finite element method is applied. This program is integrated into the optimization algorithm using the “black-box” principle. The “black-box” finite element program sends back the corresponding value of the objective function. Numerical experiments revealed the optimal quantity of points to compute bending moments. The obtained results show a certain ratio of weights in the objective function where the contribution of reactive forces and bending moments to the objective function are equivalent. This solution can serve as a pilot project for more detailed design.Article in Lithuanian
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...
Global search in photoelectron diffraction structure determination using genetic algorithms
Energy Technology Data Exchange (ETDEWEB)
Viana, M L [Departamento de Fisica, Icex, UFMG, Belo Horizonte, Minas Gerais (Brazil); Muino, R Diez [Donostia International Physics Center DIPC, Paseo Manuel de Lardizabal 4, 20018 San Sebastian (Spain); Soares, E A [Departamento de Fisica, Icex, UFMG, Belo Horizonte, Minas Gerais (Brazil); Hove, M A Van [Department of Physics and Materials Science, City University of Hong Kong, Hong Kong (China); Carvalho, V E de [Departamento de Fisica, Icex, UFMG, Belo Horizonte, Minas Gerais (Brazil)
2007-11-07
Photoelectron diffraction (PED) is an experimental technique widely used to perform structural determinations of solid surfaces. Similarly to low-energy electron diffraction (LEED), structural determination by PED requires a fitting procedure between the experimental intensities and theoretical results obtained through simulations. Multiple scattering has been shown to be an effective approach for making such simulations. The quality of the fit can be quantified through the so-called R-factor. Therefore, the fitting procedure is, indeed, an R-factor minimization problem. However, the topography of the R-factor as a function of the structural and non-structural surface parameters to be determined is complex, and the task of finding the global minimum becomes tough, particularly for complex structures in which many parameters have to be adjusted. In this work we investigate the applicability of the genetic algorithm (GA) global optimization method to this problem. The GA is based on the evolution of species, and makes use of concepts such as crossover, elitism and mutation to perform the search. We show results of its application in the structural determination of three different systems: the Cu(111) surface through the use of energy-scanned experimental curves; the Ag(110)-c(2 x 2)-Sb system, in which a theory-theory fit was performed; and the Ag(111) surface for which angle-scanned experimental curves were used. We conclude that the GA is a highly efficient method to search for global minima in the optimization of the parameters that best fit the experimental photoelectron diffraction intensities to the theoretical ones.
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.
Directory of Open Access Journals (Sweden)
Muhammad Sulaiman
2018-01-01
Full Text Available This paper presents the solution of directional overcurrent relay (DOCR problems using Simulated Annealing based Symbiotic Organism Search (SASOS. The objective function of the problem is to minimize the sum of the operating times of all primary relays. The DOCR problem is nonlinear and highly constrained with two types of decision variables, namely, the time dial settings (TDS and plug setting (PS. In this paper, three models of the problem are considered, the IEEE 3-bus, 4-bus, and 6-bus, respectively. We have applied SASOS to solve the problem and the obtained results are compared with other algorithms available in the literature.
A Variable Neighborhood Search Algorithm for the Leather Nesting Problem
Directory of Open Access Journals (Sweden)
Cláudio Alves
2012-01-01
Full Text Available The leather nesting problem is a cutting and packing optimization problem that consists in finding the best layout for a set of irregular pieces within a natural leather hide with an irregular surface and contour. In this paper, we address a real application of this problem related to the production of car seats in the automotive industry. The high quality requirements imposed on these products combined with the heterogeneity of the leather hides make the problem very complex to solve in practice. Very few results are reported in the literature for the leather nesting problem. Furthermore, the majority of the approaches impose some additional constraints to the layouts related to the particular application that is considered. In this paper, we describe a variable neighborhood search algorithm for the general leather nesting problem. To evaluate the performance of our approaches, we conducted an extensive set of computational experiments on real instances. The results of these experiments are reported at the end of the paper.
A hardware-oriented concurrent TZ search algorithm for High-Efficiency Video Coding
Doan, Nghia; Kim, Tae Sung; Rhee, Chae Eun; Lee, Hyuk-Jae
2017-12-01
High-Efficiency Video Coding (HEVC) is the latest video coding standard, in which the compression performance is double that of its predecessor, the H.264/AVC standard, while the video quality remains unchanged. In HEVC, the test zone (TZ) search algorithm is widely used for integer motion estimation because it effectively searches the good-quality motion vector with a relatively small amount of computation. However, the complex computation structure of the TZ search algorithm makes it difficult to implement it in the hardware. This paper proposes a new integer motion estimation algorithm which is designed for hardware execution by modifying the conventional TZ search to allow parallel motion estimations of all prediction unit (PU) partitions. The algorithm consists of the three phases of zonal, raster, and refinement searches. At the beginning of each phase, the algorithm obtains the search points required by the original TZ search for all PU partitions in a coding unit (CU). Then, all redundant search points are removed prior to the estimation of the motion costs, and the best search points are then selected for all PUs. Compared to the conventional TZ search algorithm, experimental results show that the proposed algorithm significantly decreases the Bjøntegaard Delta bitrate (BD-BR) by 0.84%, and it also reduces the computational complexity by 54.54%.
Finding people, papers, and posts: Vertical search algorithms and evaluation
Berendsen, R.W.
2015-01-01
There is a growing diversity of information access applications. While general web search has been dominant in the past few decades, a wide variety of so-called vertical search tasks and applications have come to the fore. Vertical search is an often used term for search that targets specific
A novel directional asymmetric sampling search algorithm for fast block-matching motion estimation
Li, Yue-e.; Wang, Qiang
2011-11-01
This paper proposes a novel directional asymmetric sampling search (DASS) algorithm for video compression. Making full use of the error information (block distortions) of the search patterns, eight different direction search patterns are designed for various situations. The strategy of local sampling search is employed for the search of big-motion vector. In order to further speed up the search, early termination strategy is adopted in procedure of DASS. Compared to conventional fast algorithms, the proposed method has the most satisfactory PSNR values for all test sequences.
Directory of Open Access Journals (Sweden)
Debkalpa Goswami
2015-03-01
Full Text Available Ultrasonic machining (USM is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored non-conventional optimization techniques, i.e. gravitational search algorithm (GSA and fireworks algorithm (FWA are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes.
Motion Vector Estimation Using Line-Square Search Block Matching Algorithm for Video Sequences
Directory of Open Access Journals (Sweden)
Guo Bao-long
2004-09-01
Full Text Available Motion estimation and compensation techniques are widely used for video coding applications but the real-time motion estimation is not easily achieved due to its enormous computations. In this paper, a new fast motion estimation algorithm based on line search is presented, in which computation complexity is greatly reduced by using the line search strategy and a parallel search pattern. Moreover, the accurate search is achieved because the small square search pattern is used. It has a best-case scenario of only 9 search points, which is 4 search points less than the diamond search algorithm. Simulation results show that, compared with the previous techniques, the LSPS algorithm significantly reduces the computational requirements for finding motion vectors, and also produces close performance in terms of motion compensation errors.
Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm
International Nuclear Information System (INIS)
Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI
2006-01-01
This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for the refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)
Teaching AI Search Algorithms in a Web-Based Educational System
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…
Searching for continuous gravitational wave signals. The hierarchical Hough transform algorithm
International Nuclear Information System (INIS)
Papa, M.; Schutz, B.F.; Sintes, A.M.
2001-01-01
It is well known that matched filtering techniques cannot be applied for searching extensive parameter space volumes for continuous gravitational wave signals. This is the reason why alternative strategies are being pursued. Hierarchical strategies are best at investigating a large parameter space when there exist computational power constraints. Algorithms of this kind are being implemented by all the groups that are developing software for analyzing the data of the gravitational wave detectors that will come online in the next years. In this talk I will report about the hierarchical Hough transform method that the GEO 600 data analysis team at the Albert Einstein Institute is developing. The three step hierarchical algorithm has been described elsewhere [8]. In this talk I will focus on some of the implementational aspects we are currently concerned with. (author)
Searching for full power control rod patterns in a boiling water reactor using genetic algorithms
Energy Technology Data Exchange (ETDEWEB)
Montes, Jose Luis [Departamento Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Ocoyoacac, Edo. de Mexico (Mexico)]. E-mail: jlmt@nuclear.inin.mx; Ortiz, Juan Jose [Departamento Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Ocoyoacac, Edo. de Mexico (Mexico)]. E-mail: jjortiz@nuclear.inin.mx; Requena, Ignacio [Departamento Ciencias Computacion e I.A. ETSII, Informatica, Universidad de Granada, C. Daniel Saucedo Aranda s/n. 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Perusquia, Raul [Departamento Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Ocoyoacac, Edo. de Mexico (Mexico)]. E-mail: rpc@nuclear.inin.mx
2004-11-01
One of the most important questions related to both safety and economic aspects in a nuclear power reactor operation, is without any doubt its reactivity control. During normal operation of a boiling water reactor, the reactivity control of its core is strongly determined by control rods patterns efficiency. In this paper, GACRP system is proposed based on the concepts of genetic algorithms for full power control rod patterns search. This system was carried out using LVNPP transition cycle characteristics, being applied too to an equilibrium cycle. Several operation scenarios, including core water flow variation throughout the cycle and different target axial power distributions, are considered. Genetic algorithm fitness function includes reactor security parameters, such as MLHGR, MCPR, reactor k{sub eff} and axial power density.
Directory of Open Access Journals (Sweden)
Yan Hong Chen
2016-01-01
Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search
Directory of Open Access Journals (Sweden)
Xingwang Huang
2017-01-01
Full Text Available Binary bat algorithm (BBA is a binary version of the bat algorithm (BA. It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO. Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.
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.
Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie
2015-12-01
The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.
Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm
Salameh Shreem, Salam; Abdullah, Salwani; Nazri, Mohd Zakree Ahmad
2016-04-01
Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper (SU-HSA). Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 (out of 10) new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.
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.
Applying Kitaev's algorithm in an ion trap quantum computer
International Nuclear Information System (INIS)
Travaglione, B.; Milburn, G.J.
2000-01-01
Full text: Kitaev's algorithm is a method of estimating eigenvalues associated with an operator. Shor's factoring algorithm, which enables a quantum computer to crack RSA encryption codes, is a specific example of Kitaev's algorithm. It has been proposed that the algorithm can also be used to generate eigenstates. We extend this proposal for small quantum systems, identifying the conditions under which the algorithm can successfully generate eigenstates. We then propose an implementation scheme based on an ion trap quantum computer. This scheme allows us to illustrate a simple example, in which the algorithm effectively generates eigenstates
Applying an efficient K-nearest neighbor search to forest attribute imputation
Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek
2006-01-01
This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...
Novel search algorithms for a mid-infrared spectral library of cotton contaminants.
Loudermilk, J Brian; Himmelsbach, David S; Barton, Franklin E; de Haseth, James A
2008-06-01
During harvest, a variety of plant based contaminants are collected along with cotton lint. The USDA previously created a mid-infrared, attenuated total reflection (ATR), Fourier transform infrared (FT-IR) spectral library of cotton contaminants for contaminant identification as the contaminants have negative impacts on yarn quality. This library has shown impressive identification rates for extremely similar cellulose based contaminants in cases where the library was representative of the samples searched. When spectra of contaminant samples from crops grown in different geographic locations, seasons, and conditions and measured with a different spectrometer and accessories were searched, identification rates for standard search algorithms decreased significantly. Six standard algorithms were examined: dot product, correlation, sum of absolute values of differences, sum of the square root of the absolute values of differences, sum of absolute values of differences of derivatives, and sum of squared differences of derivatives. Four categories of contaminants derived from cotton plants were considered: leaf, stem, seed coat, and hull. Experiments revealed that the performance of the standard search algorithms depended upon the category of sample being searched and that different algorithms provided complementary information about sample identity. These results indicated that choosing a single standard algorithm to search the library was not possible. Three voting scheme algorithms based on result frequency, result rank, category frequency, or a combination of these factors for the results returned by the standard algorithms were developed and tested for their capability to overcome the unpredictability of the standard algorithms' performances. The group voting scheme search was based on the number of spectra from each category of samples represented in the library returned in the top ten results of the standard algorithms. This group algorithm was able to identify
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.
Algorithm of search and track of static and moving large-scale objects
Directory of Open Access Journals (Sweden)
Kalyaev Anatoly
2017-01-01
Full Text Available We suggest an algorithm for processing of a sequence, which contains images of search and track of static and moving large-scale objects. The possible software implementation of the algorithm, based on multithread CUDA processing, is suggested. Experimental analysis of the suggested algorithm implementation is performed.
An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance
Grivokostopoulou, Foteini; Perikos, Isidoros; Hatzilygeroudis, Ioannis
2017-01-01
In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active…
Continuous firefly algorithm applied to PWR core pattern enhancement
Energy Technology Data Exchange (ETDEWEB)
Poursalehi, N., E-mail: npsalehi@yahoo.com [Engineering Department, Shahid Beheshti University, G.C., P.O. Box 1983963113, Tehran (Iran, Islamic Republic of); Zolfaghari, A.; Minuchehr, A.; Moghaddam, H.K. [Engineering Department, Shahid Beheshti University, G.C., P.O. Box 1983963113, Tehran (Iran, Islamic Republic of)
2013-05-15
Highlights: ► Numerical results indicate the reliability of CFA for the nuclear reactor LPO. ► The major advantages of CFA are its light computational cost and fast convergence. ► Our experiments demonstrate the ability of CFA to obtain the near optimal loading pattern. -- Abstract: In this research, the new meta-heuristic optimization strategy, firefly algorithm, is developed for the nuclear reactor loading pattern optimization problem. Two main goals in reactor core fuel management optimization are maximizing the core multiplication factor (K{sub eff}) in order to extract the maximum cycle energy and minimizing the power peaking factor due to safety constraints. In this work, we define a multi-objective fitness function according to above goals for the core fuel arrangement enhancement. In order to evaluate and demonstrate the ability of continuous firefly algorithm (CFA) to find the near optimal loading pattern, we developed CFA nodal expansion code (CFANEC) for the fuel management operation. This code consists of two main modules including CFA optimization program and a developed core analysis code implementing nodal expansion method to calculate with coarse meshes by dimensions of fuel assemblies. At first, CFA is applied for the Foxholes test case with continuous variables in order to validate CFA and then for KWU PWR using a decoding strategy for discrete variables. Results indicate the efficiency and relatively fast convergence of CFA in obtaining near optimal loading pattern with respect to considered fitness function. At last, our experience with the CFA confirms that the CFA is easy to implement and reliable.
Continuous firefly algorithm applied to PWR core pattern enhancement
International Nuclear Information System (INIS)
Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Moghaddam, H.K.
2013-01-01
Highlights: ► Numerical results indicate the reliability of CFA for the nuclear reactor LPO. ► The major advantages of CFA are its light computational cost and fast convergence. ► Our experiments demonstrate the ability of CFA to obtain the near optimal loading pattern. -- Abstract: In this research, the new meta-heuristic optimization strategy, firefly algorithm, is developed for the nuclear reactor loading pattern optimization problem. Two main goals in reactor core fuel management optimization are maximizing the core multiplication factor (K eff ) in order to extract the maximum cycle energy and minimizing the power peaking factor due to safety constraints. In this work, we define a multi-objective fitness function according to above goals for the core fuel arrangement enhancement. In order to evaluate and demonstrate the ability of continuous firefly algorithm (CFA) to find the near optimal loading pattern, we developed CFA nodal expansion code (CFANEC) for the fuel management operation. This code consists of two main modules including CFA optimization program and a developed core analysis code implementing nodal expansion method to calculate with coarse meshes by dimensions of fuel assemblies. At first, CFA is applied for the Foxholes test case with continuous variables in order to validate CFA and then for KWU PWR using a decoding strategy for discrete variables. Results indicate the efficiency and relatively fast convergence of CFA in obtaining near optimal loading pattern with respect to considered fitness function. At last, our experience with the CFA confirms that the CFA is easy to implement and reliable
Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.
Mei, Gang; Xu, Nengxiong; Xu, Liangliang
2016-01-01
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.
Effects of systematic phase errors on optimized quantum random-walk search algorithm
International Nuclear Information System (INIS)
Zhang Yu-Chao; Bao Wan-Su; Wang Xiang; Fu Xiang-Qun
2015-01-01
This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover’s algorithm. (paper)
Protopopescu, V.; D'Helon, C.; Barhen, J.
2003-06-01
A constant-time solution of the continuous global optimization problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Brüschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed.
International Nuclear Information System (INIS)
Li Qianqian; Jiang Xiaofeng; Zhang Shaohong
2009-01-01
In this study, the age technique, the concepts of relativeness degree and worth function are exploited to improve the performance of genetic algorithm (GA) for PWR loading pattern search. Among them, the age technique endows the algorithm be capable of learning from previous search 'experience' and guides it to do a better search in the vicinity ora local optimal; the introduction of the relativeness degree checks the relativeness of two loading patterns before performing crossover between them, which can significantly reduce the possibility of prematurity of the algorithm; while the application of the worth function makes the algorithm be capable of generating new loading patterns based on the statistics of common features of evaluated good loading patterns. Numerical verification against a loading pattern search benchmark problem ora two-loop reactor demonstrates that the adoption of these techniques is able to significantly enhance the efficiency of the genetic algorithm while improves the quality of the final solution as well. (authors)
International Nuclear Information System (INIS)
Chamoli, Arti; Bhandari, C.M.
2005-01-01
Entanglement plays a crucial role in the efficacy of quantum algorithms. Whereas the role of entanglement is quite obvious and conspicuous in teleportation and superdense coding, it is not so distinct in other situations such as in search algorithm. The starting state in Grover's search algorithm is supposedly a uniform superposition state (not entangled) with a success probability around unity. An operational entanglement measure has been defined and investigated analytically for two qubit states [O. Biham, M.A. Neilsen, T. Osborne, Phys. Rev. A 65 (2002) 062312, Y. Shimoni, D. Shapira, O. Biham, Phys. Rev. A 69 (2004) 062303] seeking a relationship with the success rate of search algorithm. This Letter examines the success rate of search algorithm for various four-qubit states. Analytic expressions for the same have been worked out which can provide the success rate and entanglement measure for certain kinds of four qubit input states
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.
Search and optimization by metaheuristics techniques and algorithms inspired by nature
Du, Ke-Lin
2016-01-01
This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computin...
Applying Biomimetic Algorithms for Extra-Terrestrial Habitat Generation
Birge, Brian
2012-01-01
The objective is to simulate and optimize distributed cooperation among a network of robots tasked with cooperative excavation on an extra-terrestrial surface. Additionally to examine the concept of directed Emergence among a group of limited artificially intelligent agents. Emergence is the concept of achieving complex results from very simple rules or interactions. For example, in a termite mound each individual termite does not carry a blueprint of how to make their home in a global sense, but their interactions based strictly on local desires create a complex superstructure. Leveraging this Emergence concept applied to a simulation of cooperative agents (robots) will allow an examination of the success of non-directed group strategy achieving specific results. Specifically the simulation will be a testbed to evaluate population based robotic exploration and cooperative strategies while leveraging the evolutionary teamwork approach in the face of uncertainty about the environment and partial loss of sensors. Checking against a cost function and 'social' constraints will optimize cooperation when excavating a simulated tunnel. Agents will act locally with non-local results. The rules by which the simulated robots interact will be optimized to the simplest possible for the desired result, leveraging Emergence. Sensor malfunction and line of sight issues will be incorporated into the simulation. This approach falls under Swarm Robotics, a subset of robot control concerned with finding ways to control large groups of robots. Swarm Robotics often contains biologically inspired approaches, research comes from social insect observation but also data from among groups of herding, schooling, and flocking animals. Biomimetic algorithms applied to manned space exploration is the method under consideration for further study.
Genetic local search algorithm for optimization design of diffractive optical elements.
Zhou, G; Chen, Y; Wang, Z; Song, H
1999-07-10
We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE's). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA's whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE's that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.
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.
Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch
Directory of Open Access Journals (Sweden)
M. Karthikeyan
2015-01-01
mutation (DHSPM algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR and pitch adjusting rate (PAR are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.
Numerical Algorithms for Personalized Search in Self-organizing Information Networks
Kamvar, Sep
2010-01-01
This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quad
International Nuclear Information System (INIS)
Lu Dawei; Peng Xinhua; Du Jiangfeng; Zhu Jing; Zou Ping; Yu Yihua; Zhang Shanmin; Chen Qun
2010-01-01
An important quantum search algorithm based on the quantum random walk performs an oracle search on a database of N items with O(√(phN)) calls, yielding a speedup similar to the Grover quantum search algorithm. The algorithm was implemented on a quantum information processor of three-qubit liquid-crystal nuclear magnetic resonance (NMR) in the case of finding 1 out of 4, and the diagonal elements' tomography of all the final density matrices was completed with comprehensible one-dimensional NMR spectra. The experimental results agree well with the theoretical predictions.
Applying Russian Search Engines to market Finnish Corporates
Pankratovs, Vladimirs
2013-01-01
The goal of this thesis work is to provide basic knowledge of Russia-based Search Engines marketing capabilities. After reading this material, the user is able to diverge different kinds of Search Engine Marketing tools and can perform advertising campaigns. This study includes information about the majority of tools available to the user and provides up to date screenshots of Russian Search engines front-end, which can be useful in further work. Study discusses the main principles and ba...
DEFF Research Database (Denmark)
Muller, Laurent Flindt
2009-01-01
We present an application of an Adaptive Large Neighborhood Search (ALNS) algorithm to the Resource-constrained Project Scheduling Problem (RCPSP). The ALNS framework was first proposed by Pisinger and Røpke [19] and can be described as a large neighborhood search algorithm with an adaptive layer......, where a set of destroy/repair neighborhoods compete to modify the current solution in each iteration of the algorithm. Experiments are performed on the wellknown J30, J60 and J120 benchmark instances, which show that the proposed algorithm is competitive and confirms the strength of the ALNS framework...
Directory of Open Access Journals (Sweden)
Anongrit Kangrang
2018-01-01
Full Text Available Optimal rule curves are necessary guidelines in the reservoir operation that have been used to assess performance of any reservoir to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements. This study applied the conditional genetic algorithm (CGA and the conditional tabu search algorithm (CTSA technique to connect with the reservoir simulation model in order to search optimal reservoir rule curves. The Ubolrat Reservoir located in the northeast region of Thailand was an illustrative application including historic monthly inflow, future inflow generated by the SWAT hydrological model using 50-year future climate data from the PRECIS regional climate model in case of B2 emission scenario by IPCC SRES, water demand, hydrologic data, and physical reservoir data. The future and synthetic inflow data of reservoirs were used to simulate reservoir system for evaluating water situation. The situations of water shortage and excess water were shown in terms of frequency magnitude and duration. The results have shown that the optimal rule curves from CGA and CTSA connected with the simulation model can mitigate drought and flood situations than the existing rule curves. The optimal future rule curves were more suitable for future situations than the other rule curves.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
Directory of Open Access Journals (Sweden)
Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo
2008-03-01
In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.
Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization
Directory of Open Access Journals (Sweden)
Wang Chun-Feng
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.
Search algorithms as a framework for the optimization of drug combinations.
Directory of Open Access Journals (Sweden)
Diego Calzolari
2008-12-01
Full Text Available Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.
Model Justified Search Algorithms for Scheduling Under Uncertainty
National Research Council Canada - National Science Library
Howe, Adele; Whitley, L. D
2008-01-01
.... We also identified plateaus as a significant barrier to superb performance of local search on scheduling and have studied several canonical discrete optimization problems to discover and model the nature of plateaus...
Journal of Applied Science and Technology: Advanced Search
African Journals Online (AJOL)
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West African Journal of Applied Ecology: Advanced Search
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Bayero Journal of Pure and Applied Sciences: Advanced Search
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Global Journal of Pure and Applied Sciences: Advanced Search
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Nigeria Journal of Pure and Applied Physics: Advanced Search
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Journal of Fundamental and Applied Sciences: Advanced Search
African Journals Online (AJOL)
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National Research Council Canada - National Science Library
Rohrbough, James G; Breci, Linda; Merchant, Nirav; Miller, Susan; Haynes, Paul A
2005-01-01
.... One such technique, known as the Multi-Dimensional Protein Identification Technique, or MudPIT, involves the use of computer search algorithms that automate the process of identifying proteins...
International Nuclear Information System (INIS)
Sacco, W.F.; Lapa, Celso M.F.; Pereira, C.M.N.A.; Oliveira, C.R.E. de
2006-01-01
This article extends previous efforts on genetic algorithms (GAs) applied to a nuclear power plant (NPP) auxiliary feedwater system (AFWS) surveillance tests policy optimization. We introduce the application of a niching genetic algorithm (NGA) to this problem and compare its performance to previous results. The NGA maintains a populational diversity during the search process, thus promoting a greater exploration of the search space. The optimization problem consists in maximizing the system's average availability for a given period of time, considering realistic features such as: (i) aging effects on standby components during the tests; (ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; (iii) components have distinct test parameters (outage time, aging factors, etc.) and (iv) tests are not necessarily periodic. We find that the NGA performs better than the conventional GA and the island GA due to a greater exploration of the search space
Energy Technology Data Exchange (ETDEWEB)
Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)
1994-12-31
In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.
Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm
International Nuclear Information System (INIS)
Wang, Jianzhou; Jiang, He; Wu, Yujie; Dong, Yao
2015-01-01
Due to energy crisis and environmental problems, it is very urgent to find alternative energy sources nowadays. Solar energy, as one of the great potential clean energies, has widely attracted the attention of researchers. In this paper, an optimized hybrid method by CS (Cuckoo Search) on the basis of the OP-ELM (Optimally Pruned Extreme Learning Machine), called CS-OP-ELM, is developed to forecast clear sky and real sky global horizontal radiation. First, MRSR (Multiresponse Sparse Regression) and LOO-CV (leave-one-out cross-validation) can be applied to rank neurons and prune the possibly meaningless neurons of the FFNN (Feed Forward Neural Network), respectively. Then, Direct strategy and Direct-Recursive strategy based on OP-ELM are introduced to build a hybrid model. Furthermore, CS (Cuckoo Search) optimized algorithm is employed to determine the proper weight coefficients. In order to verify the effectiveness of the developed method, hourly solar radiation data from six sites of the United States has been collected, and methods like ARMA (Autoregression moving average), BP (Back Propagation) neural network and OP-ELM can be compared with CS-OP-ELM. Experimental results show the optimized hybrid method CS-OP-ELM has the best forecasting performance. - Highlights: • An optimized hybrid method called CS-OP-ELM is proposed to forecast solar radiation. • CS-OP-ELM adopts multiple variables dataset as input variables. • Direct and Direct-Recursive strategy are introduced to build a hybrid model. • CS (Cuckoo Search) algorithm is used to determine the optimal weight coefficients. • The proposed method has the best performance compared with other methods
Search for 'Little Higgs' and reconstruction algorithms developments in Atlas
International Nuclear Information System (INIS)
Rousseau, D.
2007-05-01
This document summarizes developments of framework and reconstruction algorithms for the ATLAS detector at the LHC. A library of reconstruction algorithms has been developed in a more and more complex environment. The reconstruction software originally designed on an optimistic Monte-Carlo simulation, has been confronted with a more detailed 'as-built' simulation. The 'Little Higgs' is an effective theory which can be taken for granted, or as an opportunity to study heavy resonances. In several cases, these resonances can be detected in original channels like tZ, ZH or WH. (author)
Gao, Kaizhou; Wang, Ling; Luo, Jianping; Jiang, Hua; Sadollah, Ali; Pan, Quanke
2018-06-01
In this article, scheduling and rescheduling problems with increasing processing time and new job insertion are studied for reprocessing problems in the remanufacturing process. To handle the unpredictability of reprocessing time, an experience-based strategy is used. Rescheduling strategies are applied for considering the effect of increasing reprocessing time and the new subassembly insertion. To optimize the scheduling and rescheduling objective, a discrete harmony search (DHS) algorithm is proposed. To speed up the convergence rate, a local search method is designed. The DHS is applied to two real-life cases for minimizing the maximum completion time and the mean of earliness and tardiness (E/T). These two objectives are also considered together as a bi-objective problem. Computational optimization results and comparisons show that the proposed DHS is able to solve the scheduling and rescheduling problems effectively and productively. Using the proposed approach, satisfactory optimization results can be achieved for scheduling and rescheduling on a real-life shop floor.
Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management
Chiu, Y.; Nishikawa, T.; Martin, P.
2008-12-01
Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond
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.
Directory of Open Access Journals (Sweden)
Zhihua Zhang
2016-01-01
Full Text Available Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO. Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Directory of Open Access Journals (Sweden)
Mahendra Nandi
2017-08-01
Full Text Available In present aspect, automatic generation control (AGC of deregulated power system with thyristor controlled series compensator (TCSC device is investigated. The objective is to discuss bilateral power transaction issue with the TCSC effect. A deregulated two-area power system model having two thermal units in each control area is considered for this act. A quasi-oppositional harmony search (QOHS algorithm is being applied for the constrained optimization problem. Three cases, commonly studied in deregulation, are discussed for the effectiveness of the proposed technique. Further, sensitivity analysis is studied by varying the test system parameters up to ±25% from their rated values. The obtained simulation plots are analytically discussed with the calculation of oscillatory modes, transient details and the studied performance indices. Sugeno fuzzy logic control technique is also investigated to the studied test system. The simulation results show that the proposed QOHS based TCSC controller is quite effective in deregulated environment.
Directory of Open Access Journals (Sweden)
Yu-Huei Cheng
2017-11-01
Full Text Available The control strategy is a major unit in hybrid electric vehicles (HEVs. In order to provide suitable control parameters for reducing fuel consumptions and engine emissions while maintaining vehicle performance requirements, the genetic algorithm (GA with small population size is applied to search for feasible control parameters in parallel HEVs. The electric assist control strategy (EACS is used as the fundamental control strategy of parallel HEVs. The dynamic performance requirements stipulated in the Partnership for a New Generation of Vehicles (PNGV is considered to maintain the vehicle performance. The known ADvanced VehIcle SimulatOR (ADVISOR is used to simulate a specific parallel HEV with urban dynamometer driving schedule (UDDS. Five population sets with size 5, 10, 15, 20, and 25 are used in the GA. The experimental results show that the GA with population size of 25 is the best for selecting feasible control parameters in parallel HEVs.
Searching of fuel recharges by means of genetic algorithms and neural networks in BWRs
International Nuclear Information System (INIS)
Ortiz S, J.J.; Montes T, J.L.; Castillo M, J.A.; Perusquia del C, R.
2004-01-01
In this work improvements to the systems RENOR and RECOPIA are presented, that were developed to optimize fuel recharges in boiling water reactors. The RENOR system is based on a Multi state recurrent neural network while RECOPIA is based on a Genetic Algorithm. In the new versions of these systems there is incorporate the execution of the Turned off Margin in Cold and the Excess of Reactivity in Hot. The new systems were applied to an operation cycle of the Unit 1 of the Nuclear Power station of Laguna Verde. The recharges of fuel obtained by both methods are compared among if being observed that RENOR has better performance that RECOPIA, due to the nature of its search process. RECOPIA requires of approximately 1.4 times more time that RENOR to find a satisfactory recharge of fuel. (Author)
On the use of harmony search algorithm in the training of wavelet neural networks
Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline
2015-10-01
Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.
A Teaching Approach from the Exhaustive Search Method to the Needleman-Wunsch Algorithm
Xu, Zhongneng; Yang, Yayun; Huang, Beibei
2017-01-01
The Needleman-Wunsch algorithm has become one of the core algorithms in bioinformatics; however, this programming requires more suitable explanations for students with different major backgrounds. In supposing sample sequences and using a simple store system, the connection between the exhaustive search method and the Needleman-Wunsch algorithm…
Directory of Open Access Journals (Sweden)
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.
Nearby Search Indekos Based Android Using A Star (A*) Algorithm
Siregar, B.; Nababan, EB; Rumahorbo, JA; Andayani, U.; Fahmi, F.
2018-03-01
Indekos or rented room is a temporary residence for months or years. Society of academicians who come from out of town need a temporary residence, such as Indekos or rented room during their education, teaching, or duties. They are often found difficulty in finding a Indekos because lack of information about the Indekos. Besides, new society of academicians don’t recognize the areas around the campus and desire the shortest path from Indekos to get to the campus. The problem can be solved by implementing A Star (A*) algorithm. This algorithm is one of the shortest path algorithm to a finding shortest path from campus to the Indekos application, where the faculties in the campus as the starting point of the finding. Determination of the starting point used in this study aims to allow students to determine the starting point in finding the Indekos. The mobile based application facilitates the finding anytime and anywhere. Based on the experimental results, A* algorithm can find the shortest path with 86,67% accuracy.
New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization
Directory of Open Access Journals (Sweden)
Alejandro MURRIETA-MENDOZA
2016-06-01
Full Text Available Burning the fuel required to sustain a given flight releases pollution such as carbon dioxide and nitrogen oxides, and the amount of fuel consumed is also a significant expense for airlines. It is desirable to reduce fuel consumption to reduce both pollution and flight costs. To increase fuel savings in a given flight, one option is to compute the most economical vertical reference trajectory (or flight plan. A deterministic algorithm was developed using a numerical aircraft performance model to determine the most economical vertical flight profile considering take-off weight, flight distance, step climb and weather conditions. This algorithm is based on linear interpolations of the performance model using the Lagrange interpolation method. The algorithm downloads the latest available forecast from Environment Canada according to the departure date and flight coordinates, and calculates the optimal trajectory taking into account the effects of wind and temperature. Techniques to avoid unnecessary calculations are implemented to reduce the computation time. The costs of the reference trajectories proposed by the algorithm are compared with the costs of the reference trajectories proposed by a commercial flight management system using the fuel consumption estimated by the FlightSim® simulator made by Presagis®.
Orientation estimation algorithm applied to high-spin projectiles
International Nuclear Information System (INIS)
Long, D F; Lin, J; Zhang, X M; Li, J
2014-01-01
High-spin projectiles are low cost military weapons. Accurate orientation information is critical to the performance of the high-spin projectiles control system. However, orientation estimators have not been well translated from flight vehicles since they are too expensive, lack launch robustness, do not fit within the allotted space, or are too application specific. This paper presents an orientation estimation algorithm specific for these projectiles. The orientation estimator uses an integrated filter to combine feedback from a three-axis magnetometer, two single-axis gyros and a GPS receiver. As a new feature of this algorithm, the magnetometer feedback estimates roll angular rate of projectile. The algorithm also incorporates online sensor error parameter estimation performed simultaneously with the projectile attitude estimation. The second part of the paper deals with the verification of the proposed orientation algorithm through numerical simulation and experimental tests. Simulations and experiments demonstrate that the orientation estimator can effectively estimate the attitude of high-spin projectiles. Moreover, online sensor calibration significantly enhances the estimation performance of the algorithm. (paper)
Orientation estimation algorithm applied to high-spin projectiles
Long, D. F.; Lin, J.; Zhang, X. M.; Li, J.
2014-06-01
High-spin projectiles are low cost military weapons. Accurate orientation information is critical to the performance of the high-spin projectiles control system. However, orientation estimators have not been well translated from flight vehicles since they are too expensive, lack launch robustness, do not fit within the allotted space, or are too application specific. This paper presents an orientation estimation algorithm specific for these projectiles. The orientation estimator uses an integrated filter to combine feedback from a three-axis magnetometer, two single-axis gyros and a GPS receiver. As a new feature of this algorithm, the magnetometer feedback estimates roll angular rate of projectile. The algorithm also incorporates online sensor error parameter estimation performed simultaneously with the projectile attitude estimation. The second part of the paper deals with the verification of the proposed orientation algorithm through numerical simulation and experimental tests. Simulations and experiments demonstrate that the orientation estimator can effectively estimate the attitude of high-spin projectiles. Moreover, online sensor calibration significantly enhances the estimation performance of the algorithm.
Differential Evolution algorithm applied to FSW model calibration
Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.
2014-03-01
Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.
Wang, Li; Li, Feng; Xing, Jian
2017-10-01
In this paper, a hybrid artificial bee colony (ABC) algorithm and pattern search (PS) method is proposed and applied for recovery of particle size distribution (PSD) from spectral extinction data. To be more useful and practical, size distribution function is modelled as the general Johnson's ? function that can overcome the difficulty of not knowing the exact type beforehand encountered in many real circumstances. The proposed hybrid algorithm is evaluated through simulated examples involving unimodal, bimodal and trimodal PSDs with different widths and mean particle diameters. For comparison, all examples are additionally validated by the single ABC algorithm. In addition, the performance of the proposed algorithm is further tested by actual extinction measurements with real standard polystyrene samples immersed in water. Simulation and experimental results illustrate that the hybrid algorithm can be used as an effective technique to retrieve the PSDs with high reliability and accuracy. Compared with the single ABC algorithm, our proposed algorithm can produce more accurate and robust inversion results while taking almost comparative CPU time over ABC algorithm alone. The superiority of ABC and PS hybridization strategy in terms of reaching a better balance of estimation accuracy and computation effort increases its potentials as an excellent inversion technique for reliable and efficient actual measurement of PSD.
International Nuclear Information System (INIS)
Sacco, Wagner F.; Machado, Marcelo D.; Pereira, Claudio M.N.A.; Schirru, Roberto
2004-01-01
This article extends previous efforts on genetic algorithms (GAs) applied to a core design optimization problem. We introduce the application of a new Niching Genetic Algorithm (NGA) to this problem and compare its performance to these previous works. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a three-enrichment zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. After exhaustive experiments we observed that our new niching method performs better than the conventional GA due to a greater exploration of the search space
Modification of Brueschweiler quantum searching algorithm and realization by NMR experiment
International Nuclear Information System (INIS)
Yang Xiaodong; Wei Daxiu; Luo Jun; Miao Xijia
2002-01-01
In recent years, quantum computing research has made big progress, which exploit quantum mechanical laws, such as interference, superposition and parallelism, to perform computing tasks. The most inducing thing is that the quantum computing can provide large rise to the speedup in quantum algorithm. Quantum computing can solve some problems, which are impossible or difficult for the classical computing. The problem of searching for a specific item in an unsorted database can be solved with certain quantum algorithm, for example, Grover quantum algorithm and Brueschweiler quantum algorithm. The former gives a quadratic speedup, and the latter gives an exponential speedup comparing with the corresponding classical algorithm. In Brueschweiler quantum searching algorithm, the data qubit and the read-out qubit (the ancilla qubit) are different qubits. The authors have studied Brueschweiler algorithm and proposed a modified version, in which no ancilla qubit is needed to reach exponential speedup in the searching, the data and the read-out qubit are the same qubits. The modified Brueschweiler algorithm can be easier to design and realize. The authors also demonstrate the modified Brueschweiler algorithm in a 3-qubit molecular system by Nuclear Magnetic Resonance (NMR) experiment
Applying genetic algorithms for programming manufactoring cell tasks
Directory of Open Access Journals (Sweden)
Efredy Delgado
2005-05-01
Full Text Available This work was aimed for developing computational intelligence for scheduling a manufacturing cell's tasks, based manily on genetic algorithms. The manufacturing cell was modelled as beign a production-line; the makespan was calculated by using heuristics adapted from several libraries for genetic algorithms computed in C++ builder. Several problems dealing with small, medium and large list of jobs and machinery were resolved. The results were compared with other heuristics. The approach developed here would seem to be promising for future research concerning scheduling manufacturing cell tasks involving mixed batches.
Optimising a shaft's geometry by applying genetic algorithms
Directory of Open Access Journals (Sweden)
María Alejandra Guzmán
2005-05-01
Full Text Available Many engnieering design tasks involve optimising several conflicting goals; these types of problem are known as Multiobjective Optimisation Problems (MOPs. Evolutionary techniques have proved to be an effective tool for finding solutions to these MOPs during the last decade, Variations on the basic generic algorithm have been particulary proposed by different researchers for finding rapid optimal solutions to MOPs. The NSGA (Non-dominated Sorting Generic Algorithm has been implemented in this paper for finding an optimal design for a shaft subjected to cyclic loads, the conflycting goals being minimum weight and minimum lateral deflection.
Parallel preconditioned conjugate gradient algorithm applied to neutron diffusion problem
International Nuclear Information System (INIS)
Majumdar, A.; Martin, W.R.
1992-01-01
Numerical solution of the neutron diffusion problem requires solving a linear system of equations such as Ax = b, where A is an n x n symmetric positive definite (SPD) matrix; x and b are vectors with n components. The preconditioned conjugate gradient (PCG) algorithm is an efficient iterative method for solving such a linear system of equations. In this paper, the authors describe the implementation of a parallel PCG algorithm on a shared memory machine (BBN TC2000) and on a distributed workstation (IBM RS6000) environment created by the parallel virtual machine parallelization software
Directory of Open Access Journals (Sweden)
Wei Huang
2013-01-01
Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.
Quantum Partial Searching Algorithm of a Database with Several Target Items
International Nuclear Information System (INIS)
Pu-Cha, Zhong; Wan-Su, Bao; Yun, Wei
2009-01-01
Choi and Korepin [Quantum Information Processing 6(2007)243] presented a quantum partial search algorithm of a database with several target items which can find a target block quickly when each target block contains the same number of target items. Actually, the number of target items in each target block is arbitrary. Aiming at this case, we give a condition to guarantee performance of the partial search algorithm to be performed and the number of queries to oracle of the algorithm to be minimized. In addition, by further numerical computing we come to the conclusion that the more uniform the distribution of target items, the smaller the number of queries
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
Kmer-SSR: a fast and exhaustive SSR search algorithm.
Pickett, Brandon D; Miller, Justin B; Ridge, Perry G
2017-12-15
One of the main challenges with bioinformatics software is that the size and complexity of datasets necessitate trading speed for accuracy, or completeness. To combat this problem of computational complexity, a plethora of heuristic algorithms have arisen that report a 'good enough' solution to biological questions. However, in instances such as Simple Sequence Repeats (SSRs), a 'good enough' solution may not accurately portray results in population genetics, phylogenetics and forensics, which require accurate SSRs to calculate intra- and inter-species interactions. We present Kmer-SSR, which finds all SSRs faster than most heuristic SSR identification algorithms in a parallelized, easy-to-use manner. The exhaustive Kmer-SSR option has 100% precision and 100% recall and accurately identifies every SSR of any specified length. To identify more biologically pertinent SSRs, we also developed several filters that allow users to easily view a subset of SSRs based on user input. Kmer-SSR, coupled with the filter options, accurately and intuitively identifies SSRs quickly and in a more user-friendly manner than any other SSR identification algorithm. The source code is freely available on GitHub at https://github.com/ridgelab/Kmer-SSR. perry.ridge@byu.edu. © The Author(s) 2017. Published by Oxford University Press.
Comparison of genetic algorithm and harmony search for generator maintenance scheduling
International Nuclear Information System (INIS)
Khan, L.; Mumtaz, S.; Khattak, A.
2012-01-01
GMS (Generator Maintenance Scheduling) ranks very high in decision making of power generation management. Generators maintenance schedule decides the time period of maintenance tasks and a reliable reserve margin is also maintained during this time period. In this paper, a comparison of GA (Genetic Algorithm) and US (Harmony Search) algorithm is presented to solve generators maintenance scheduling problem for WAPDA (Water And Power Development Authority) Pakistan. GA is a search procedure, which is used in search problems to compute exact and optimized solution. GA is considered as global search heuristic technique. HS algorithm is quite efficient, because the convergence rate of this algorithm is very fast. HS algorithm is based on the concept of music improvisation process of searching for a perfect state of harmony. The two algorithms generate feasible and optimal solutions and overcome the limitations of the conventional methods including extensive computational effort, which increases exponentially as the size of the problem increases. The proposed methods are tested, validated and compared on the WAPDA electric system. (author)
Performance evaluation of the EM algorithm applied to radiographic images
International Nuclear Information System (INIS)
Brailean, J.C.; Giger, M.L.; Chen, C.T.; Sullivan, B.J.
1990-01-01
In this paper the authors evaluate the expectation maximization (EM) algorithm, both qualitatively and quantitatively, as a technique for enhancing radiographic images. Previous studies have qualitatively shown the usefulness of the EM algorithm but have failed to quantify and compare its performance with those of other image processing techniques. Recent studies by Loo et al, Ishida et al, and Giger et al, have explained improvements in image quality quantitatively in terms of a signal-to-noise ratio (SNR) derived from signal detection theory. In this study, we take a similar approach in quantifying the effect of the EM algorithm on detection of simulated low-contrast square objects superimposed on radiographic mottle. The SNRs of the original and processed images are calculated taking into account both the human visual system response and the screen-film transfer function as well as a noise component internal to the eye-brain system. The EM algorithm was also implemented on digital screen-film images of test patterns and clinical mammograms
The PBIL algorithm applied to a nuclear reactor design optimization
Energy Technology Data Exchange (ETDEWEB)
Machado, Marcelo D.; Medeiros, Jose A.C.C.; Lima, Alan M.M. de; Schirru, Roberto [Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ-RJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoracao de Processos]. E-mails: marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; alan@lmp.ufrj.br; schirru@lmp.ufrj.br
2007-07-01
The Population-Based Incremental Learning (PBIL) algorithm is a method that combines the mechanism of genetic algorithm with the simple competitive learning, creating an important tool to be used in the optimization of numeric functions and combinatory problems. PBIL works with a set of solutions to the problems, called population, whose objective is create a probability vector, containing real values in each position, that when used in a decoding procedure gives subjects that present the best solutions for the function to be optimized. In this work a new form of learning for algorithm PBIL is developed, having aimed at to reduce the necessary time for the optimization process. This new algorithm will be used in the nuclear reactor design optimization. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment zone reactor, considering some restrictions. In this optimization is used the computational code HAMMER, and the results compared with other methods of optimization by artificial intelligence. (author)
The PBIL algorithm applied to a nuclear reactor design optimization
International Nuclear Information System (INIS)
Machado, Marcelo D.; Medeiros, Jose A.C.C.; Lima, Alan M.M. de; Schirru, Roberto
2007-01-01
The Population-Based Incremental Learning (PBIL) algorithm is a method that combines the mechanism of genetic algorithm with the simple competitive learning, creating an important tool to be used in the optimization of numeric functions and combinatory problems. PBIL works with a set of solutions to the problems, called population, whose objective is create a probability vector, containing real values in each position, that when used in a decoding procedure gives subjects that present the best solutions for the function to be optimized. In this work a new form of learning for algorithm PBIL is developed, having aimed at to reduce the necessary time for the optimization process. This new algorithm will be used in the nuclear reactor design optimization. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment zone reactor, considering some restrictions. In this optimization is used the computational code HAMMER, and the results compared with other methods of optimization by artificial intelligence. (author)
Genetic algorithms applied to the nuclear power plant operation
International Nuclear Information System (INIS)
Schirru, R.; Martinez, A.S.; Pereira, C.M.N.A.
2000-01-01
Nuclear power plant operation often involves very important human decisions, such as actions to be taken after a nuclear accident/transient, or finding the best core reload pattern, a complex combinatorial optimization problem which requires expert knowledge. Due to the complexity involved in the decisions to be taken, computerized systems have been intensely explored in order to aid the operator. Following hardware advances, soft computing has been improved and, nowadays, intelligent technologies, such as genetic algorithms, neural networks and fuzzy systems, are being used to support operator decisions. In this chapter two main problems are explored: transient diagnosis and nuclear core refueling. Here, solutions to such kind of problems, based on genetic algorithms, are described. A genetic algorithm was designed to optimize the nuclear fuel reload of Angra-1 nuclear power plant. Results compared to those obtained by an expert reveal a gain in the burn-up cycle. Two other genetic algorithm approaches were used to optimize real time diagnosis systems. The first one learns partitions in the time series that represents the transients, generating a set of classification centroids. The other one involves the optimization of an adaptive vector quantization neural network. Results are shown and commented. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Ngonkham, S. [Khonkaen Univ., Amphur Muang (Thailand). Dept. of Electrical Engineering; Buasri, P. [Khonkaen Univ., Amphur Muang (Thailand). Embed System Research Group
2009-03-11
A harmony search (HS) algorithm was used to optimize economic dispatch (ED) in a wind energy conversion system (WECS) for power system integration. The HS algorithm was based on a stochastic random search method. System costs for the WECS system were estimated in relation to average wind speeds. The HS algorithm was implemented to optimize the ED with a simple programming procedure. The study showed that the initial parameters must be carefully selected to ensure the accuracy of the HS algorithm. The algorithm demonstrated that total costs of the WECS system were higher than costs associated with energy efficiency procedures that reduced the same amount of greenhouse gas (GHG) emissions. 7 refs,. 10 tabs., 16 figs.
Directory of Open Access Journals (Sweden)
ALTINOZ, O. T.
2014-08-01
Full Text Available Nature-inspired optimization algorithms can obtain the optima by updating the position of each member in the population. At the beginning of the algorithm, the particles of the population are spread into the search space. The initial distribution of particles corresponds to the beginning points of the search process. Hence, the aim is to alter the position for each particle beginning with this initial position until the optimum solution will be found with respect to the pre-determined conditions like maximum iteration, and specific error value for the fitness function. Therefore, initial positions of the population have a direct effect on both accuracy of the optima and the computational cost. If any member in the population is close enough to the optima, this eases the achievement of the exact solution. On the contrary, individuals grouped far away from the optima might yield pointless efforts. In this study, low-discrepancy quasi-random number sequence is preferred for the localization of the population at the initialization phase. By this way, the population is distributed into the search space in a more uniform manner at the initialization phase. The technique is applied to the Gravitational Search Algorithm and compared via the performance on benchmark function solutions.
Optimization of Particle Search Algorithm for CFD-DEM Simulations
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G. Baryshev
2013-09-01
Full Text Available Discrete element method has numerous applications in particle physics. However, simulating particles as discrete entities can become costly for large systems. In time-driven DEM simulation most computation time is taken by contact search stage. We propose an efficient collision detection method which is based on sorting particles by their coordinates. Using multiple sorting criteria allows minimizing number of potential neighbours and defines fitness of this approach for simulation of massive systems in 3D. This method is compared to a common approach that consists of placing particles onto a grid of cells. Advantage of the new approach is independence of simulation parameters upon particle radius and domain size.
Applied algorithm in the liner inspection of solid rocket motors
Hoffmann, Luiz Felipe Simões; Bizarria, Francisco Carlos Parquet; Bizarria, José Walter Parquet
2018-03-01
In rocket motors, the bonding between the solid propellant and thermal insulation is accomplished by a thin adhesive layer, known as liner. The liner application method involves a complex sequence of tasks, which includes in its final stage, the surface integrity inspection. Nowadays in Brazil, an expert carries out a thorough visual inspection to detect defects on the liner surface that may compromise the propellant interface bonding. Therefore, this paper proposes an algorithm that uses the photometric stereo technique and the K-nearest neighbor (KNN) classifier to assist the expert in the surface inspection. Photometric stereo allows the surface information recovery of the test images, while the KNN method enables image pixels classification into two classes: non-defect and defect. Tests performed on a computer vision based prototype validate the algorithm. The positive results suggest that the algorithm is feasible and when implemented in a real scenario, will be able to help the expert in detecting defective areas on the liner surface.
ANTQ evolutionary algorithm applied to nuclear fuel reload problem
International Nuclear Information System (INIS)
Machado, Liana; Schirru, Roberto
2000-01-01
Nuclear fuel reload optimization is a NP-complete combinatorial optimization problem where the aim is to find fuel rods' configuration that maximizes burnup or minimizes the power peak factor. For decades this problem was solved exclusively using an expert's knowledge. From the eighties, however, there have been efforts to automatize fuel reload. The first relevant effort used Simulated Annealing, but more recent publications show Genetic Algorithm's (GA) efficiency on this problem's solution. Following this direction, our aim is to optimize nuclear fuel reload using Ant-Q, a reinforcement learning algorithm based on the Cellular Computing paradigm. Ant-Q's results on the Travelling Salesmen Problem, which is conceptually similar to fuel reload, are better than the GA's ones. Ant-Q was tested on fuel reload by the simulation of the first cycle in-out reload of Bibils, a 193 fuel element PWR. Comparing An-Q's result with the GA's ones, it can b seen that even without a local heuristics, the former evolutionary algorithm can be used to solve the nuclear fuel reload problem. (author)
Multiagency Urban Search Experiment Detector and Algorithm Test Bed
Nicholson, Andrew D.; Garishvili, Irakli; Peplow, Douglas E.; Archer, Daniel E.; Ray, William R.; Swinney, Mathew W.; Willis, Michael J.; Davidson, Gregory G.; Cleveland, Steven L.; Patton, Bruce W.; Hornback, Donald E.; Peltz, James J.; McLean, M. S. Lance; Plionis, Alexander A.; Quiter, Brian J.; Bandstra, Mark S.
2017-07-01
In order to provide benchmark data sets for radiation detector and algorithm development, a particle transport test bed has been created using experimental data as model input and validation. A detailed radiation measurement campaign at the Combined Arms Collective Training Facility in Fort Indiantown Gap, PA (FTIG), USA, provides sample background radiation levels for a variety of materials present at the site (including cinder block, gravel, asphalt, and soil) using long dwell high-purity germanium (HPGe) measurements. In addition, detailed light detection and ranging data and ground-truth measurements inform model geometry. This paper describes the collected data and the application of these data to create background and injected source synthetic data for an arbitrary gamma-ray detection system using particle transport model detector response calculations and statistical sampling. In the methodology presented here, HPGe measurements inform model source terms while detector response calculations are validated via long dwell measurements using 2"×4"×16" NaI(Tl) detectors at a variety of measurement points. A collection of responses, along with sampling methods and interpolation, can be used to create data sets to gauge radiation detector and algorithm (including detection, identification, and localization) performance under a variety of scenarios. Data collected at the FTIG site are available for query, filtering, visualization, and download at muse.lbl.gov.
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)
Accelerating Smith-Waterman Algorithm for Biological Database Search on CUDA-Compatible GPUs
Munekawa, Yuma; Ino, Fumihiko; Hagihara, Kenichi
This paper presents a fast method capable of accelerating the Smith-Waterman algorithm for biological database search on a cluster of graphics processing units (GPUs). Our method is implemented using compute unified device architecture (CUDA), which is available on the nVIDIA GPU. As compared with previous methods, our method has four major contributions. (1) The method efficiently uses on-chip shared memory to reduce the data amount being transferred between off-chip video memory and processing elements in the GPU. (2) It also reduces the number of data fetches by applying a data reuse technique to query and database sequences. (3) A pipelined method is also implemented to overlap GPU execution with database access. (4) Finally, a master/worker paradigm is employed to accelerate hundreds of database searches on a cluster system. In experiments, the peak performance on a GeForce GTX 280 card reaches 8.32 giga cell updates per second (GCUPS). We also find that our method reduces the amount of data fetches to 1/140, achieving approximately three times higher performance than a previous CUDA-based method. Our 32-node cluster version is approximately 28 times faster than a single GPU version. Furthermore, the effective performance reaches 75.6 giga instructions per second (GIPS) using 32 GeForce 8800 GTX cards.
Structural optimization of a motorcycle chassis by pattern search algorithm
Scappaticci, Lorenzo; Bartolini, Nicola; Guglielmino, Eugenio; Risitano, Giacomo
2017-08-01
Changes to the technical regulations of the motorcycle racing world classes introduced the new Moto2 category. The vehicles are prototypes that use single-brand tyres and engines derived from series production, supplied by a single manufacturer. The stability and handling of the vehicle are highly dependent on the geometric properties of the chassis. The performance of a racing motorcycle chassis can be primarily evaluated in terms of weight and stiffness. The aim of this work is to maximize the performance of a tubular frame designed for a motorcycle racing in the Moto2 category. The goal is the implementation of an optimization algorithm that acts on the dimensions of the single pipes of the frame and involves the design of an objective function to minimize the weight of the frame by controlling its stiffnesses.
Keyword-based Ciphertext Search Algorithm under Cloud Storage
Directory of Open Access Journals (Sweden)
Ren Xunyi
2016-01-01
Full Text Available With the development of network storage services, cloud storage have the advantage of high scalability , inexpensive, without access limit and easy to manage. These advantages make more and more small or medium enterprises choose to outsource large quantities of data to a third party. This way can make lots of small and medium enterprises get rid of costs of construction and maintenance, so it has broad market prospects. But now lots of cloud storage service providers can not protect data security.This result leakage of user data, so many users have to use traditional storage method.This has become one of the important factors that hinder the development of cloud storage. In this article, establishing keyword index by extracting keywords from ciphertext data. After that, encrypted data and the encrypted index upload cloud server together.User get related ciphertext by searching encrypted index, so it can response data leakage problem.
Novel Back Propagation Optimization by Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
Jiao-hong Yi
2014-01-01
Full Text Available The traditional Back Propagation (BP has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS, called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN. Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.
Applying Planning Algorithms to Argue in Cooperative Work
Monteserin, Ariel; Schiaffino, Silvia; Amandi, Analía
Negotiation is typically utilized in cooperative work scenarios for solving conflicts. Anticipating possible arguments in this negotiation step represents a key factor since we can take decisions about our participation in the cooperation process. In this context, we present a novel application of planning algorithms for argument generation, where the actions of a plan represent the arguments that a person might use during the argumentation process. In this way, we can plan how to persuade the other participants in cooperative work for reaching an expected agreement in terms of our interests. This approach allows us to take advantages since we can test anticipated argumentative solutions in advance.
Directory of Open Access Journals (Sweden)
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.
Cuckoo search via Levy flights applied to uncapacitated facility location problem
Mesa, Armacheska; Castromayor, Kris; Garillos-Manliguez, Cinmayii; Calag, Vicente
2017-11-01
Facility location problem (FLP) is a mathematical way to optimally locate facilities within a set of candidates to satisfy the requirements of a given set of clients. This study addressed the uncapacitated FLP as it assures that the capacity of every selected facility is finite. Thus, even if the demand is not known, which often is the case, in reality, organizations may still be able to take strategic decisions such as locating the facilities. There are different approaches relevant to the uncapacitated FLP. Here, the cuckoo search via Lévy flight (CS-LF) was used to solve the problem. Though hybrid methods produce better results, this study employed CS-LF to determine first its potential in finding solutions for the problem, particularly when applied to a real-world problem. The method was applied to the data set obtained from a department store in Davao City, Philippines. Results showed that applying CS-LF yielded better facility locations compared to particle swarm optimization and other existing algorithms. Although these results showed that CS-LF is a promising method to solve this particular problem, further studies on other FLP are recommended to establish a strong foundation of the capability of CS-LF in solving FLP.
Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS
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Stephan P. Lovstedt
2008-01-01
Full Text Available The FXLMS algorithm, used extensively in active noise control (ANC, exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.
Directory of Open Access Journals (Sweden)
R. A. Swief
2018-01-01
Full Text Available This paper presents an efficient Cuckoo Search Optimization technique to improve the reliability of electrical power systems. Various reliability objective indices such as Energy Not Supplied, System Average Interruption Frequency Index, System Average Interruption, and Duration Index are the main indices indicating reliability. The Cuckoo Search Optimization (CSO technique is applied to optimally place the protection devices, install the distributed generators, and to determine the size of distributed generators in radial feeders for reliability improvement. Distributed generator affects reliability and system power losses and voltage profile. The volatility behaviour for both photovoltaic cells and the wind turbine farms affect the values and the selection of protection devices and distributed generators allocation. To improve reliability, the reconfiguration will take place before installing both protection devices and distributed generators. Assessment of consumer power system reliability is a vital part of distribution system behaviour and development. Distribution system reliability calculation will be relayed on probabilistic reliability indices, which can expect the disruption profile of a distribution system based on the volatility behaviour of added generators and load behaviour. The validity of the anticipated algorithm has been tested using a standard IEEE 69 bus system.
Directory of Open Access Journals (Sweden)
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.
Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images
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Akara Sopharak
2013-07-01
Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.
Directory of Open Access Journals (Sweden)
Jianwen Guo
2016-01-01
Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.
Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines
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Dongxiao Niu
2018-01-01
Full Text Available Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.
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.
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Iona Maghali S. de; Schirru, Roberto; Medeiros, Jose A.C.C., E-mail: maghali@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b, E-mail: canedo@lmp.ufrj.b [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-Graduacao de Engenharia. Programa de Engenharia Nuclear
2009-07-01
The swarm-based algorithm described in this paper is a new search algorithm capable of locating good solutions efficiently and within a reasonable running time. The work presents a population-based search algorithm that mimics the food foraging behavior of honey bee swarms and can be regarded as belonging to the category of intelligent optimization tools. In its basic version, the algorithm performs a kind of random search combined with neighborhood search and can be used for solving multi-dimensional numeric problems. Following a description of the algorithm, this paper presents a new event classification system based exclusively on the ability of the algorithm to find the best centroid positions that correctly identifies an accident in a PWR nuclear power plant, thus maximizing the number of correct classification of transients. The simulation results show that the performance of the proposed algorithm is comparable to other population-based algorithms when applied to the same problem, with the advantage of employing fewer control parameters. (author)
International Nuclear Information System (INIS)
Oliveira, Iona Maghali S. de; Schirru, Roberto; Medeiros, Jose A.C.C.
2009-01-01
The swarm-based algorithm described in this paper is a new search algorithm capable of locating good solutions efficiently and within a reasonable running time. The work presents a population-based search algorithm that mimics the food foraging behavior of honey bee swarms and can be regarded as belonging to the category of intelligent optimization tools. In its basic version, the algorithm performs a kind of random search combined with neighborhood search and can be used for solving multi-dimensional numeric problems. Following a description of the algorithm, this paper presents a new event classification system based exclusively on the ability of the algorithm to find the best centroid positions that correctly identifies an accident in a PWR nuclear power plant, thus maximizing the number of correct classification of transients. The simulation results show that the performance of the proposed algorithm is comparable to other population-based algorithms when applied to the same problem, with the advantage of employing fewer control parameters. (author)
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.
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 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.
δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms
Aguirre, Hernán; Sato, Masahiko; Tanaka, Kiyoshi
In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.
Apply lightweight recognition algorithms in optical music recognition
Pham, Viet-Khoi; Nguyen, Hai-Dang; Nguyen-Khac, Tung-Anh; Tran, Minh-Triet
2015-02-01
The problems of digitalization and transformation of musical scores into machine-readable format are necessary to be solved since they help people to enjoy music, to learn music, to conserve music sheets, and even to assist music composers. However, the results of existing methods still require improvements for higher accuracy. Therefore, the authors propose lightweight algorithms for Optical Music Recognition to help people to recognize and automatically play musical scores. In our proposal, after removing staff lines and extracting symbols, each music symbol is represented as a grid of identical M ∗ N cells, and the features are extracted and classified with multiple lightweight SVM classifiers. Through experiments, the authors find that the size of 10 ∗ 12 cells yields the highest precision value. Experimental results on the dataset consisting of 4929 music symbols taken from 18 modern music sheets in the Synthetic Score Database show that our proposed method is able to classify printed musical scores with accuracy up to 99.56%.
Applying Intelligent Algorithms to Automate the Identification of Error Factors.
Jin, Haizhe; Qu, Qingxing; Munechika, Masahiko; Sano, Masataka; Kajihara, Chisato; Duffy, Vincent G; Chen, Han
2018-05-03
Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.
Comparison of a constraint directed search to a genetic algorithm in a scheduling application
International Nuclear Information System (INIS)
Abbott, L.
1993-01-01
Scheduling plutonium containers for blending is a time-intensive operation. Several constraints must be taken into account; including the number of containers in a dissolver run, the size of each dissolver run, and the size and target purity of the blended mixture formed from these runs. Two types of algorithms have been used to solve this problem: a constraint directed search and a genetic algorithm. This paper discusses the implementation of these two different approaches to the problem and the strengths and weaknesses of each algorithm
Harmony search algorithm for solving combined heat and power economic dispatch problems
Energy Technology Data Exchange (ETDEWEB)
Khorram, Esmaile, E-mail: eskhor@aut.ac.i [Department of Applied Mathematics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, No. 424, Hafez Ave., 15914 Tehran (Iran, Islamic Republic of); Jaberipour, Majid, E-mail: Majid.Jaberipour@gmail.co [Department of Applied Mathematics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, No. 424, Hafez Ave., 15914 Tehran (Iran, Islamic Republic of)
2011-02-15
Economic dispatch (ED) is one of the key optimization problems in electric power system operation. The problem grows complex if one or more units produce both power and heat. Combined heat and power economic dispatch (CHPED) problem is a complicated problem that needs powerful methods to solve. This paper presents a harmony search (EDHS) algorithm to solve CHPED. Some standard examples are presented to demonstrate the effectiveness of this algorithm in obtaining the optimal solution. In all cases, the solutions obtained using EDHS algorithm are better than those obtained by other methods.
A Local Search Algorithm for the Flow Shop Scheduling Problem with Release Dates
Directory of Open Access Journals (Sweden)
Tao Ren
2015-01-01
Full Text Available This paper discusses the flow shop scheduling problem to minimize the makespan with release dates. By resequencing the jobs, a modified heuristic algorithm is obtained for handling large-sized problems. Moreover, based on some properties, a local search scheme is provided to improve the heuristic to gain high-quality solution for moderate-sized problems. A sequence-independent lower bound is presented to evaluate the performance of the algorithms. A series of simulation results demonstrate the effectiveness of the proposed algorithms.
A Line Search Multilevel Truncated Newton Algorithm for Computing the Optical Flow
Directory of Open Access Journals (Sweden)
Lluís Garrido
2015-06-01
Full Text Available We describe the implementation details and give the experimental results of three optimization algorithms for dense optical flow computation. In particular, using a line search strategy, we evaluate the performance of the unilevel truncated Newton method (LSTN, a multiresolution truncated Newton (MR/LSTN and a full multigrid truncated Newton (FMG/LSTN. We use three image sequences and four models of optical flow for performance evaluation. The FMG/LSTN algorithm is shown to lead to better optical flow estimation with less computational work than both the LSTN and MR/LSTN algorithms.
Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects
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.
Audain, Enrique; Uszkoreit, Julian; Sachsenberg, Timo; Pfeuffer, Julianus; Liang, Xiao; Hermjakob, Henning; Sanchez, Aniel; Eisenacher, Martin; Reinert, Knut; Tabb, David L; Kohlbacher, Oliver; Perez-Riverol, Yasset
2017-01-06
In mass spectrometry-based shotgun proteomics, protein identifications are usually the desired result. However, most of the analytical methods are based on the identification of reliable peptides and not the direct identification of intact proteins. Thus, assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Currently, different protein inference algorithms and tools are available for the proteomics community. Here, we evaluated five software tools for protein inference (PIA, ProteinProphet, Fido, ProteinLP, MSBayesPro) using three popular database search engines: Mascot, X!Tandem, and MS-GF+. All the algorithms were evaluated using a highly customizable KNIME workflow using four different public datasets with varying complexities (different sample preparation, species and analytical instruments). We defined a set of quality control metrics to evaluate the performance of each combination of search engines, protein inference algorithm, and parameters on each dataset. We show that the results for complex samples vary not only regarding the actual numbers of reported protein groups but also concerning the actual composition of groups. Furthermore, the robustness of reported proteins when using databases of differing complexities is strongly dependant on the applied inference algorithm. Finally, merging the identifications of multiple search engines does not necessarily increase the number of reported proteins, but does increase the number of peptides per protein and thus can generally be recommended. Protein inference is one of the major challenges in MS-based proteomics nowadays. Currently, there are a vast number of protein inference algorithms and implementations available for the proteomics community. Protein assembly impacts in the final results of the research, the quantitation values and the final claims in the research manuscript. Even though protein
Directory of Open Access Journals (Sweden)
Xuyun FU
2018-01-01
Full Text Available The opportunistic replacement of multiple Life-Limited Parts (LLPs is a problem widely existing in industry. The replacement strategy of LLPs has a great impact on the total maintenance cost to a lot of equipment. This article focuses on finding a quick and effective algorithm for this problem. To improve the algorithm efficiency, six reduction rules are suggested from the perspectives of solution feasibility, determination of the replacement of LLPs, determination of the maintenance occasion and solution optimality. Based on these six reduction rules, a search algorithm is proposed. This search algorithm can identify one or several optimal solutions. A numerical experiment shows that these six reduction rules are effective, and the time consumed by the algorithm is less than 38 s if the total life of equipment is shorter than 55000 and the number of LLPs is less than 11. A specific case shows that the algorithm can obtain optimal solutions which are much better than the result of the traditional method in 10 s, and it can provide support for determining to-be-replaced LLPs when determining the maintenance workscope of an aircraft engine. Therefore, the algorithm is applicable to engineering applications concerning opportunistic replacement of multiple LLPs in aircraft engines.
Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K
2016-01-01
The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
A comparative study of the A* heuristic search algorithm used to solve efficiently a puzzle game
Iordan, A. E.
2018-01-01
The puzzle game presented in this paper consists in polyhedra (prisms, pyramids or pyramidal frustums) which can be moved using the free available spaces. The problem requires to be found the minimum number of movements in order the game reaches to a goal configuration starting from an initial configuration. Because the problem is enough complex, the principal difficulty in solving it is given by dimension of search space, that leads to necessity of a heuristic search. The improving of the search method consists into determination of a strong estimation by the heuristic function which will guide the search process to the most promising side of the search tree. The comparative study is realized among Manhattan heuristic and the Hamming heuristic using A* search algorithm implemented in Java. This paper also presents the necessary stages in object oriented development of a software used to solve efficiently this puzzle game. The modelling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. With the purpose to confirm the theoretical results which demonstrates that Manhattan heuristic is more efficient was used space complexity criterion. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. From the experimental results obtained by using the Manhattan heuristic, improvements were observed regarding space complexity of A* algorithm versus Hamming heuristic.
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.
Directory of Open Access Journals (Sweden)
Ali Wagdy Mohamed
2014-11-01
Full Text Available In this paper, a novel version of Differential Evolution (DE algorithm based on a couple of local search mutation and a restart mechanism for solving global numerical optimization problems over continuous space is presented. The proposed algorithm is named as Restart Differential Evolution algorithm with Local Search Mutation (RDEL. In RDEL, inspired by Particle Swarm Optimization (PSO, a novel local mutation rule based on the position of the best and the worst individuals among the entire population of a particular generation is introduced. The novel local mutation scheme is joined with the basic mutation rule through a linear decreasing function. The proposed local mutation scheme is proven to enhance local search tendency of the basic DE and speed up the convergence. Furthermore, a restart mechanism based on random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme is combined to avoid stagnation and/or premature convergence. Additionally, an exponent increased crossover probability rule and a uniform scaling factors of DE are introduced to promote the diversity of the population and to improve the search process, respectively. The performance of RDEL is investigated and compared with basic differential evolution, and state-of-the-art parameter adaptive differential evolution variants. It is discovered that the proposed modifications significantly improve the performance of DE in terms of quality of solution, efficiency and robustness.
DEFF Research Database (Denmark)
Neumann, Frank; Witt, Carsten
2015-01-01
combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very...
An Adaptive Large Neighborhood Search Algorithm for the Multi-mode RCPSP
DEFF Research Database (Denmark)
Muller, Laurent Flindt
We present an Adaptive Large Neighborhood Search algorithm for the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP). We incorporate techniques for deriving additional precedence relations and propose a new method, so-called mode-diminution, for removing modes during execution...
From Schrцdinger's equation to the quantum search algorithm£
Indian Academy of Sciences (India)
Also the framework was simple and general and could be extended to ... It is unusual to write a paper listing the steps that led to a result after the result itself ... the quantum search algorithm – it is by no means a comprehensive review of quantum ..... D, as defined in the previous section, is no longer unitary for large ε.
A variable-depth search algorithm for recursive bi-partitioning of signal flow graphs
de Kock, E.A.; Aarts, E.H.L.; Essink, G.; Jansen, R.E.J.; Korst, J.H.M.
1995-01-01
We discuss the use of local search techniques for mapping video algorithms onto programmable high-performance video signal processors. The mapping problem is very complex due to many constraints that need to be satisfied in order to obtain a feasible solution. The complexity is reduced by
A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil
Negri, A. J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)
2000-01-01
The development of a satellite infrared technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall in Amazonia are presented. The Convective-Stratiform. Technique, calibrated by coincident, physically retrieved rain rates from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI), is applied during January to April 1999 over northern South America. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall is presented. Results compare well (a one-hour lag) with the diurnal cycle derived from Tropical Ocean-Global Atmosphere (TOGA) radar-estimated rainfall in Rondonia. The satellite estimates reveal that the convective rain constitutes, in the mean, 24% of the rain area while accounting for 67% of the rain volume. The effects of geography (rivers, lakes, coasts) and topography on the diurnal cycle of convection are examined. In particular, the Amazon River, downstream of Manaus, is shown to both enhance early morning rainfall and inhibit afternoon convection. Monthly estimates from this technique, dubbed CST/TMI, are verified over a dense rain gage network in the state of Ceara, in northeast Brazil. The CST/TMI showed a high bias equal to +33% of the gage mean, indicating that possibly the TMI estimates alone are also high. The root mean square difference (after removal of the bias) equaled 36.6% of the gage mean. The correlation coefficient was 0.77 based on 72 station-months.
Directory of Open Access Journals (Sweden)
Weitian Lin
2014-01-01
Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.
Solving k-Barrier Coverage Problem Using Modified Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Yanhua Zhang
2017-01-01
Full Text Available Coverage problem is a critical issue in wireless sensor networks for security applications. The k-barrier coverage is an effective measure to ensure robustness. In this paper, we formulate the k-barrier coverage problem as a constrained optimization problem and introduce the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. A novel hybrid particle swarm optimization and gravitational search algorithm (PGSA is proposed to solve this problem. The proposed PGSA adopts a k-barrier coverage generation strategy based on probability and integrates the exploitation ability in particle swarm optimization to update the velocity and enhance the global search capability and introduce the boundary mutation strategy of an agent to increase the population diversity and search accuracy. Extensive simulations are conducted to demonstrate the effectiveness of our proposed algorithm.
Directory of Open Access Journals (Sweden)
Chang Kung-Yen
2011-07-01
Full Text Available Abstract Background Database searching is the most frequently used approach for automated peptide assignment and protein inference of tandem mass spectra. The results, however, depend on the sequences in target databases and on search algorithms. Recently by using an alternative splicing database, we identified more proteins than with the annotated proteins in Aspergillus flavus. In this study, we aimed at finding a greater number of eligible splice variants based on newly available transcript sequences and the latest genome annotation. The improved database was then used to compare four search algorithms: Mascot, OMSSA, X! Tandem, and InsPecT. Results The updated alternative splicing database predicted 15833 putative protein variants, 61% more than the previous results. There was transcript evidence for 50% of the updated genes compared to the previous 35% coverage. Database searches were conducted using the same set of spectral data, search parameters, and protein database but with different algorithms. The false discovery rates of the peptide-spectrum matches were estimated Conclusions We were able to detect dozens of new peptides using the improved alternative splicing database with the recently updated annotation of the A. flavus genome. Unlike the identifications of the peptides and the RefSeq proteins, large variations existed between the putative splice variants identified by different algorithms. 12 candidates of putative isoforms were reported based on the consensus peptide-spectrum matches. This suggests that applications of multiple search engines effectively reduced the possible false positive results and validated the protein identifications from tandem mass spectra using an alternative splicing database.
International Nuclear Information System (INIS)
Ohtsuki, Yukiyoshi
2010-01-01
In this paper, molecular quantum computation is numerically studied with the quantum search algorithm (Grover's algorithm) by means of optimal control simulation. Qubits are implemented in the vibronic states of I 2 , while gate operations are realized by optimally designed laser pulses. The methodological aspects of the simulation are discussed in detail. We show that the algorithm for solving a gate pulse-design problem has the same mathematical form as a state-to-state control problem in the density matrix formalism, which provides monotonically convergent algorithms as an alternative to the Krotov method. The sequential irradiation of separately designed gate pulses leads to the population distribution predicted by Grover's algorithm. The computational accuracy is reduced by the imperfect quality of the pulse design and by the electronic decoherence processes that are modeled by the non-Markovian master equation. However, as long as we focus on the population distribution of the vibronic qubits, we can search a target state with high probability without introducing error-correction processes during the computation. A generalized gate pulse-design scheme to explicitly include decoherence effects is outlined, in which we propose a new objective functional together with its solution algorithm that guarantees monotonic convergence.
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.
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.
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.
Directory of Open Access Journals (Sweden)
Z. Masomi Zohrabad
2016-12-01
Full Text Available Power networks continue to grow following the annual growth of energy demand. As constructing new energy generation facilities bears a high cost, minimizing power grid losses becomes essential to permit low cost energy transmission in larger distances and additional areas. This study aims to model an optimization problem for an IEEE 30-bus power grid using a Tabu search algorithm based on an improved hybrid Harmony Search (HS method to reduce overall grid losses. The proposed algorithm is applied to find the best location for the installation of a Unified Power Flow Controller (UPFC. The results obtained from installation of the UPFC in the grid are presented by displaying outputs.
Directory of Open Access Journals (Sweden)
Santosh Kumar Singh
2017-06-01
Full Text Available This paper presents a new hybrid method based on Gravity Search Algorithm (GSA and Recursive Least Square (RLS, known as GSA-RLS, to solve the harmonic estimation problems in the case of time varying power signals in presence of different noises. GSA is based on the Newton’s law of gravity and mass interactions. In the proposed method, the searcher agents are a collection of masses that interact with each other using Newton’s laws of gravity and motion. The basic GSA algorithm strategy is combined with RLS algorithm sequentially in an adaptive way to update the unknown parameters (weights of the harmonic signal. Simulation and practical validation are made with the experimentation of the proposed algorithm with real time data obtained from a heavy paper industry. A comparative performance of the proposed algorithm is evaluated with other recently reported algorithms like, Differential Evolution (DE, Particle Swarm Optimization (PSO, Bacteria Foraging Optimization (BFO, Fuzzy-BFO (F-BFO hybridized with Least Square (LS and BFO hybridized with RLS algorithm, which reveals that the proposed GSA-RLS algorithm is the best in terms of accuracy, convergence and computational time.
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
Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning
Directory of Open Access Journals (Sweden)
Guangyi Liu
2014-01-01
Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.
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.
International Nuclear Information System (INIS)
Chen, Fang; Zhou, Jianzhong; Wang, Chao; Li, Chunlong; Lu, Peng
2017-01-01
Wind power is a type of clean and renewable energy, and reasonable utilization of wind power is beneficial to environmental protection and economic development. Therefore, a short-term hydro-thermal-wind economic emission dispatching (SHTW-EED) problem is presented in this paper. The proposed problem aims to distribute the load among hydro, thermal and wind power units to simultaneously minimize economic cost and pollutant emission. To solve the SHTW-EED problem with complex constraints, a modified gravitational search algorithm based on the non-dominated sorting genetic algorithm-III (MGSA-NSGA-III) is proposed. In the proposed MGSA-NSGA-III, a non-dominated sorting approach, reference-point based selection mechanism and chaotic mutation strategy are applied to improve the evolutionary process of the original gravitational search algorithm (GSA) and maintain the distribution diversity of Pareto optimal solutions. Moreover, a parallel computing strategy is introduced to improve the computational efficiency. Finally, the proposed MGSA-NSGA-III is applied to a typical hydro-thermal-wind system to verify its feasibility and effectiveness. The simulation results indicate that the proposed algorithm can obtain low economic cost and small pollutant emission when dealing with the SHTW-EED problem. - Highlights: • A hybrid algorithm is proposed to handle hydro-thermal-wind power dispatching. • Several improvement strategies are applied to the algorithm. • A parallel computing strategy is applied to improve computational efficiency. • Two cases are analyzed to verify the efficiency of the optimize mode.
Adnan, F. A.; Romlay, F. R. M.; Shafiq, M.
2018-04-01
Owing to the advent of the industrial revolution 4.0, the need for further evaluating processes applied in the additive manufacturing application particularly the computational process for slicing is non-trivial. This paper evaluates a real-time slicing algorithm for slicing an STL formatted computer-aided design (CAD). A line-plane intersection equation was applied to perform the slicing procedure at any given height. The application of this algorithm has found to provide a better computational time regardless the number of facet in the STL model. The performance of this algorithm is evaluated by comparing the results of the computational time for different geometry.
Al-Rajab, Murad; Lu, Joan; Xu, Qiang
2017-07-01
This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Guang-zhou Chen
2015-01-01
Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.
Directory of Open Access Journals (Sweden)
Sabita Chaine
2015-05-01
Full Text Available This work presents a methodology adopted in order to tune the controller parameters of superconducting magnetic energy storage (SMES system in the automatic generation control (AGC of a two-area thermal power system. The gains of integral controllers of AGC loop, proportional controller of SMES loop and gains of the current feedback loop of the inductor in SMES are optimized simultaneously in order to achieve a desired performance. Recently proposed intelligent technique based algorithm known as Cuckoo search algorithm (CSA is applied for optimization. Sensitivity and robustness of the tuned gains tested at different operating conditions prove the effectiveness of fast acting energy storage devices like SMES in damping out oscillations in power system when their controllers are properly tuned.
A novel algorithm for validating peptide identification from a shotgun proteomics search engine.
Jian, Ling; Niu, Xinnan; Xia, Zhonghang; Samir, Parimal; Sumanasekera, Chiranthani; Mu, Zheng; Jennings, Jennifer L; Hoek, Kristen L; Allos, Tara; Howard, Leigh M; Edwards, Kathryn M; Weil, P Anthony; Link, Andrew J
2013-03-01
Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC-MS/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC-MS/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.
Combined heat and power economic dispatch by a fish school search algorithm
Energy Technology Data Exchange (ETDEWEB)
Santos, Leonardo Trigueiro dos; Costa e Silva, Marsil de Athayde [Undergraduate in Mechatronics Engineering, Pontifical Catholic University of Parana, Curitiba, PR (Brazil); Coelho, Leandro dos Santos [Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Parana, Curitiba, PR (Brazil)], e-mail: leandro.coelho@pucpr.br
2010-07-01
The conversion of primary fossil fuels, such as coal and gas, to electricity is a a relatively inefficient process. Even the most modern combined cycle plants can only achieve efficiencies of between 50-60%. A great portion of the energy wasted in this conversion process is released to the environment as waste heat. The principle of combined heat and power, also known as cogeneration, is to recover and make beneficial use of this heat, significantly raising the overall efficiency of the conversion process. However, the optimal utilization of multiple combined heat and power systems is a complicated problem which needs powerful methods to solve. This paper presents a fish school search (FSS) algorithm to solve the combined heat and power economic dispatch problem. FSS is a novel approach recently proposed to perform search in complex optimization problems. Some simulations presented in the literature indicated that FSS can outperform many bio-inspired algorithms, mainly in multimodal functions. The search process in FSS is carried out by a population of limited-memory individuals - the fishes. Each fish represents a possible solution to the problem. Similarly to particle swarm optimization or genetic algorithm, search guidance in FSS is driven by the success of some individual members of the population. A four-unit system proposed recently which is a benchmark case in the power systems field has been validated as a case study in this paper. (author)
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.
International Nuclear Information System (INIS)
Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim
2014-01-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems
Energy Technology Data Exchange (ETDEWEB)
Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
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Dharmbir Prasad
2016-03-01
Full Text Available In this paper, symbiotic organisms search (SOS algorithm is proposed for the solution of optimal power flow (OPF problem of power system equipped with flexible ac transmission systems (FACTS devices. Inspired by interaction between organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. The performance of the proposed SOS algorithm is tested on the modified IEEE-30 bus and IEEE-57 bus test systems incorporating two types of FACTS devices, namely, thyristor controlled series capacitor and thyristor controlled phase shifter at fixed locations. The OPF problem of the present work is formulated with four different objective functions viz. (a fuel cost minimization, (b transmission active power loss minimization, (c emission reduction and (d minimization of combined economic and environmental cost. The simulation results exhibit the potential of the proposed SOS algorithm and demonstrate its effectiveness for solving the OPF problem of power system incorporating FACTS devices over the other evolutionary optimization techniques that surfaced in the recent state-of-the-art literature.
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MANAR Y. KASHMOLA
2012-06-01
Full Text Available The development of hybrid algorithms for solving complex optimization problems focuses on enhancing the strengths and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of these approaches to find superior solutions to solve optimization problems. Optimal routing in communication network is considering a complex optimization problem. In this paper we propose a hybrid Hopfield Neural Network (HNN and Tabu Search (TS algorithm, this algorithm called hybrid HNN-TS algorithm. The paradigm of this hybridization is embedded. We embed the short-term memory and tabu restriction features from TS algorithm in the HNN model. The short-term memory and tabu restriction control the neuron selection process in the HNN model in order to get around the local minima problem and find an optimal solution using the HNN model to solve complex optimization problem. The proposed algorithm is intended to find the optimal path for packet transmission in the network which is fills in the field of routing problem. The optimal path that will be selected is depending on 4-tuples (delay, cost, reliability and capacity. Test results show that the propose algorithm can find path with optimal cost and a reasonable number of iterations. It also shows that the complexity of the network model won’t be a problem since the neuron selection is done heuristically.
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...
An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network.
Cheng, Jing; Xia, Linyuan
2016-08-31
Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm.
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Alejandro MURRIETA-MENDOZA
2017-08-01
Full Text Available With the objective of reducing the flight cost and the amount of polluting emissions released in the atmosphere, a new optimization algorithm considering the climb, cruise and descent phases is presented for the reference vertical flight trajectory. The selection of the reference vertical navigation speeds and altitudes was solved as a discrete combinatory problem by means of a graph-tree passing through nodes using the beam search optimization technique. To achieve a compromise between the execution time and the algorithm’s ability to find the global optimal solution, a heuristic methodology introducing a parameter called “optimism coefficient was used in order to estimate the trajectory’s flight cost at every node. The optimal trajectory cost obtained with the developed algorithm was compared with the cost of the optimal trajectory provided by a commercial flight management system(FMS. The global optimal solution was validated against an exhaustive search algorithm(ESA, other than the proposed algorithm. The developed algorithm takes into account weather effects, step climbs during cruise and air traffic management constraints such as constant altitude segments, constant cruise Mach, and a pre-defined reference lateral navigation route. The aircraft fuel burn was computed using a numerical performance model which was created and validated using flight test experimental data.
Hartzell, S.; Liu, P.
1996-01-01
A method is presented for the simultaneous calculation of slip amplitudes and rupture times for a finite fault using a hybrid global search algorithm. The method we use combines simulated annealing with the downhill simplex method to produce a more efficient search algorithm then either of the two constituent parts. This formulation has advantages over traditional iterative or linearized approaches to the problem because it is able to escape local minima in its search through model space for the global optimum. We apply this global search method to the calculation of the rupture history for the Landers, California, earthquake. The rupture is modeled using three separate finite-fault planes to represent the three main fault segments that failed during this earthquake. Both the slip amplitude and the time of slip are calculated for a grid work of subfaults. The data used consist of digital, teleseismic P and SH body waves. Long-period, broadband, and short-period records are utilized to obtain a wideband characterization of the source. The results of the global search inversion are compared with a more traditional linear-least-squares inversion for only slip amplitudes. We use a multi-time-window linear analysis to relax the constraints on rupture time and rise time in the least-squares inversion. Both inversions produce similar slip distributions, although the linear-least-squares solution has a 10% larger moment (7.3 ?? 1026 dyne-cm compared with 6.6 ?? 1026 dyne-cm). Both inversions fit the data equally well and point out the importance of (1) using a parameterization with sufficient spatial and temporal flexibility to encompass likely complexities in the rupture process, (2) including suitable physically based constraints on the inversion to reduce instabilities in the solution, and (3) focusing on those robust rupture characteristics that rise above the details of the parameterization and data set.
Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems
Hazra, Abhik; Das, Saborni; Basu, Mousumi
2018-03-01
This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.
Algorithms for searching Fast radio bursts and pulsars in tight binary systems.
Zackay, Barak
2017-01-01
Fast radio bursts (FRB's) are an exciting, recently discovered, astrophysical transients which their origins are unknown.Currently, these bursts are believed to be coming from cosmological distances, allowing us to probe the electron content on cosmological length scales. Even though their precise localization is crucial for the determination of their origin, radio interferometers were not extensively employed in searching for them due to computational limitations.I will briefly present the Fast Dispersion Measure Transform (FDMT) algorithm,that allows to reduce the operation count in blind incoherent dedispersion by 2-3 orders of magnitude.In addition, FDMT enables to probe the unexplored domain of sub-microsecond astrophysical pulses.Pulsars in tight binary systems are among the most important astrophysical objects as they provide us our best tests of general relativity in the strong field regime.I will provide a preview to a novel algorithm that enables the detection of pulsars in short binary systems using observation times longer than an orbital period.Current pulsar search programs limit their searches for integration times shorter than a few percents of the orbital period.Until now, searching for pulsars in binary systems using observation times longer than an orbital period was considered impossible as one has to blindly enumerate all options for the Keplerian parameters, the pulsar rotation period, and the unknown DM.Using the current state of the art pulsar search techniques and all computers on the earth, such an enumeration would take longer than a Hubble time. I will demonstrate that using the new algorithm, it is possible to conduct such an enumeration on a laptop using real data of the double pulsar PSR J0737-3039.Among the other applications of this algorithm are:1) Searching for all pulsars on all sky positions in gamma ray observations of the Fermi LAT satellite.2) Blind searching for continuous gravitational wave sources emitted by pulsars with
Certain integrable system on a space associated with a quantum search algorithm
International Nuclear Information System (INIS)
Uwano, Y.; Hino, H.; Ishiwatari, Y.
2007-01-01
On thinking up a Grover-type quantum search algorithm for an ordered tuple of multiqubit states, a gradient system associated with the negative von Neumann entropy is studied on the space of regular relative configurations of multiqubit states (SR 2 CMQ). The SR 2 CMQ emerges, through a geometric procedure, from the space of ordered tuples of multiqubit states for the quantum search. The aim of this paper is to give a brief report on the integrability of the gradient dynamical system together with quantum information geometry of the underlying space, SR 2 CMQ, of that system
Power to the People! Meta-algorithmic modelling in applied data science
Spruit, M.; Jagesar, R.
2016-01-01
This position paper first defines the research field of applied data science at the intersection of domain expertise, data mining, and engineering capabilities, with particular attention to analytical applications. We then propose a meta-algorithmic approach for applied data science with societal
Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
V. D. Sulimov
2014-01-01
Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search
Directory of Open Access Journals (Sweden)
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.
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Mohammad Taghi Ameli
2012-01-01
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
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.
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 Prefiltered Cuckoo Search Algorithm with Geometric Operators for Solving Sudoku Problems
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Ricardo Soto
2014-01-01
Full Text Available The Sudoku is a famous logic-placement game, originally popularized in Japan and today widely employed as pastime and as testbed for search algorithms. The classic Sudoku consists in filling a 9×9 grid, divided into nine 3×3 regions, so that each column, row, and region contains different digits from 1 to 9. This game is known to be NP-complete, with existing various complete and incomplete search algorithms able to solve different instances of it. In this paper, we present a new cuckoo search algorithm for solving Sudoku puzzles combining prefiltering phases and geometric operations. The geometric operators allow one to correctly move toward promising regions of the combinatorial space, while the prefiltering phases are able to previously delete from domains the values that do not conduct to any feasible solution. This integration leads to a more efficient domain filtering and as a consequence to a faster solving process. We illustrate encouraging experimental results where our approach noticeably competes with the best approximate methods reported in the literature.
Directory of Open Access Journals (Sweden)
Zhiwei Ye
2015-01-01
Full Text Available Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
An improved algorithm for searching all minimal cuts in modified networks
International Nuclear Information System (INIS)
Yeh, W.-C.
2008-01-01
A modified network is an updated network after inserting a branch string (a special path) between two nodes in the original network. Modifications are common for network expansion or reinforcement evaluation and planning. The problem of searching all minimal cuts (MCs) in a modified network is discussed and solved in this study. The existing best-known methods for solving this problem either needed extensive comparison and verification or failed to solve some special but important cases. Therefore, a more efficient, intuitive and generalized method for searching all MCs without an extensive research procedure is proposed. In this study, we first develop an intuitive algorithm based upon the reformation of all MCs in the original network to search for all MCs in a modified network. Next, the correctness of the proposed algorithm will be analyzed and proven. The computational complexity of the proposed algorithm is analyzed and compared with the existing best-known methods. Finally, two examples illustrate how all MCs are generated in a modified network using the information of all of the MCs in the corresponding original network
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
Voytishek, Anton V.; Shipilov, Nikolay M.
2017-11-01
In this paper, the systematization of numerical (implemented on a computer) randomized functional algorithms for approximation of a solution of Fredholm integral equation of the second kind is carried out. Wherein, three types of such algorithms are distinguished: the projection, the mesh and the projection-mesh methods. The possibilities for usage of these algorithms for solution of practically important problems is investigated in detail. The disadvantages of the mesh algorithms, related to the necessity of calculation values of the kernels of integral equations in fixed points, are identified. On practice, these kernels have integrated singularities, and calculation of their values is impossible. Thus, for applied problems, related to solving Fredholm integral equation of the second kind, it is expedient to use not mesh, but the projection and the projection-mesh randomized algorithms.
Expeditious 3D poisson vlasov algorithm applied to ion extraction from a plasma
International Nuclear Information System (INIS)
Whealton, J.H.; McGaffey, R.W.; Meszaros, P.S.
1983-01-01
A new 3D Poisson Vlasov algorithm is under development which differs from a previous algorithm, referenced in this paper, in two respects: the mesh lines are cartesian, and the Poisson equation is solved iteratively. The resulting algorithm has been used to examine the same boundary value problem as considered in the earlier algorithm except that the number of nodes is 2 times greater. The same physical results were obtained except the computational time was reduced by a factor of 60 and the memory requirement was reduced by a factor of 10. This algorithm at present restricts Neumann boundary conditions to orthogonal planes lying along mesh lines. No such restriction applies to Dirichlet boundaries. An emittance diagram is shown below where those points lying on the y = 0 line start on the axis of symmetry and those near the y = 1 line start near the slot end
Directory of Open Access Journals (Sweden)
Rabindra Kumar Sahu
2014-09-01
Full Text Available An attempt is made for the effective application of Gravitational Search Algorithm (GSA to optimize PI/PIDF controller parameters in Automatic Generation Control (AGC of interconnected power systems. Initially, comparison of several conventional objective functions reveals that ITAE yields better system performance. Then, the parameters of GSA technique are properly tuned and the GSA control parameters are proposed. The superiority of the proposed approach is demonstrated by comparing the results of some recently published techniques such as Differential Evolution (DE, Bacteria Foraging Optimization Algorithm (BFOA and Genetic Algorithm (GA. Additionally, sensitivity analysis is carried out that demonstrates the robustness of the optimized controller parameters to wide variations in operating loading condition and time constants of speed governor, turbine, tie-line power. Finally, the proposed approach is extended to a more realistic power system model by considering the physical constraints such as reheat turbine, Generation Rate Constraint (GRC and Governor Dead Band nonlinearity.
An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm
Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin
2018-04-01
Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.
THE ALGORITHM AND PROGRAM OF M-MATRICES SEARCH AND STUDY
Directory of Open Access Journals (Sweden)
Y. N. Balonin
2013-05-01
Full Text Available The algorithm and software for search and study of orthogonal bases matrices – minimax matrices (M-matrix are considered. The algorithm scheme is shown, comments on calculation blocks are given, and interface of the MMatrix software system developed with participation of the authors is explained. The results of the universal algorithm work are presented as Hadamard matrices, Belevitch matrices (C-matrices, conference matrices and matrices of even and odd orders complementary and closely related to those ones by their properties, in particular, the matrix of the 22-th order for which there is no C-matrix. Examples of portraits for alternative matrices of the 255-th and the 257-th orders are given corresponding to the sequences of Mersenne and Fermat numbers. A new way to get Hadamard matrices is explained, different from the previously known procedures based on iterative processes and calculations of Lagrange symbols, with theoretical and practical meaning.
Directory of Open Access Journals (Sweden)
Jamal Abd Ali
2015-11-01
Full Text Available This paper presents a novel lightning search algorithm (LSA using quantum mechanics theories to generate a quantum-inspired LSA (QLSA. The QLSA improves the searching of each step leader to obtain the best position for a projectile. To evaluate the reliability and efficiency of the proposed algorithm, the QLSA is tested using eighteen benchmark functions with various characteristics. The QLSA is applied to improve the design of the fuzzy logic controller (FLC for controlling the speed response of the induction motor drive. The proposed algorithm avoids the exhaustive conventional trial-and-error procedure for obtaining membership functions (MFs. The generated adaptive input and output MFs are implemented in the fuzzy speed controller design to formulate the objective functions. Mean absolute error (MAE of the rotor speed is the objective function of optimization controller. An optimal QLSA-based FLC (QLSAF optimization controller is employed to tune and minimize the MAE, thereby improving the performance of the induction motor with the change in speed and mechanical load. To validate the performance of the developed controller, the results obtained with the QLSAF are compared to the results obtained with LSA, the backtracking search algorithm (BSA, the gravitational search algorithm (GSA, the particle swarm optimization (PSO and the proportional integral derivative controllers (PID, respectively. Results show that the QLASF outperforms the other control methods in all of the tested cases in terms of damping capability and transient response under different mechanical loads and speeds.
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.
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.
Energy Technology Data Exchange (ETDEWEB)
Wang, Cheng-Der, E-mail: jdwang@iner.gov.tw [Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan, ROC (China); Lin, Chaung [National Tsing Hua University, Department of Engineering and System Science, 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan (China)
2013-02-15
Highlights: ► The PSO algorithm was adopted to automatically design a BWR CRP. ► The local search procedure was added to improve the result of PSO algorithm. ► The results show that the obtained CRP is the same good as that in the previous work. -- Abstract: This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained.
Directory of Open Access Journals (Sweden)
Ligang Cui
2013-01-01
Full Text Available The capacitated vehicle routing problem (CVRP is the most classical vehicle routing problem (VRP; many solution techniques are proposed to find its better answer. In this paper, a new improved quantum evolution algorithm (IQEA with a mixed local search procedure is proposed for solving CVRPs. First, an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum Not gate is constructed to initialize and generate feasible solutions. Then, to further strengthen IQEA's searching ability, three local search procedures 1-1 exchange, 1-0 exchange, and 2-OPT, are adopted. Experiments on a small case have been conducted to analyze the sensitivity of main parameters and compare the performances of the IQEA with different local search strategies. Together with results from the testing of CVRP benchmarks, the superiorities of the proposed algorithm over the PSO, SR-1, and SR-2 have been demonstrated. At last, a profound analysis of the experimental results is presented and some suggestions on future researches are given.
Directory of Open Access Journals (Sweden)
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
Modified Cuckoo Search Algorithm for Solving Nonconvex Economic Load Dispatch Problems
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Thang Trung Nguyen
2016-01-01
Full Text Available This paper presents the application of modified cuckoo search algorithm (MCSA for solving economic load dispatch (ELD problems. The MCSA method is developed to improve the search ability and solution quality of the conventional CSA method. In the MCSA, the evaluation of eggs has divided the initial eggs into two groups, the top egg group with good quality and the abandoned group with worse quality. Moreover, the value of the updated step size in MCSA is adapted as generating a new solution for the abandoned group and the top group via the Levy flights so that a large zone is searched at the beginning and a local zone is foraged as the maximum number of iterations is nearly reached. The MCSA method has been tested on different systems with different characteristics of thermal units and constraints. The result comparison with other methods in the literature has indicated that the MCSA method can be a powerful method for solving the ELD.
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Srikanta Mahapatra
2014-12-01
Full Text Available In this paper, a novel hybrid Firefly Algorithm and Pattern Search (h-FAPS technique is proposed for a Static Synchronous Series Compensator (SSSC-based power oscillation damping controller design. The proposed h-FAPS technique takes the advantage of global search capability of FA and local search facility of PS. In order to tackle the drawback of using the remote signal that may impact reliability of the controller, a modified signal equivalent to the remote speed deviation signal is constructed from the local measurements. The performances of the proposed controllers are evaluated in SMIB and multi-machine power system subjected to various transient disturbances. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with some recently published approaches such as Differential Evolution (DE and Particle Swarm Optimization (PSO. It is observed that the proposed approach yield superior damping performance compared to some recently reported approaches.
International Nuclear Information System (INIS)
Chan, Apple L.S.; Hanby, Vic I.; Chow, T.T.
2007-01-01
A district cooling system is a sustainable means of distribution of cooling energy through mass production. A cooling medium like chilled water is generated at a central refrigeration plant and supplied to serve a group of consumer buildings through a piping network. Because of the substantial capital investment involved, an optimal design of the distribution piping configuration is one of the crucial factors for successful implementation of the district cooling scheme. In the present study, genetic algorithm (GA) incorporated with local search techniques was developed to find the optimal/near optimal configuration of the piping network in a hypothetical site. The effect of local search, mutation rate and frequency of local search on the performance of the GA in terms of both solution quality and computation time were investigated and presented in this paper
PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
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Huu-Khoa Tran
2016-09-01
Full Text Available Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO-based algorithm and the evolutionary programming (EP algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models.
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.
MIDAS: a database-searching algorithm for metabolite identification in metabolomics.
Wang, Yingfeng; Kora, Guruprasad; Bowen, Benjamin P; Pan, Chongle
2014-10-07
A database searching approach can be used for metabolite identification in metabolomics by matching measured tandem mass spectra (MS/MS) against the predicted fragments of metabolites in a database. Here, we present the open-source MIDAS algorithm (Metabolite Identification via Database Searching). To evaluate a metabolite-spectrum match (MSM), MIDAS first enumerates possible fragments from a metabolite by systematic bond dissociation, then calculates the plausibility of the fragments based on their fragmentation pathways, and finally scores the MSM to assess how well the experimental MS/MS spectrum from collision-induced dissociation (CID) is explained by the metabolite's predicted CID MS/MS spectrum. MIDAS was designed to search high-resolution tandem mass spectra acquired on time-of-flight or Orbitrap mass spectrometer against a metabolite database in an automated and high-throughput manner. The accuracy of metabolite identification by MIDAS was benchmarked using four sets of standard tandem mass spectra from MassBank. On average, for 77% of original spectra and 84% of composite spectra, MIDAS correctly ranked the true compounds as the first MSMs out of all MetaCyc metabolites as decoys. MIDAS correctly identified 46% more original spectra and 59% more composite spectra at the first MSMs than an existing database-searching algorithm, MetFrag. MIDAS was showcased by searching a published real-world measurement of a metabolome from Synechococcus sp. PCC 7002 against the MetaCyc metabolite database. MIDAS identified many metabolites missed in the previous study. MIDAS identifications should be considered only as candidate metabolites, which need to be confirmed using standard compounds. To facilitate manual validation, MIDAS provides annotated spectra for MSMs and labels observed mass spectral peaks with predicted fragments. The database searching and manual validation can be performed online at http://midas.omicsbio.org.
New approaches of the potential field for QPSO algorithm applied to nuclear reactor reload problem
International Nuclear Information System (INIS)
Nicolau, Andressa dos Santos; Schirru, Roberto
2015-01-01
Recently quantum-inspired version of the Particle Swarm Optimization (PSO) algorithm, Quantum Particle Swarm Optimization (QPSO) was proposed. The QPSO algorithm permits all particles to have a quantum behavior, where some sort of 'quantum motion' is imposed in the search process. When the QPSO is tested against a set of benchmarking functions, it showed superior performances as compared to classical PSO. The QPSO outperforms the classical one most of the time in convergence speed and achieves better levels for the fitness functions. The great advantage of QPSO algorithm is that it uses only one parameter control. The critical step or QPSO algorithm is the choice of suitable attractive potential field that can guarantee bound states for the particles moving in the quantum environment. In this article, one version of QPSO algorithm was tested with two types of potential well: delta-potential well harmonic oscillator. The main goal of this study is to show with of the potential field is the most suitable for use in QPSO in a solution of the Nuclear Reactor Reload Optimization Problem, especially in the cycle 7 of a Brazilian Nuclear Power Plant. All result were compared with the performance of its classical counterpart of the literature and shows that QPSO algorithm are well situated among the best alternatives for dealing with hard optimization problems, such as NRROP. (author)
New approaches of the potential field for QPSO algorithm applied to nuclear reactor reload problem
Energy Technology Data Exchange (ETDEWEB)
Nicolau, Andressa dos Santos; Schirru, Roberto, E-mail: andressa@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear
2015-07-01
Recently quantum-inspired version of the Particle Swarm Optimization (PSO) algorithm, Quantum Particle Swarm Optimization (QPSO) was proposed. The QPSO algorithm permits all particles to have a quantum behavior, where some sort of 'quantum motion' is imposed in the search process. When the QPSO is tested against a set of benchmarking functions, it showed superior performances as compared to classical PSO. The QPSO outperforms the classical one most of the time in convergence speed and achieves better levels for the fitness functions. The great advantage of QPSO algorithm is that it uses only one parameter control. The critical step or QPSO algorithm is the choice of suitable attractive potential field that can guarantee bound states for the particles moving in the quantum environment. In this article, one version of QPSO algorithm was tested with two types of potential well: delta-potential well harmonic oscillator. The main goal of this study is to show with of the potential field is the most suitable for use in QPSO in a solution of the Nuclear Reactor Reload Optimization Problem, especially in the cycle 7 of a Brazilian Nuclear Power Plant. All result were compared with the performance of its classical counterpart of the literature and shows that QPSO algorithm are well situated among the best alternatives for dealing with hard optimization problems, such as NRROP. (author)
Energy Technology Data Exchange (ETDEWEB)
Esquivel-Estrada, Jaime, E-mail: jaime.esquivel@fi.uaemex.m [Facultad de Ingenieria, Universidad Autonoma del Estado de Mexico, Cerro de Coatepec S/N, Toluca de Lerdo, Estado de Mexico 50000 (Mexico); Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Ortiz-Servin, Juan Jose, E-mail: juanjose.ortiz@inin.gob.m [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Castillo, Jose Alejandro; Perusquia, Raul [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico)
2011-01-15
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
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)
R. A. Swief; T. S. Abdel-Salam; Noha H. El-Amary
2018-01-01
This paper presents an efficient Cuckoo Search Optimization technique to improve the reliability of electrical power systems. Various reliability objective indices such as Energy Not Supplied, System Average Interruption Frequency Index, System Average Interruption, and Duration Index are the main indices indicating reliability. The Cuckoo Search Optimization (CSO) technique is applied to optimally place the protection devices, install the distributed generators, and to determine the size of ...
Directory of Open Access Journals (Sweden)
J. L. Guardado
2014-01-01
Full Text Available Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2 and the Nondominated Sorting Genetic Algorithm II (NSGA-II. The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.
Guardado, J L; Rivas-Davalos, F; Torres, J; Maximov, S; Melgoza, E
2014-01-01
Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.
Cuckoo Search with flight of Levy applied to the problem of reload of fuels in nuclear reactors
International Nuclear Information System (INIS)
Silva, Patrick V.; Nast, Fernando N.; Schirru, Roberto; Meneses, Anderson A.M.; Coordenacao de Pos-Graduacao e Pesquisa de Engenharia
2017-01-01
Intra-Nuclear Fuel Management Optimization is a complex combinatorial problem of the NP-difficult type, associated with the refueling process of a nuclear reactor, which aims to extend the cycle of operation by determining loading patterns, obeying safety margins. In addition to the combinatorial problem, we have the aspect of calculations of reactor physics, which increases the difficult of OGCIN. Methods that are proving effective when applied to OGCIN are the algorithms belonging to the swarm intelligence paradigm. A new member of this paradigm is Cuckoo Search (CS), which has shown results promising when applied to optimization issues. The CS is based on the litter parasitism of some cuckoo species combined with the Levy flight behavior of some birds. In the present work we present the results of the application of CS to OGCIN, and compare them to the results obtained by the application of ABC
Algorithm of axial fuel optimization based in progressive steps of turned search
International Nuclear Information System (INIS)
Martin del Campo, C.; Francois, J.L.
2003-01-01
The development of an algorithm for the axial optimization of fuel of boiling water reactors (BWR) is presented. The algorithm is based in a serial optimizations process in the one that the best solution in each stage is the starting point of the following stage. The objective function of each stage adapts to orient the search toward better values of one or two parameters leaving the rest like restrictions. Conform to it advances in those optimization stages, it is increased the fineness of the evaluation of the investigated designs. The algorithm is based on three stages, in the first one are used Genetic algorithms and in the two following Tabu Search. The objective function of the first stage it looks for to minimize the average enrichment of the one it assembles and to fulfill with the generation of specified energy for the operation cycle besides not violating none of the limits of the design base. In the following stages the objective function looks for to minimize the power factor peak (PPF) and to maximize the margin of shutdown (SDM), having as restrictions the one average enrichment obtained for the best design in the first stage and those other restrictions. The third stage, very similar to the previous one, it begins with the design of the previous stage but it carries out a search of the margin of shutdown to different exhibition steps with calculations in three dimensions (3D). An application to the case of the design of the fresh assemble for the fourth fuel reload of the Unit 1 reactor of the Laguna Verde power plant (U1-CLV) is presented. The obtained results show an advance in the handling of optimization methods and in the construction of the objective functions that should be used for the different design stages of the fuel assemblies. (Author)
Directory of Open Access Journals (Sweden)
Hailong Wang
2018-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.
Kota, Sujatha; Padmanabhuni, Venkata Nageswara Rao; Budda, Kishor; K, Sruthi
2018-05-01
Elliptic Curve Cryptography (ECC) uses two keys private key and public key and is considered as a public key cryptographic algorithm that is used for both authentication of a person and confidentiality of data. Either one of the keys is used in encryption and other in decryption depending on usage. Private key is used in encryption by the user and public key is used to identify user in the case of authentication. Similarly, the sender encrypts with the private key and the public key is used to decrypt the message in case of confidentiality. Choosing the private key is always an issue in all public key Cryptographic Algorithms such as RSA, ECC. If tiny values are chosen in random the security of the complete algorithm becomes an issue. Since the Public key is computed based on the Private Key, if they are not chosen optimally they generate infinity values. The proposed Modified Elliptic Curve Cryptography uses selection in either of the choices; the first option is by using Particle Swarm Optimization and the second option is by using Cuckoo Search Algorithm for randomly choosing the values. The proposed algorithms are developed and tested using sample database and both are found to be secured and reliable. The test results prove that the private key is chosen optimally not repetitive or tiny and the computations in public key will not reach infinity.
Directory of Open Access Journals (Sweden)
Behnam Barzegar
2012-01-01
Full Text Available Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN that is based on gravitational search algorithm (GSA. In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.
Directory of Open Access Journals (Sweden)
Zhigang Lian
2010-01-01
Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.
International Nuclear Information System (INIS)
Wang, Zhe; Li, Yanzhong
2015-01-01
Highlights: • The first application of IMOCS for plate-fin heat exchanger design. • Irreversibility degrees of heat transfer and fluid friction are minimized. • Trade-off of efficiency, total cost and pumping power is achieved. • Both EGM and EDM methods have been compared in the optimization of PFHE. • This study has superiority over other single-objective optimization design. - Abstract: This paper introduces and applies an improved multi-objective cuckoo search (IMOCS) algorithm, a novel met-heuristic optimization algorithm based on cuckoo breeding behavior, for the multi-objective optimization design of plate-fin heat exchangers (PFHEs). A modified irreversibility degree of the PFHE is separated into heat transfer and fluid friction irreversibility degrees which are adopted as two initial objective functions to be minimized simultaneously for narrowing the search scope of the design. The maximization efficiency, minimization of pumping power, and total annual cost are considered final objective functions. Results obtained from a two dimensional normalized Pareto-optimal frontier clearly demonstrate the trade-off between heat transfer and fluid friction irreversibility. Moreover, a three dimensional Pareto-optimal frontier reveals a relationship between efficiency, total annual cost, and pumping power in the PFHE design. Three examples presented here further demonstrate that the presented method is able to obtain optimum solutions with higher accuracy, lower irreversibility, and fewer iterations as compared to the previous methods and single-objective design approaches
Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data
Chierici, F.; Embriaco, D.; Morucci, S.
2017-12-01
Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.
System network planning expansion using mathematical programming, genetic algorithms and tabu search
International Nuclear Information System (INIS)
Sadegheih, A.; Drake, P.R.
2008-01-01
In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA's give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used
Neural Network Blind Equalization Algorithm Applied in Medical CT Image Restoration
Directory of Open Access Journals (Sweden)
Yunshan Sun
2013-01-01
Full Text Available A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI. In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.
International Nuclear Information System (INIS)
Wu, Xia; Wu, Genhua
2014-01-01
Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron
Defining Algorithmic Ideology: Using Ideology Critique to Scrutinize Corporate Search Engines
Directory of Open Access Journals (Sweden)
Astrid Mager
2014-02-01
Full Text Available This article conceptualizes “algorithmic ideology” as a valuable tool to understand and critique corporate search engines in the context of wider socio-political developments. Drawing on critical theory it shows how capitalist value-systems manifest in search technology, how they spread through algorithmic logics and how they are stabilized in society. Following philosophers like Althusser, Marx and Gramsci it elaborates how content providers and users contribute to Google’s capital accumulation cycle and exploitation schemes that come along with it. In line with contemporary mass media and neoliberal politics they appear to be fostering capitalism and its “commodity fetishism” (Marx. It further reveals that the capitalist hegemony has to be constantly negotiated and renewed. This dynamic notion of ideology opens up the view for moments of struggle and counter-actions. “Organic intellectuals” (Gramsci can play a central role in challenging powerful actors like Google and their algorithmic ideology. To pave the way towards more democratic information technology, however, requires more than single organic intellectuals. Additional obstacles need to be conquered, as I finally discuss.
Directory of Open Access Journals (Sweden)
Reza Sirjani
2017-09-01
Full Text Available Currently, many wind farms exist throughout the world and, in some cases, supply a significant portion of energy to networks. However, numerous uncertainties remain with respect to the amount of energy generated by wind turbines and other sophisticated operational aspects, such as voltage and reactive power management, which requires further development and consideration. To fix the problem of poor reactive power compensation in wind farms, optimal capacitor placement has been proposed in existing wind farms as a simple and relatively inexpensive method. However, the use of induction generators, transformers, and additional capacitors represent potential problems for the harmonics of a system and therefore must be taken into account at wind farms. The optimal location and size of capacitors at buses of an 80-MW wind farm were determined according to modelled wind speed, system equivalent circuits, and harmonics in order to minimize energy losses, optimize reactive power and reduce the management costs. The discrete version of the lightning search algorithm (DLSA is a powerful and flexible nature-inspired optimization technique that was developed and implemented herein for optimal capacitor placement in wind farms. The obtained results are compared with the results of the genetic algorithm (GA and the discrete harmony search algorithm (DHSA.
Directory of Open Access Journals (Sweden)
Xingsheng Gu
2013-03-01
Full Text Available he accurate forecasting of carbon dioxide (CO2 emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS algorithm-based discounted mean square forecast error (DMSFE combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO2 emission of the World top‒5 CO2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE and mean absolute error (MAE are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.
O'Loughlin, Declan; Oliveira, Bárbara L; Elahi, Muhammad Adnan; Glavin, Martin; Jones, Edward; Popović, Milica; O'Halloran, Martin
2017-12-06
Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.
ACTION OF UNIFORM SEARCH ALGORITHM WHEN SELECTING LANGUAGE UNITS IN THE PROCESS OF SPEECH
Directory of Open Access Journals (Sweden)
Ирина Михайловна Некипелова
2013-05-01
Full Text Available The article is devoted to research of action of uniform search algorithm when selecting by human of language units for speech produce. The process is connected with a speech optimization phenomenon. This makes it possible to shorten the time of cogitation something that human want to say, and to achieve the maximum precision in thoughts expression. The algorithm of uniform search works at consciousness and subconsciousness levels. It favours the forming of automatism produce and perception of speech. Realization of human's cognitive potential in the process of communication starts up complicated mechanism of self-organization and self-regulation of language. In turn, it results in optimization of language system, servicing needs not only human's self-actualization but realization of communication in society. The method of problem-oriented search is used for researching of optimization mechanisms, which are distinctive to speech producing and stabilization of language.DOI: http://dx.doi.org/10.12731/2218-7405-2013-4-50
The Research and Test of Fast Radio Burst Real-time Search Algorithm Based on GPU Acceleration
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.
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.
Parameter Estimation for Traffic Noise Models Using a Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
Deok-Soon An
2013-01-01
Full Text Available A technique has been developed for predicting road traffic noise for environmental assessment, taking into account traffic volume as well as road surface conditions. The ASJ model (ASJ Prediction Model for Road Traffic Noise, 1999, which is based on the sound power level of the noise emitted by the interaction between the road surface and tires, employs regression models for two road surface types: dense-graded asphalt (DGA and permeable asphalt (PA. However, these models are not applicable to other types of road surfaces. Accordingly, this paper introduces a parameter estimation procedure for ASJ-based noise prediction models, utilizing a harmony search (HS algorithm. Traffic noise measurement data for four different vehicle types were used in the algorithm to determine the regression parameters for several road surface types. The parameters of the traffic noise prediction models were evaluated using another measurement set, and good agreement was observed between the predicted and measured sound power levels.
Park, Won-Kwang; Kim, Hwa Pyung; Lee, Kwang-Jae; Son, Seong-Ho
2017-11-01
Motivated by the biomedical engineering used in early-stage breast cancer detection, we investigated the use of MUltiple SIgnal Classification (MUSIC) algorithm for location searching of small anomalies using S-parameters. We considered the application of MUSIC to functional imaging where a small number of dipole antennas are used. Our approach is based on the application of Born approximation or physical factorization. We analyzed cases in which the anomaly is respectively small and large in relation to the wavelength, and the structure of the left-singular vectors is linked to the nonzero singular values of a Multi-Static Response (MSR) matrix whose elements are the S-parameters. Using simulations, we demonstrated the strengths and weaknesses of the MUSIC algorithm in detecting both small and extended anomalies.
Continuous grasp algorithm applied to economic dispatch problem of thermal units
Energy Technology Data Exchange (ETDEWEB)
Vianna Neto, Julio Xavier [Pontifical Catholic University of Parana - PUCPR, Curitiba, PR (Brazil). Undergraduate Program at Mechatronics Engineering; Bernert, Diego Luis de Andrade; Coelho, Leandro dos Santos [Pontifical Catholic University of Parana - PUCPR, Curitiba, PR (Brazil). Industrial and Systems Engineering Graduate Program, LAS/PPGEPS], e-mail: leandro.coelho@pucpr.br
2010-07-01
The economic dispatch problem (EDP) is one of the fundamental issues in power systems to obtain benefits with the stability, reliability and security. Its objective is to allocate the power demand among committed generators in the most economical manner, while all physical and operational constraints are satisfied. The cost of power generation, particularly in fossil fuel plants, is very high and economic dispatch helps in saving a significant amount of revenue. Recently, as an alternative to the conventional mathematical approaches, modern heuristic optimization techniques such as simulated annealing, evolutionary algorithms, neural networks, ant colony, and tabu search have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. On other hand, continuous GRASP (C-GRASP) is a stochastic local search meta-heuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints. Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. The C-GRASP algorithm is validated for a test system consisting of fifteen units, test system that takes into account spinning reserve and prohibited operating zones constrains. (author)
Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions
Latos, Dorota; Kolanowski, Bogdan; Pachelski, Wojciech; Sołoducha, Ryszard
2017-12-01
Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object's behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).
A meta-heuristic method for solving scheduling problem: crow search algorithm
Adhi, Antono; Santosa, Budi; Siswanto, Nurhadi
2018-04-01
Scheduling is one of the most important processes in an industry both in manufacturingand services. The scheduling process is the process of selecting resources to perform an operation on tasks. Resources can be machines, peoples, tasks, jobs or operations.. The selection of optimum sequence of jobs from a permutation is an essential issue in every research in scheduling problem. Optimum sequence becomes optimum solution to resolve scheduling problem. Scheduling problem becomes NP-hard problem since the number of job in the sequence is more than normal number can be processed by exact algorithm. In order to obtain optimum results, it needs a method with capability to solve complex scheduling problems in an acceptable time. Meta-heuristic is a method usually used to solve scheduling problem. The recently published method called Crow Search Algorithm (CSA) is adopted in this research to solve scheduling problem. CSA is an evolutionary meta-heuristic method which is based on the behavior in flocks of crow. The calculation result of CSA for solving scheduling problem is compared with other algorithms. From the comparison, it is found that CSA has better performance in term of optimum solution and time calculation than other algorithms.
VES/TEM 1D joint inversion by using Controlled Random Search (CRS) algorithm
Bortolozo, Cassiano Antonio; Porsani, Jorge Luís; Santos, Fernando Acácio Monteiro dos; Almeida, Emerson Rodrigo
2015-01-01
Electrical (DC) and Transient Electromagnetic (TEM) soundings are used in a great number of environmental, hydrological, and mining exploration studies. Usually, data interpretation is accomplished by individual 1D models resulting often in ambiguous models. This fact can be explained by the way as the two different methodologies sample the medium beneath surface. Vertical Electrical Sounding (VES) is good in marking resistive structures, while Transient Electromagnetic sounding (TEM) is very sensitive to conductive structures. Another difference is VES is better to detect shallow structures, while TEM soundings can reach deeper layers. A Matlab program for 1D joint inversion of VES and TEM soundings was developed aiming at exploring the best of both methods. The program uses CRS - Controlled Random Search - algorithm for both single and 1D joint inversions. Usually inversion programs use Marquadt type algorithms but for electrical and electromagnetic methods, these algorithms may find a local minimum or not converge. Initially, the algorithm was tested with synthetic data, and then it was used to invert experimental data from two places in Paraná sedimentary basin (Bebedouro and Pirassununga cities), both located in São Paulo State, Brazil. Geoelectric model obtained from VES and TEM data 1D joint inversion is similar to the real geological condition, and ambiguities were minimized. Results with synthetic and real data show that 1D VES/TEM joint inversion better recovers simulated models and shows a great potential in geological studies, especially in hydrogeological studies.
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.
A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Youchuan Wan
2016-01-01
Full Text Available Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA and particle swarm optimization (PSO easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA. The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO, and artificial immune algorithm (AIA. Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.
Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.
2015-01-01
This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.
Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions
Directory of Open Access Journals (Sweden)
Latos Dorota
2017-12-01
Full Text Available Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar.
A Rule-Based Local Search Algorithm for General Shift Design Problems in Airport Ground Handling
DEFF Research Database (Denmark)
Clausen, Tommy
We consider a generalized version of the shift design problem where shifts are created to cover a multiskilled demand and fit the parameters of the workforce. We present a collection of constraints and objectives for the generalized shift design problem. A local search solution framework with mul......We consider a generalized version of the shift design problem where shifts are created to cover a multiskilled demand and fit the parameters of the workforce. We present a collection of constraints and objectives for the generalized shift design problem. A local search solution framework...... with multiple neighborhoods and a loosely coupled rule engine based on simulated annealing is presented. Computational experiments on real-life data from various airport ground handling organization show the performance and flexibility of the proposed algorithm....
An inertia-free filter line-search algorithm for large-scale nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
Chiang, Nai-Yuan; Zavala, Victor M.
2016-02-15
We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.
A Hybrid Differential Evolution and Tree Search Algorithm for the Job Shop Scheduling Problem
Directory of Open Access Journals (Sweden)
Rui Zhang
2011-01-01
Full Text Available The job shop scheduling problem (JSSP is a notoriously difficult problem in combinatorial optimization. In terms of the objective function, most existing research has been focused on the makespan criterion. However, in contemporary manufacturing systems, due-date-related performances are more important because they are essential for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a hybrid differential evolution (DE algorithm is proposed for the problem. To enhance the overall search efficiency, a neighborhood property of the problem is discovered, and then a tree search procedure is designed and embedded into the DE framework. According to the extensive computational experiments, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness objective.
Schneider, Sébastien; Jacques, Diederik; Mallants, Dirk
2010-05-01
Numerical models are of precious help for predicting water fluxes in the vadose zone and more specifically in Soil-Vegetation-Atmosphere (SVA) systems. For such simulations, robust models and representative soil hydraulic parameters are required. Calibration of unsaturated hydraulic properties is known to be a difficult optimization problem due to the high non-linearity of the water flow equations. Therefore, robust methods are needed to avoid the optimization process to lead to non-optimal parameters. Evolutionary algorithms and specifically genetic algorithms (GAs) are very well suited for those complex parameter optimization problems. Additionally, GAs offer the opportunity to assess the confidence in the hydraulic parameter estimations, because of the large number of model realizations. The SVA system in this study concerns a pine stand on a heterogeneous sandy soil (podzol) in the Campine region in the north of Belgium. Throughfall and other meteorological data and water contents at different soil depths have been recorded during one year at a daily time step in two lysimeters. The water table level, which is varying between 95 and 170 cm, has been recorded with intervals of 0.5 hour. The leaf area index was measured as well at some selected time moments during the year in order to evaluate the energy which reaches the soil and to deduce the potential evaporation. Water contents at several depths have been recorded. Based on the profile description, five soil layers have been distinguished in the podzol. Two models have been used for simulating water fluxes: (i) a mechanistic model, the HYDRUS-1D model, which solves the Richards' equation, and (ii) a compartmental model, which treats the soil profile as a bucket into which water flows until its maximum capacity is reached. A global sensitivity analysis (Morris' one-at-a-time sensitivity analysis) was run previously to the calibration, in order to check the sensitivity in the chosen parameter search space. For
Day-ahead distributed energy resource scheduling using differential search algorithm
DEFF Research Database (Denmark)
Soares, J.; Lobo, C.; Silva, M.
2015-01-01
The number of dispersed energy resources is growing every day, such as the use of more distributed generators. This paper deals with energy resource scheduling model in future smart grids. The methodology can be used by virtual power players (VPPs) considering day-ahead time horizon. This method...... considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. This paper presents an application of differential search algorithm (DSA) for solving the day-ahead scheduling...
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.
A Nonmonotone Line Search Filter Algorithm for the System of Nonlinear Equations
Directory of Open Access Journals (Sweden)
Zhong Jin
2012-01-01
Full Text Available We present a new iterative method based on the line search filter method with the nonmonotone strategy to solve the system of nonlinear equations. The equations are divided into two groups; some equations are treated as constraints and the others act as the objective function, and the two groups are just updated at the iterations where it is needed indeed. We employ the nonmonotone idea to the sufficient reduction conditions and filter technique which leads to a flexibility and acceptance behavior comparable to monotone methods. The new algorithm is shown to be globally convergent and numerical experiments demonstrate its effectiveness.
Directory of Open Access Journals (Sweden)
Yukai Yao
2015-01-01
Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.
Clarkin, T. J.; Kasprzyk, J. R.; Raseman, W. J.; Herman, J. D.
2015-12-01
This study contributes a diagnostic assessment of multiobjective evolutionary algorithm (MOEA) search on a set of water resources problem formulations with different configurations of constraints. Unlike constraints in classical optimization modeling, constraints within MOEA simulation-optimization represent limits on acceptable performance that delineate whether solutions within the search problem are feasible. Constraints are relevant because of the emergent pressures on water resources systems: increasing public awareness of their sustainability, coupled with regulatory pressures on water management agencies. In this study, we test several state-of-the-art MOEAs that utilize restricted tournament selection for constraint handling on varying configurations of water resources planning problems. For example, a problem that has no constraints on performance levels will be compared with a problem with several severe constraints, and a problem with constraints that have less severe values on the constraint thresholds. One such problem, Lower Rio Grande Valley (LRGV) portfolio planning, has been solved with a suite of constraints that ensure high reliability, low cost variability, and acceptable performance in a single year severe drought. But to date, it is unclear whether or not the constraints are negatively affecting MOEAs' ability to solve the problem effectively. Two categories of results are explored. The first category uses control maps of algorithm performance to determine if the algorithm's performance is sensitive to user-defined parameters. The second category uses run-time performance metrics to determine the time required for the algorithm to reach sufficient levels of convergence and diversity on the solution sets. Our work exploring the effect of constraints will better enable practitioners to define MOEA problem formulations for real-world systems, especially when stakeholders are concerned with achieving fixed levels of performance according to one or
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.
An Intuitive Dominant Test Algorithm of CP-nets Applied on Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Liu Zhaowei
2014-07-01
Full Text Available A wireless sensor network is of spatially distributed with autonomous sensors, just like a multi-Agent system with single Agent. Conditional Preference networks is a qualitative tool for representing ceteris paribus (all other things being equal preference statements, it has been a research hotspot in artificial intelligence recently. But the algorithm and complexity of strong dominant test with respect to binary-valued structure CP-nets have not been solved, and few researchers address the application to other domain. In this paper, strong dominant test and application of CP-nets are studied in detail. Firstly, by constructing induced graph of CP-nets and studying its properties, we make a conclusion that the problem of strong dominant test on binary-valued CP-nets is single source shortest path problem essentially, so strong dominant test problem can be solved by improved Dijkstra’s algorithm. Secondly, we apply the algorithm above mentioned to the completeness of wireless sensor network, and design a completeness judging algorithm based on strong dominant test. Thirdly, we apply the algorithm on wireless sensor network to solve routing problem. In the end, we point out some interesting work in the future.
Elsheikh, Ahmed H.
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems. © 2013 Elsevier Inc.
Shareef, Hussain; Mohamed, Azah
2017-01-01
The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method. PMID:29220396
International Nuclear Information System (INIS)
Milligan, M. R.; Factor, T.
2001-01-01
This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the method using two objective functions for the optimization: maximum economic benefit and maximum reliability. We also illustrate the sensitivity of the fuzzy economic benefit solutions to small perturbations of the capacity selections at each wind site. We find that small changes in site capacity and/or location have small effects on the economic benefit provided by wind power plants. We use electric load and generator data from Iowa, along with high-quality wind-speed data collected by the Iowa Wind Energy Institute
Directory of Open Access Journals (Sweden)
Md Mainul Islam
Full Text Available The electric vehicle (EV is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS planning. A novel optimization technique, called binary lightning search algorithm (BLSA, is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.
Energy Technology Data Exchange (ETDEWEB)
Milligan, M. R., National Renewable Energy Laboratory; Factor, T., Iowa Wind Energy Institute
2001-09-21
This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the method using two objective functions for the optimization: maximum economic benefit and maximum reliability. We also illustrate the sensitivity of the fuzzy economic benefit solutions to small perturbations of the capacity selections at each wind site. We find that small changes in site capacity and/or location have small effects on the economic benefit provided by wind power plants. We use electric load and generator data from Iowa, along with high-quality wind-speed data collected by the Iowa Wind Energy Institute.
Optimal path planning for a mobile robot using cuckoo search algorithm
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.
Directory of Open Access Journals (Sweden)
Chengfen Zhang
2015-01-01
Full Text Available Dry-type air-core reactor is now widely applied in electrical power distribution systems, for which the optimization design is a crucial issue. In the optimization design problem of dry-type air-core reactor, the objectives of minimizing the production cost and minimizing the operation cost are both important. In this paper, a multiobjective optimal model is established considering simultaneously the two objectives of minimizing the production cost and minimizing the operation cost. To solve the multi-objective optimization problem, a memetic evolutionary algorithm is proposed, which combines elitist nondominated sorting genetic algorithm version II (NSGA-II with a local search strategy based on the covariance matrix adaptation evolution strategy (CMA-ES. NSGA-II can provide decision maker with flexible choices among the different trade-off solutions, while the local-search strategy, which is applied to nondominated individuals randomly selected from the current population in a given generation and quantity, can accelerate the convergence speed. Furthermore, another modification is that an external archive is set in the proposed algorithm for increasing the evolutionary efficiency. The proposed algorithm is tested on a dry-type air-core reactor made of rectangular cross-section litz-wire. Simulation results show that the proposed algorithm has high efficiency and it converges to a better Pareto front.
Obstacle Avoidance for Redundant Manipulators Utilizing a Backward Quadratic Search Algorithm
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Tianjian Hu
2016-06-01
Full Text Available Obstacle avoidance can be achieved as a secondary task by appropriate inverse kinematics (IK resolution of redundant manipulators. Most prior literature requires the time-consuming determination of the closest point to the obstacle for every calculation step. Aiming at the relief of computational burden, this paper develops what is termed a backward quadratic search algorithm (BQSA as another option for solving IK problems in obstacle avoidance. The BQSA detects possible collisions based on the root property of a category of quadratic functions, which are derived from ellipse-enveloped obstacles and the positions of each link's end-points. The algorithm executes a backward search for possible obstacle collisions, from the end-effector to the base, and avoids obstacles by utilizing a hybrid IK scheme, incorporating the damped least-squares method, the weighted least-norm method and the gradient projection method. Some details of the hybrid IK scheme, such as values of the damped factor, weights and the clamping velocity, are discussed, along with a comparison of computational load between previous methods and BQSA. Simulations of a planar seven-link manipulator and a PUMA 560 robot verify the effectiveness of BQSA.
A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles
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Ricardo Soto
2015-01-01
Full Text Available The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n2 × n2 grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n2. Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods.
Salehi, Mojtaba; Bahreininejad, Ardeshir
2011-08-01
Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.
Turn-Based War Chess Model and Its Search Algorithm per Turn
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Hai Nan
2016-01-01
Full Text Available War chess gaming has so far received insufficient attention but is a significant component of turn-based strategy games (TBS and is studied in this paper. First, a common game model is proposed through various existing war chess types. Based on the model, we propose a theory frame involving combinational optimization on the one hand and game tree search on the other hand. We also discuss a key problem, namely, that the number of the branching factors of each turn in the game tree is huge. Then, we propose two algorithms for searching in one turn to solve the problem: (1 enumeration by order; (2 enumeration by recursion. The main difference between these two is the permutation method used: the former uses the dictionary sequence method, while the latter uses the recursive permutation method. Finally, we prove that both of these algorithms are optimal, and we analyze the difference between their efficiencies. An important factor is the total time taken for the unit to expand until it achieves its reachable position. The factor, which is the total number of expansions that each unit makes in its reachable position, is set. The conclusion proposed is in terms of this factor: Enumeration by recursion is better than enumeration by order in all situations.
International Nuclear Information System (INIS)
Hukushima, K; Iba, Y
2008-01-01
We develop a recently proposed importance-sampling Monte Carlo algorithm for sampling rare events and quenched variables in random disordered systems. We apply it to a two dimensional bond-diluted Ising model and study the Griffiths singularity which is considered to be due to the existence of rare large clusters. It is found that the distribution of the inverse susceptibility has an exponential tail down to the origin which is considered the consequence of the Griffiths singularity
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
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 depth-first search algorithm to compute elementary flux modes by linear programming.
Quek, Lake-Ee; Nielsen, Lars K
2014-07-30
The decomposition of complex metabolic networks into elementary flux modes (EFMs) provides a useful framework for exploring reaction interactions systematically. Generating a complete set of EFMs for large-scale models, however, is near impossible. Even for moderately-sized models (linear programming (LP) to enumerate EFMs in an exhaustive fashion. Constraints can be introduced to directly generate a subset of EFMs satisfying the set of constraints. The depth-first search algorithm has a constant memory overhead. Using flux constraints, a large LP problem can be massively divided and parallelized into independent sub-jobs for deployment into computing clusters. Since the sub-jobs do not overlap, the approach scales to utilize all available computing nodes with minimal coordination overhead or memory limitations. The speed of the algorithm was comparable to efmtool, a mainstream Double Description method, when enumerating all EFMs; the attrition power gained from performing flux feasibility tests offsets the increased computational demand of running an LP solver. Unlike the Double Description method, the algorithm enables accelerated enumeration of all EFMs satisfying a set of constraints.
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585
Multiobjective pressurized water reactor reload core design by nondominated genetic algorithm search
International Nuclear Information System (INIS)
Parks, G.T.
1996-01-01
The design of pressurized water reactor reload cores is not only a formidable optimization problem but also, in many instances, a multiobjective problem. A genetic algorithm (GA) designed to perform true multiobjective optimization on such problems is described. Genetic algorithms simulate natural evolution. They differ from most optimization techniques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sense) solutions as parents more often, the population can be evolved to reveal the trade-off surface between the competing objectives. An example illustrating the effectiveness of this novel method is presented and analyzed. It is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends on the core simulator used; the GA itself is code independent
Floares, Alexandru George
2008-01-01
Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.
The Surface Extraction from TIN based Search-space Minimization (SETSM) algorithm
Noh, Myoung-Jong; Howat, Ian M.
2017-07-01
Digital Elevation Models (DEMs) provide critical information for a wide range of scientific, navigational and engineering activities. Submeter resolution, stereoscopic satellite imagery with high geometric and radiometric quality, and wide spatial coverage are becoming increasingly accessible for generating stereo-photogrammetric DEMs. However, low contrast and repeatedly-textured surfaces, such as snow and glacial ice at high latitudes, and mountainous terrains challenge existing stereo-photogrammetric DEM generation techniques, particularly without a-priori information such as existing seed DEMs or the manual setting of terrain-specific parameters. To utilize these data for fully-automatic DEM extraction at a large scale, we developed the Surface Extraction from TIN-based Search-space Minimization (SETSM) algorithm. SETSM is fully automatic (i.e. no search parameter settings are needed) and uses only the sensor model Rational Polynomial Coefficients (RPCs). SETSM adopts a hierarchical, combined image- and object-space matching strategy utilizing weighted normalized cross-correlation with both original distorted and geometrically corrected images for overcoming ambiguities caused by foreshortening and occlusions. In addition, SETSM optimally minimizes search-spaces to extract optimal matches over problematic terrains by iteratively updating object surfaces within a Triangulated Irregular Network, and utilizes a geometric-constrained blunder and outlier detection in object space. We prove the ability of SETSM to mitigate typical stereo-photogrammetric matching problems over a range of challenging terrains. SETSM is the primary DEM generation software for the US National Science Foundation's ArcticDEM project.
Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp.
Nguyen, Phi-Vu; Ghezal, Ali; Hsueh, Ya-Chih; Boudier, Thomas; Gan, Samuel Ken-En; Lee, Hwee Kuan
2016-08-01
In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the segmentation algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9 ± 2.0% for protein polyacrylamide gels, and 35.9 ± 2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimize general image segmentation. The improved version of GelApp-GelApp 2.0-is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform). © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes: Level II Milestone
Energy Technology Data Exchange (ETDEWEB)
Alfonsi, Andrea [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Cogliati, Joshua Joseph [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Sen, Ramazan Sonat [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Smith, Curtis Lee [Idaho National Laboratory (INL), Idaho Falls, ID (United States)
2015-07-01
The RAVEN code is becoming a comprehensive tool to perform Probabilistic Risk Assessment (PRA); Uncertainty Quantification (UQ) and Propagation; and Verification and Validation (V&V). The RAVEN code is being developed to support the Risk-Informed Safety Margin Characterization (RISMC) pathway by developing an advanced set of methodologies and algorithms for use in advanced risk analysis. The RISMC approach uses system simulator codes applied to stochastic analysis tools. The fundamental idea behind this coupling approach to perturb (by employing sampling strategies) timing and sequencing of events, internal parameters of the system codes (i.e., uncertain parameters of the physics model) and initial conditions to estimate values ranges and associated probabilities of figures of merit of interest for engineering and safety (e.g. core damage probability, etc.). This approach applied to complex systems such as nuclear power plants requires performing a series of computationally expensive simulation runs. The large computational burden is caused by the large set of (uncertain) parameters characterizing those systems. Consequently, exploring the uncertain/parametric domain, with a good level of confidence, is generally not affordable, considering the limited computational resources that are currently available. In addition, the recent tendency to develop newer tools, characterized by higher accuracy and larger computational resources (if compared with the presently used legacy codes, that have been developed decades ago), has made this issue even more compelling. In order to overcome to these limitations, the strategy for the exploration of the uncertain/parametric space needs to use at best the computational resources focusing the computational effort in those regions of the uncertain/parametric space that are “interesting” (e.g., risk-significant regions of the input space) with respect the targeted Figures Of Merit (FOM): for example, the failure of the system
Srinivasa, K. G.; Shree Devi, B. N.
2017-10-01
String searching in documents has become a tedious task with the evolution of Big Data. Generation of large data sets demand for a high performance search algorithm in areas such as text mining, information retrieval and many others. The popularity of GPU's for general purpose computing has been increasing for various applications. Therefore it is of great interest to exploit the thread feature of a GPU to provide a high performance search algorithm. This paper proposes an optimized new approach to N-gram model for string search in a number of lengthy documents and its GPU implementation. The algorithm exploits GPGPUs for searching strings in many documents employing character level N-gram matching with parallel Score Table approach and search using CUDA API. The new approach of Score table used for frequency storage of N-grams in a document, makes the search independent of the document's length and allows faster access to the frequency values, thus decreasing the search complexity. The extensive thread feature in a GPU has been exploited to enable parallel pre-processing of trigrams in a document for Score Table creation and parallel search in huge number of documents, thus speeding up the whole search process even for a large pattern size. Experiments were carried out for many documents of varied length and search strings from the standard Lorem Ipsum text on NVIDIA's GeForce GT 540M GPU with 96 cores. Results prove that the parallel approach for Score Table creation and searching gives a good speed up than the same approach executed serially.
Energy Technology Data Exchange (ETDEWEB)
Doert, Marlene [Technische Universitaet Dortmund (Germany); Ruhr-Universitaet Bochum (Germany); Einecke, Sabrina [Technische Universitaet Dortmund (Germany); Errando, Manel [Barnard College, Columbia University, New York City (United States)
2015-07-01
The second Fermi-LAT source catalog (2FGL) is the deepest all-sky survey of the gamma-ray sky currently available to the community. Out of the 1873 catalog sources, 576 remain unassociated. We present a search for active galactic nuclei (AGN) among these unassociated objects, which aims at a reduction of the number of unassociated gamma-ray sources and a more complete characterization of the population of gamma-ray emitting AGN. Our study uses two complimentary machine learning algorithms which are individually trained on the gamma-ray properties of associated 2FGL sources and thereafter applied to the unassociated sample. The intersection of the two methods yields a high-confidence sample of 231 AGN candidate sources. We estimate the performance of the classification by taking inherent differences between the samples of associated and unassociated 2FGL sources into account. A search for infra-red counterparts and first results from follow-up studies in the X-ray band using Swift satellite data for a subset of our AGN candidates are also presented.
International Nuclear Information System (INIS)
Mahdad, Belkacem; Srairi, K.
2015-01-01
Highlights: • A generalized optimal security power system planning strategy for blackout risk prevention is proposed. • A Grey Wolf Optimizer dynamically coordinated with Pattern Search algorithm is proposed. • A useful optimized database dynamically generated considering margin loading stability under severe faults. • The robustness and feasibility of the proposed strategy is validated in the standard IEEE 30 Bus system. • The proposed planning strategy will be useful for power system protection coordination and control. - Abstract: Developing a flexible and reliable power system planning strategy under critical situations is of great importance to experts and industrials to minimize the probability of blackouts occurrence. This paper introduces the first stage of this practical strategy by the application of Grey Wolf Optimizer coordinated with pattern search algorithm for solving the security smart grid power system management under critical situations. The main objective of this proposed planning strategy is to prevent the practical power system against blackout due to the apparition of faults in generating units or important transmission lines. At the first stage the system is pushed to its margin stability limit, the critical loads shedding are selected using voltage stability index. In the second stage the generator control variables, the reactive power of shunt and dynamic compensators are adjusted in coordination with minimization the active and reactive power at critical loads to maintain the system at security state to ensure service continuity. The feasibility and efficiency of the proposed strategy is applied to IEEE 30-Bus test system. Results are promising and prove the practical efficiency of the proposed strategy to ensure system security under critical situations
International Nuclear Information System (INIS)
Pucello, N.; D'Agostino, G.; Pisacane, F.
1997-01-01
A genetic algorithm for the optimization of the ground-state structure of a metallic cluster has been developed and ported on a SIMD-MIMD parallel platform. The SIMD part of the parallel platform is represented by a Quadrics/APE100 consisting of 512 floating point units, while the MIMD part is formed by a cluster of workstations. The proposed algorithm is composed by a part where the genetic operators are applied to the elements of the population and a part which performs a further local relaxation and the fitness calculation via Molecular Dynamics. These parts have been implemented on the MIMD and on the SIMD part, respectively. Results have been compared to those generated by using Simulated Annealing
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)
An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS
Directory of Open Access Journals (Sweden)
Yang Jiao
2017-01-01
Full Text Available To solve problems such as the high cost of microgrids (MGs, balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS and harmony search algorithm (HSA are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA can effectively improve the stability and economy of MG operation with an ESS.
Halligan, Brian D; Geiger, Joey F; Vallejos, Andrew K; Greene, Andrew S; Twigger, Simon N
2009-06-01
One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).
Rahman, Imran; Vasant, Pandian M.; Singh, Balbir Singh Mahinder; Abdullah-Al-Wadud, M.
2014-10-01
Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.
International Nuclear Information System (INIS)
Ketabi, Abbas; Alibabaee, Ahmad; Feuillet, R.
2010-01-01
Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price. (author)
An extension of the directed search domain algorithm to bilevel optimization
Wang, Kaiqiang; Utyuzhnikov, Sergey V.
2017-08-01
A method is developed for generating a well-distributed Pareto set for the upper level in bilevel multiobjective optimization. The approach is based on the Directed Search Domain (DSD) algorithm, which is a classical approach for generation of a quasi-evenly distributed Pareto set in multiobjective optimization. The approach contains a double-layer optimizer designed in a specific way under the framework of the DSD method. The double-layer optimizer is based on bilevel single-objective optimization and aims to find a unique optimal Pareto solution rather than generate the whole Pareto frontier on the lower level in order to improve the optimization efficiency. The proposed bilevel DSD approach is verified on several test cases, and a relevant comparison against another classical approach is made. It is shown that the approach can generate a quasi-evenly distributed Pareto set for the upper level with relatively low time consumption.
Directory of Open Access Journals (Sweden)
Saifullah Khalid
2016-09-01
Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.
Performance Evaluation of Various STL File Mesh Refining Algorithms Applied for FDM-RP Process
Ledalla, Siva Rama Krishna; Tirupathi, Balaji; Sriram, Venkatesh
2018-06-01
Layered manufacturing machines use the stereolithography (STL) file to build parts. When a curved surface is converted from a computer aided design (CAD) file to STL, it results in a geometrical distortion and chordal error. Parts manufactured with this file, might not satisfy geometric dimensioning and tolerance requirements due to approximated geometry. Current algorithms built in CAD packages have export options to globally reduce this distortion, which leads to an increase in the file size and pre-processing time. In this work, different mesh subdivision algorithms are applied on STL file of a complex geometric features using MeshLab software. The mesh subdivision algorithms considered in this work are modified butterfly subdivision technique, loops sub division technique and general triangular midpoint sub division technique. A comparative study is made with respect to volume and the build time using the above techniques. It is found that triangular midpoint sub division algorithm is more suitable for the geometry under consideration. Only the wheel cap part is then manufactured on Stratasys MOJO FDM machine. The surface roughness of the part is measured on Talysurf surface roughness tester.
Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data
Martins, Fabio J. W. A.; Foucaut, Jean-Marc; Thomas, Lionel; Azevedo, Luis F. A.; Stanislas, Michel
2015-08-01
Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time.
Energy Technology Data Exchange (ETDEWEB)
Perrot, P
1993-12-01
In a complete system of acquisition and processing of ultrasonic signals, it is often necessary at one stage to use some processing tools to get rid of the influence of the different elements of that system. By that means, the final quality of the signals in terms of resolution is improved. There are two main characteristics of ultrasonic signals which make this task difficult. Firstly, the signals generated by transducers are very often non-minimum phase. The classical deconvolution algorithms are unable to deal with such characteristics. Secondly, depending on the medium, the shape of the propagating pulse is evolving. The spatial invariance assumption often used in classical deconvolution algorithms is rarely valid. Many classical algorithms, parametric and non-parametric, have been investigated: the Wiener-type, the adaptive predictive techniques, the Oldenburg technique in the frequency domain, the minimum variance deconvolution. All the algorithms have been firstly tested on simulated data. One specific experimental set-up has also been analysed. Simulated and real data has been produced. This set-up demonstrated the interest in applying deconvolution, in terms of the achieved resolution. (author). 32 figs., 29 refs.
Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data
International Nuclear Information System (INIS)
Martins, Fabio J W A; Foucaut, Jean-Marc; Stanislas, Michel; Thomas, Lionel; Azevedo, Luis F A
2015-01-01
Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time. (paper)
Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm
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Norlina Mohd Sabri
2016-06-01
Full Text Available This research is focusing on the radio frequency (RF magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO, Genetic Algorithm (GA, Artificial Immune System (AIS and Ant Colony Optimization (ACO. Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.
Kombinasi Firefly Algorithm-Tabu Search untuk Penyelesaian Traveling Salesman Problem
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Riyan Naufal Hay's
2017-07-01
Full Text Available Traveling Salesman Problem (TSP adalah masalah optimasi kombinatorial klasik dan memiliki peran dalam perencanaan, penjadwalan, dan pencarian pada bidang rekayasa dan pengetahuan (Dong, 2012. TSP juga merupakan objek yang baik untuk menguji kinerja metode optimasi, beberapa metode seperti Cooperative Genetic Ant System (CGAS (Dong, 2012, Parallelized Genetic Ant Colony System (PGAS Particle Swarm Optimization and Ant Colony Optimization Algorithms (PSO–ACO (Elloumi, 2014, dan Ant Colony Hyper-Heuristics (ACO HH (Aziz, 2015 telah dikembangkan untuk memecahkan TSP. Sehingga, pada penelitian ini diimplementasikan kombinasi metode baru untuk meningkatkan akurasi penyelesaian TSP. Firefly Algorithm (FA merupakan salah satu algoritma yang dapat digunakan untuk memecahkan masalah optimasi kombinatorial (Layeb, 2014. FA merupakan algoritma yang berpotensi kuat dalam memecahkan kasus optimasi dibanding algoritma yang ada termasuk Particle Swarm Optimization (Yang, 2010. Namun, FA memiliki kekurangan dalam memecahkan masalah optimasi dengan skala besar (Baykasoğlu dan Ozsoy, 2014. Tabu Search (TS merupakan metode optimasi yang terbukti efektif untuk memecahkan masalah optimasi dengan skala besar (Pedro, 2013. Pada penelitian ini, TS akan diterapkan pada FA (FATS untuk memecahkan kasus TSP. Hasil FATS akan dibandingkan terhadap penelitian sebelumnya yaitu ACOHH. Perbandingan hasil menunjukan peningkatan akurasi sebesar 0.89% pada dataset Oliver30, 0.14% dataset Eil51, 3.81% dataset Eil76 dan 1.27% dataset KroA100.
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Yuliang Su
2015-04-01
Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.
A Framing Link Based Tabu Search Algorithm for Large-Scale Multidepot Vehicle Routing Problems
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Xuhao Zhang
2014-01-01
Full Text Available A framing link (FL based tabu search algorithm is proposed in this paper for a large-scale multidepot vehicle routing problem (LSMDVRP. Framing links are generated during continuous great optimization of current solutions and then taken as skeletons so as to improve optimal seeking ability, speed up the process of optimization, and obtain better results. Based on the comparison between pre- and postmutation routes in the current solution, different parts are extracted. In the current optimization period, links involved in the optimal solution are regarded as candidates to the FL base. Multiple optimization periods exist in the whole algorithm, and there are several potential FLs in each period. If the update condition is satisfied, the FL base is updated, new FLs are added into the current route, and the next period starts. Through adjusting the borderline of multidepot sharing area with dynamic parameters, the authors define candidate selection principles for three kinds of customer connections, respectively. Link split and the roulette approach are employed to choose FLs. 18 LSMDVRP instances in three groups are studied and new optimal solution values for nine of them are obtained, with higher computation speed and reliability.
A trust-based sensor allocation algorithm in cooperative space search problems
Shen, Dan; Chen, Genshe; Pham, Khanh; Blasch, Erik
2011-06-01
Sensor allocation is an important and challenging problem within the field of multi-agent systems. The sensor allocation problem involves deciding how to assign a number of targets or cells to a set of agents according to some allocation protocol. Generally, in order to make efficient allocations, we need to design mechanisms that consider both the task performers' costs for the service and the associated probability of success (POS). In our problem, the costs are the used sensor resource, and the POS is the target tracking performance. Usually, POS may be perceived differently by different agents because they typically have different standards or means of evaluating the performance of their counterparts (other sensors in the search and tracking problem). Given this, we turn to the notion of trust to capture such subjective perceptions. In our approach, we develop a trust model to construct a novel mechanism that motivates sensor agents to limit their greediness or selfishness. Then we model the sensor allocation optimization problem with trust-in-loop negotiation game and solve it using a sub-game perfect equilibrium. Numerical simulations are performed to demonstrate the trust-based sensor allocation algorithm in cooperative space situation awareness (SSA) search problems.
An Effective Framework For Economic Dispatch Using Modified Harmony Search Algorithm
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Advik Kumar
2017-09-01
Full Text Available The effects of ever-increasing wind power generation for solving the economic dispatch ED problem have led to high penetration of renewable energy source in new power systems. Continuing search for better utilizing of wind turbine associated with thermal sources to find the optimal allocation of output power is necessary in which pro-vide more reliability and efficiency. Dynamic nature of wind energy has imposed uncertainties characteristics in the poser systems. To deal with this problem an effective probabilistic method to investigate all unpredictability would be a good idea to make more realistic analysis. This paper presents a heuristics optimization method based on harmony search HS algorithm to solve non-convex ED problems while uncertainties effects caused by wind turbines are considered. To involve a realistic analysis as a more practical investigation the proposed probabilistic ED PED approach includes prohibited operating zone POZ system spinning reserve ramp rate limits variety of fuel is considered in this studies. Point Estimate Method PEM as a proposed PED model the uncertainties of wind speed for wind turbines to present better realization to the problem. Optimal solution are presented for vari-ous test system and these solutions demonstrate the benefits of our approach in terms of cost over existing ED techniques.
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Erik Cuevas
2015-01-01
Full Text Available In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC algorithm and the evolutionary method harmony search (HS. With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.
A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers
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Concepción Crespo Turrado
2015-12-01
Full Text Available Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS and compares it with the well-known technique called multivariate imputation by chained equations (MICE. The results obtained demonstrate how the proposed method outperforms the MICE algorithm.
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Zhong Liu
2018-05-01
Full Text Available In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM, the uncertain map (UM, and the digital pheromone map (DPM are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius
EVALUATION OF WEB SEARCHING METHOD USING A NOVEL WPRR ALGORITHM FOR TWO DIFFERENT CASE STUDIES
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V. Lakshmi Praba
2012-04-01
Full Text Available The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to web data and documents. Web content mining and web structure mining have important roles in identifying the relevant web page. Relevancy of web page denotes how well a retrieved web page or set of web pages meets the information need of the user. Page Rank, Weighted Page Rank and Hypertext Induced Topic Selection (HITS are existing algorithms which considers only web structure mining. Vector Space Model (VSM, Cover Density Ranking (CDR, Okapi similarity measurement (Okapi and Three-Level Scoring method (TLS are some of existing relevancy score methods which consider only web content mining. In this paper, we propose a new algorithm, Weighted Page with Relevant Rank (WPRR which is blend of both web content mining and web structure mining that demonstrates the relevancy of the page with respect to given query for two different case scenarios. It is shown that WPRR’s performance is better than the existing algorithms.
Amra, Sakusic; O'Horo, John C; Singh, Tarun D; Wilson, Gregory A; Kashyap, Rahul; Petersen, Ronald; Roberts, Rosebud O; Fryer, John D; Rabinstein, Alejandro A; Gajic, Ognjen
2017-02-01
Long-term cognitive impairment is a common and important problem in survivors of critical illness. We developed electronic search algorithms to identify cognitive impairment and dementia from the electronic medical records (EMRs) that provide opportunity for big data analysis. Eligible patients met 2 criteria. First, they had a formal cognitive evaluation by The Mayo Clinic Study of Aging. Second, they were hospitalized in intensive care unit at our institution between 2006 and 2014. The "criterion standard" for diagnosis was formal cognitive evaluation supplemented by input from an expert neurologist. Using all available EMR data, we developed and improved our algorithms in the derivation cohort and validated them in the independent validation cohort. Of 993 participants who underwent formal cognitive testing and were hospitalized in intensive care unit, we selected 151 participants at random to form the derivation and validation cohorts. The automated electronic search algorithm for cognitive impairment was 94.3% sensitive and 93.0% specific. The search algorithms for dementia achieved respective sensitivity and specificity of 97% and 99%. EMR search algorithms significantly outperformed International Classification of Diseases codes. Automated EMR data extractions for cognitive impairment and dementia are reliable and accurate and can serve as acceptable and efficient alternatives to time-consuming manual data review. Copyright © 2016 Elsevier Inc. All rights reserved.
Cheng, Jun; Zhang, Jun; Tian, Jinwen
2015-12-01
Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.
Energy Technology Data Exchange (ETDEWEB)
Bernal, Jose; Escobar, John Willmer; Linfati, Rodrigo
2017-07-01
We consider a real case study of a vehicle routing problem with a heterogeneous fleet and time windows (HFVRPTW) for a franchise company bottling Coca-Cola products in Colombia. This study aims to determine the routes to be performed to fulfill the demand of the customers by using a heterogeneous fleet and considering soft time windows. The objective is to minimize the distance traveled by the performed routes. Design/methodology/approach: We propose a two-phase heuristic algorithm. In the proposed approach, after an initial phase (first phase), a granular tabu search is applied during the improvement phase (second phase). Two additional procedures are considered to help that the algorithm could escape from local optimum, given that during a given number of iterations there has been no improvement. Findings: Computational experiments on real instances show that the proposed algorithm is able to obtain high-quality solutions within a short computing time compared to the results found by the software that the company currently uses to plan the daily routes. Originality/value: We propose a novel metaheuristic algorithm for solving a real routing problem by considering heterogeneous fleet and time windows. The efficiency of the proposed approach has been tested on real instances, and the computational experiments shown its applicability and performance for solving NP-Hard Problems related with routing problems with similar characteristics. The proposed algorithm was able to improve some of the current solutions applied by the company by reducing the route length and the number of vehicles.
International Nuclear Information System (INIS)
Bernal, Jose; Escobar, John Willmer; Linfati, Rodrigo
2017-01-01
We consider a real case study of a vehicle routing problem with a heterogeneous fleet and time windows (HFVRPTW) for a franchise company bottling Coca-Cola products in Colombia. This study aims to determine the routes to be performed to fulfill the demand of the customers by using a heterogeneous fleet and considering soft time windows. The objective is to minimize the distance traveled by the performed routes. Design/methodology/approach: We propose a two-phase heuristic algorithm. In the proposed approach, after an initial phase (first phase), a granular tabu search is applied during the improvement phase (second phase). Two additional procedures are considered to help that the algorithm could escape from local optimum, given that during a given number of iterations there has been no improvement. Findings: Computational experiments on real instances show that the proposed algorithm is able to obtain high-quality solutions within a short computing time compared to the results found by the software that the company currently uses to plan the daily routes. Originality/value: We propose a novel metaheuristic algorithm for solving a real routing problem by considering heterogeneous fleet and time windows. The efficiency of the proposed approach has been tested on real instances, and the computational experiments shown its applicability and performance for solving NP-Hard Problems related with routing problems with similar characteristics. The proposed algorithm was able to improve some of the current solutions applied by the company by reducing the route length and the number of vehicles.
Breadth-First Search-Based Single-Phase Algorithms for Bridge Detection in Wireless Sensor Networks
Akram, Vahid Khalilpour; Dagdeviren, Orhan
2013-01-01
Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milic's algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times. PMID:23845930
A necessary condition for applying MUSIC algorithm in limited-view inverse scattering problem
International Nuclear Information System (INIS)
Park, Taehoon; Park, Won-Kwang
2015-01-01
Throughout various results of numerical simulations, it is well-known that MUltiple SIgnal Classification (MUSIC) algorithm can be applied in the limited-view inverse scattering problems. However, the application is somehow heuristic. In this contribution, we identify a necessary condition of MUSIC for imaging of collection of small, perfectly conducting cracks. This is based on the fact that MUSIC imaging functional can be represented as an infinite series of Bessel function of integer order of the first kind. Numerical experiments from noisy synthetic data supports our investigation. (paper)
APPLYING ARTIFICIAL NEURAL NETWORK OPTIMIZED BY FIREWORKS ALGORITHM FOR STOCK PRICE ESTIMATION
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Khuat Thanh Tung
2016-04-01
Full Text Available Stock prediction is to determine the future value of a company stock dealt on an exchange. It plays a crucial role to raise the profit gained by firms and investors. Over the past few years, many methods have been developed in which plenty of efforts focus on the machine learning framework achieving the promising results. In this paper, an approach based on Artificial Neural Network (ANN optimized by Fireworks algorithm and data preprocessing by Haar Wavelet is applied to estimate the stock prices. The system was trained and tested with real data of various companies collected from Yahoo Finance. The obtained results are encouraging.
A necessary condition for applying MUSIC algorithm in limited-view inverse scattering problem
Park, Taehoon; Park, Won-Kwang
2015-09-01
Throughout various results of numerical simulations, it is well-known that MUltiple SIgnal Classification (MUSIC) algorithm can be applied in the limited-view inverse scattering problems. However, the application is somehow heuristic. In this contribution, we identify a necessary condition of MUSIC for imaging of collection of small, perfectly conducting cracks. This is based on the fact that MUSIC imaging functional can be represented as an infinite series of Bessel function of integer order of the first kind. Numerical experiments from noisy synthetic data supports our investigation.
International Nuclear Information System (INIS)
Sanchez Lopez, Hector
2001-01-01
This work describes an alternative algorithm of Simulated Annealing applied to the design of the main magnet for a Magnetic Resonance Imaging machine. The algorithm uses a probabilistic radial base neuronal network to classify the possible solutions, before the objective function evaluation. This procedure allows reducing up to 50% the number of iterations required by simulated annealing to achieve the global maximum, when compared with the SA algorithm. The algorithm was applied to design a 0.1050 Tesla four coil resistive magnet, which produces a magnetic field 2.13 times more uniform than the solution given by SA. (author)
Specific algorithm method of scoring the Clock Drawing Test applied in cognitively normal elderly
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Liana Chaves Mendes-Santos
Full Text Available The Clock Drawing Test (CDT is an inexpensive, fast and easily administered measure of cognitive function, especially in the elderly. This instrument is a popular clinical tool widely used in screening for cognitive disorders and dementia. The CDT can be applied in different ways and scoring procedures also vary. OBJECTIVE: The aims of this study were to analyze the performance of elderly on the CDT and evaluate inter-rater reliability of the CDT scored by using a specific algorithm method adapted from Sunderland et al. (1989. METHODS: We analyzed the CDT of 100 cognitively normal elderly aged 60 years or older. The CDT ("free-drawn" and Mini-Mental State Examination (MMSE were administered to all participants. Six independent examiners scored the CDT of 30 participants to evaluate inter-rater reliability. RESULTS AND CONCLUSION: A score of 5 on the proposed algorithm ("Numbers in reverse order or concentrated", equivalent to 5 points on the original Sunderland scale, was the most frequent (53.5%. The CDT specific algorithm method used had high inter-rater reliability (p<0.01, and mean score ranged from 5.06 to 5.96. The high frequency of an overall score of 5 points may suggest the need to create more nuanced evaluation criteria, which are sensitive to differences in levels of impairment in visuoconstructive and executive abilities during aging.
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
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Tinggui Chen
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.
A methodology for the geometric design of heat recovery steam generators applying genetic algorithms
International Nuclear Information System (INIS)
Durán, M. Dolores; Valdés, Manuel; Rovira, Antonio; Rincón, E.
2013-01-01
This paper shows how the geometric design of heat recovery steam generators (HRSG) can be achieved. The method calculates the product of the overall heat transfer coefficient (U) by the area of the heat exchange surface (A) as a function of certain thermodynamic design parameters of the HRSG. A genetic algorithm is then applied to determine the best set of geometric parameters which comply with the desired UA product and, at the same time, result in a small heat exchange area and low pressure losses in the HRSG. In order to test this method, the design was applied to the HRSG of an existing plant and the results obtained were compared with the real exchange area of the steam generator. The findings show that the methodology is sound and offers reliable results even for complex HRSG designs. -- Highlights: ► The paper shows a methodology for the geometric design of heat recovery steam generators. ► Calculates product of the overall heat transfer coefficient by heat exchange area as a function of certain HRSG thermodynamic design parameters. ► It is a complement for the thermoeconomic optimization method. ► Genetic algorithms are used for solving the optimization problem
Active filtering applied to radiographic images unfolded by the Richardson-Lucy algorithm
International Nuclear Information System (INIS)
Almeida, Gevaldo L. de; Silvani, Maria Ines; Lopes, Ricardo T.
2011-01-01
Degradation of images caused by systematic uncertainties can be reduced when one knows the features of the spoiling agent. Typical uncertainties of this kind arise in radiographic images due to the non - zero resolution of the detector used to acquire them, and from the non-punctual character of the source employed in the acquisition, or from the beam divergence when extended sources are used. Both features blur the image, which, instead of a single point exhibits a spot with a vanishing edge, reproducing hence the point spread function - PSF of the system. Once this spoiling function is known, an inverse problem approach, involving inversion of matrices, can then be used to retrieve the original image. As these matrices are generally ill-conditioned, due to statistical fluctuation and truncation errors, iterative procedures should be applied, such as the Richardson-Lucy algorithm. This algorithm has been applied in this work to unfold radiographic images acquired by transmission of thermal neutrons and gamma-rays. After this procedure, the resulting images undergo an active filtering which fairly improves their final quality at a negligible cost in terms of processing time. The filter ruling the process is based on the matrix of the correction factors for the last iteration of the deconvolution procedure. Synthetic images degraded with a known PSF, and undergone to the same treatment, have been used as benchmark to evaluate the soundness of the developed active filtering procedure. The deconvolution and filtering algorithms have been incorporated to a Fortran program, written to deal with real images, generate the synthetic ones and display both. (author)
Abdullahi, Mohammed; Ngadi, Md Asri
2016-01-01
Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.
Directory of Open Access Journals (Sweden)
Mohammed Abdullahi
Full Text Available Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS has been shown to perform competitively with Particle Swarm Optimization (PSO. The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA based SOS (SASOS in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.
Wang, Xueyi
2012-02-08
The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.
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.
Directory of Open Access Journals (Sweden)
Yongquan Dong
2018-04-01
Full Text Available Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence. Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of electric loads, a seasonal mechanism is combined with the SVRCCS model, namely giving a seasonal SVR with chaotic cuckoo search (SSVRCCS model, to produce more accurate forecasting performances. The numerical results, tested by using the datasets from the National Electricity Market (NEM, Queensland, Australia and the New York Independent System Operator (NYISO, NY, USA, show that the proposed SSVRCCS model outperforms other alternative models.
International Nuclear Information System (INIS)
Li, H.Y.; Wu, C.W.; Liu, W.T.; Chen, P.X.; Li, C.Z.
2011-01-01
We propose a method for implementing the Grover search algorithm directly in a database containing any number of items based on multi-level systems. Compared with the searching procedure in the database with qubits encoding, our modified algorithm needs fewer iteration steps to find the marked item and uses the carriers of the information more economically. Furthermore, we illustrate how to realize our idea in cavity QED using Zeeman's level structure of atoms. And the numerical simulation under the influence of the cavity and atom decays shows that the scheme could be achieved efficiently within current state-of-the-art technology. -- Highlights: ► A modified Grover algorithm is proposed for searching in an arbitrary dimensional Hilbert space. ► Our modified algorithm requires fewer iteration steps to find the marked item. ► The proposed method uses the carriers of the information more economically. ► A scheme for a six-item Grover search in cavity QED is proposed. ► Numerical simulation under decays shows that the scheme can be achieved with enough fidelity.
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim
2014-01-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using
Directory of Open Access Journals (Sweden)
Zongyan Li
2016-01-01
Full Text Available This paper describes an improved global harmony search (IGHS algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes.
International Nuclear Information System (INIS)
Maduar, Marcelo F.; Pecequilo, Brigitte R.S.
2009-01-01
Radioactivity quantification of gamma-ray emitter radionuclides in samples measured by HPGe gamma spectrometers relies on the analysis of the photopeaks present in the spectra, especially on the accurate determination of their net areas. This paper presents a methodology and an algorithm description for the peak search and analysis in order to obtain the relevant peaks parameters and their uncertainties. The procedure is performed on a three step approach: a preliminary search is done by using the second-difference method; experimental peaks widths are assessed in order to obtain a width vs. channel relationship and to define regions with single or overlapping peaks; a non-linear fit is applied to each region of the spectrum with candidate peaks. The final target function is in the form G(x) = B(x) + F(x), where B(x) is the baseline composed by a sum of a weighed left-side B L (x) and right-side B R (x) base-line quadratic functions and the photopeaks term F(x) is a sum of Gaussian functions. The computational implementation is released entirely in open-source license. The code was developed in C++ language and the interface was developed with Qt GUI software toolkit. GNU scientific library, GSL, was employed to perform linear and non-linear fitting procedures as needed. Spectra previously generated at our laboratories were analyzed with the presented methodology and with the commercial software package WinnerGamma. Results obtained are consistent with those obtained with the aforementioned package, suggesting that it could be safely used in general-purpose gamma-ray spectrometry. (author)
Three different applications of genetic algorithm (GA) search techniques on oil demand estimation
International Nuclear Information System (INIS)
Canyurt, Olcay Ersel; Oztuerk, Harun Kemal
2006-01-01
This present study develops three scenarios to analyze oil consumption and make future projections based on the Genetic algorithm (GA) notion, and examines the effect of the design parameters on the oil utilization values. The models developed in the non-linear form are applied to the oil demand of Turkey. The GA Oil Demand Estimation Model (GAODEM) is developed to estimate the future oil demand values based on Gross National Product (GNP), population, import, export, oil production, oil import and car, truck and bus sales figures. Among these models, the GA-PGOiTI model, which uses population, GNP, oil import, truck sales and import as design parameters/indicators, was found to provide the best fit solution with the observed data. It may be concluded that the proposed models can be used as alternative solution and estimation techniques for the future oil utilization values of any country
The Scatter Search Based Algorithm to Revenue Management Problem in Broadcasting Companies
Pishdad, Arezoo; Sharifyazdi, Mehdi; Karimpour, Reza
2009-09-01
The problem under question in this paper which is faced by broadcasting companies is how to benefit from a limited advertising space. This problem is due to the stochastic behavior of customers (advertiser) in different fare classes. To address this issue we propose a mathematical constrained nonlinear multi period model which incorporates cancellation and overbooking. The objective function is to maximize the total expected revenue and our numerical method performs it by determining the sales limits for each class of customer to present the revenue management control policy. Scheduling the advertising spots in breaks is another area of concern and we consider it as a constraint in our model. In this paper an algorithm based on Scatter search is developed to acquire a good feasible solution. This method uses simulation over customer arrival and in a continuous finite time horizon [0, T]. Several sensitivity analyses are conducted in computational result for depicting the effectiveness of proposed method. It also provides insight into better results of considering revenue management (control policy) compared to "no sales limit" policy in which sooner demand will served first.
The sloan digital sky Survey-II supernova survey: search algorithm and follow-up observations
Energy Technology Data Exchange (ETDEWEB)
Sako, Masao [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States); Bassett, Bruce [Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701 (South Africa); Becker, Andrew; Hogan, Craig J. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); Cinabro, David [Department of Physics, Wayne State University, Detroit, MI 48202 (United States); DeJongh, Fritz; Frieman, Joshua A.; Marriner, John; Miknaitis, Gajus [Center for Particle Astrophysics, Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510 (United States); Depoy, D. L.; Prieto, Jose Luis [Department of Astronomy, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210-1173 (United States); Dilday, Ben; Kessler, Richard [Kavli Institute for Cosmological Physics, The University of Chicago, 5640 South Ellis Avenue Chicago, IL 60637 (United States); Doi, Mamoru [Institute of Astronomy, Graduate School of Science, University of Tokyo 2-21-1, Osawa, Mitaka, Tokyo 181-0015 (Japan); Garnavich, Peter M. [University of Notre Dame, 225 Nieuwland Science, Notre Dame, IN 46556-5670 (United States); Holtzman, Jon [Department of Astronomy, MSC 4500, New Mexico State University, P.O. Box 30001, Las Cruces, NM 88003 (United States); Jha, Saurabh [Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, P.O. Box 20450, MS29, Stanford, CA 94309 (United States); Konishi, Kohki [Institute for Cosmic Ray Research, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8582 (Japan); Lampeitl, Hubert [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Nichol, Robert C. [Institute of Cosmology and Gravitation, Mercantile House, Hampshire Terrace, University of Portsmouth, Portsmouth PO1 2EG (United Kingdom); and others
2008-01-01
The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 deg{sup 2} region along the celestial equator during its first two seasons of a three-season campaign. Multi-band (ugriz) light curves were measured for most of the sources, which include solar system objects, galactic variable stars, active galactic nuclei, supernovae (SNe), and other astronomical transients. The imaging survey is augmented by an extensive spectroscopic follow-up program to identify SNe, measure their redshifts, and study the physical conditions of the explosions and their environment through spectroscopic diagnostics. During the survey, light curves are rapidly evaluated to provide an initial photometric type of the SNe, and a selected sample of sources are targeted for spectroscopic observations. In the first two seasons, 476 sources were selected for spectroscopic observations, of which 403 were identified as SNe. For the type Ia SNe, the main driver for the survey, our photometric typing and targeting efficiency is 90%. Only 6% of the photometric SN Ia candidates were spectroscopically classified as non-SN Ia instead, and the remaining 4% resulted in low signal-to-noise, unclassified spectra. This paper describes the search algorithm and the software, and the real-time processing of the SDSS imaging data. We also present the details of the supernova candidate selection procedures and strategies for follow-up spectroscopic and imaging observations of the discovered sources.
Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree
Directory of Open Access Journals (Sweden)
M. Z. Gashti
2017-06-01
Full Text Available Spam emails is probable the main problem faced by most e-mail users. There are many features in spam email detection and some of these features have little effect on detection and cause skew detection and classification of spam email. Thus, Feature Selection (FS is one of the key topics in spam email detection systems. With choosing the important and effective features in classification, its performance can be optimized. Selector features has the task of finding a subset of features to improve the accuracy of its predictions. In this paper, a hybrid of Harmony Search Algorithm (HSA and decision tree is used for selecting the best features and classification. The obtained results on Spam-base dataset show that the rate of recognition accuracy in the proposed model is 95.25% which is high in comparison with models such as SVM, NB, J48 and MLP. Also, the accuracy of the proposed model on the datasets of Ling-spam and PU1 is high in comparison with models such as NB, SVM and LR.
Harada, Ryuhei; Takano, Yu; Shigeta, Yasuteru
2016-05-10
The TaBoo SeArch (TBSA) algorithm [ Harada et al. J. Comput. Chem. 2015 , 36 , 763 - 772 and Harada et al. Chem. Phys. Lett. 2015 , 630 , 68 - 75 ] was recently proposed as an enhanced conformational sampling method for reproducing biologically relevant rare events of a given protein. In TBSA, an inverse histogram of the original distribution, mapped onto a set of reaction coordinates, is constructed from trajectories obtained by multiple short-time molecular dynamics (MD) simulations. Rarely occurring states of a given protein are statistically selected as new initial states based on the inverse histogram, and resampling is performed by restarting the MD simulations from the new initial states to promote the conformational transition. In this process, the definition of the inverse histogram, which characterizes the rarely occurring states, is crucial for the efficiency of TBSA. In this study, we propose a simple modification of the inverse histogram to further accelerate the convergence of TBSA. As demonstrations of the modified TBSA, we applied it to (a) hydrogen bonding rearrangements of Met-enkephalin, (b) large-amplitude domain motions of Glutamine-Binding Protein, and (c) folding processes of the B domain of Staphylococcus aureus Protein A. All demonstrations numerically proved that the modified TBSA reproduced these biologically relevant rare events with nanosecond-order simulation times, although a set of microsecond-order, canonical MD simulations failed to reproduce the rare events, indicating the high efficiency of the modified TBSA.
Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.
Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin
2017-08-16
The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
Aida-CMK multi-algorithm optimization kernel applied to analog IC sizing
Lourenço, Ricardo; Horta, Nuno
2015-01-01
This work addresses the research and development of an innovative optimization kernel applied to analog integrated circuit (IC) design. Particularly, this works describes the modifications inside the AIDA Framework, an electronic design automation framework fully developed by at the Integrated Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, by enhancing AIDA-C, which is the circuit optimizer component of AIDA, with a new multi-objective multi-constraint optimization module that constructs a base for multiple algorithm implementations. The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with NSGAII, a swarm intelligence approach with MOPSO and stochastic hill climbing approach with MOSA. Moreover, the implemented structure allows the easy hybridization between kernels transforming the previous simple NSGAII optimization module into a more evolved and versatile module supporting multiple s...
An improved flux-split algorithm applied to hypersonic flows in chemical equilibrium
Palmer, Grant
1988-01-01
An explicit, finite-difference, shock-capturing numerical algorithm is presented and applied to hypersonic flows assumed to be in thermochemical equilibrium. Real-gas chemistry is either loosely coupled to the gasdynamics by way of a Gibbs free energy minimization package or fully coupled using species mass conservation equations with finite-rate chemical reactions. A scheme is developed that maintains stability in the explicit, finite-rate formulation while allowing relatively high time steps. The codes use flux vector splitting to difference the inviscid fluxes and employ real-gas corrections to viscosity and thermal conductivity. Numerical results are compared against existing ballistic range and flight data. Flows about complex geometries are also computed.
The Theory of Planned Behaviour Applied to Search Engines as a Learning Tool
Liaw, Shu-Sheng
2004-01-01
Search engines have been developed for helping learners to seek online information. Based on theory of planned behaviour approach, this research intends to investigate the behaviour of using search engines as a learning tool. After factor analysis, the results suggest that perceived satisfaction of search engine, search engines as an information…
FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.
Directory of Open Access Journals (Sweden)
Shouheng Tuo
Full Text Available Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS. Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models.In this study, two scoring functions (Bayesian network based K2-score and Gini-score are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models.We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR, specificity (SPC, positive predictive value (PPV and accuracy (ACC. Our method has identified two SNPs (rs3775652 and rs10511467 that may be also associated with disease in AMD dataset.
Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei
2014-04-01
Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.
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
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Akhtar, Mahmuda; Hannan, M A; Begum, R A; Basri, Hassan; Scavino, Edgar
2017-03-01
Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO 2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO 2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
H. Rezghian Moghadam
2018-06-01
Full Text Available The tremor injury is one of the common symptoms of Parkinson's disease. The patients suffering from Parkinson's disease have difficulty in controlling their movements owing to tremor. The intensity of the disease can be determined through specifying the range of intensity values of involuntary tremor in Parkinson patients. The level of disease in patients is determined through an empirical range of 0-5. In the early stages of Parkinson, resting tremor can be very mild and intermittent. So, diagnosing the levels of disease is difficult but important since it has only medication therapy. The aim of this study is to quantify the intensity of tremor by the analysis of electromyogram signal. The solution proposed in this paper is to employ a polynomial function model to estimate the Unified Parkinson's Disease Rating Scale (UPDRS value. The algorithm of Fast Orthogonal Search (FOS, which is based on identification of orthogonal basic functions, was utilized for model identification. In fact, some linear and nonlinear features extracted from wrist surface electromyogram signal were considered as the input of the model identified by FOS, and the model output was the UPDRS value. In this research, the proposed model was designed based on two different structures which have been called the single structure and parallel structure. The efficiency of designed models with different structures was evaluated. The evaluation results using K-fold cross validation approach showed that the proposed model with a parallel structure could determine the tremor severity of the Parkinson's disease with accuracy of 99.25% ±0.41, sensitivity of 97.17% ±1.9 and specificity of 99.72% ±0.18.
Wang, Yan; Huang, Song; Ji, Zhicheng
2017-07-01
This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.
Dorn-Wallenstein, Trevor Z.; Levesque, Emily
2017-11-01
Thanks to incredible advances in instrumentation, surveys like the Sloan Digital Sky Survey have been able to find and catalog billions of objects, ranging from local M dwarfs to distant quasars. Machine learning algorithms have greatly aided in the effort to classify these objects; however, there are regimes where these algorithms fail, where interesting oddities may be found. We present here an X-ray bright quasar misidentified as a red supergiant/X-ray binary, and a subsequent search of the SDSS quasar catalog for X-ray bright stars misidentified as quasars.
CONFORMATIONAL SEARCH BY POTENTIAL-ENERGY ANNEALING - ALGORITHM AND APPLICATION TO CYCLOSPORINE-A
VANSCHAIK, RC; VANGUNSTEREN, WF; BERENDSEN, HJC
A major problem in modelling (biological) macromolecules is the search for low-energy conformations. The complexity of a conformational search problem increases exponentially with the number of degrees of freedom which means that a systematic search can only be performed for very small structures.
Söderberg, Jonas; Alm Carlsson, Gudrun; Ahnesjö, Anders
2003-10-01
When dedicated software is lacking, treatment planning for fast neutron therapy is sometimes performed using dose calculation algorithms designed for photon beam therapy. In this work Monte Carlo derived neutron pencil kernels in water were parametrized using the photon dose algorithm implemented in the Nucletron TMS (treatment management system) treatment planning system. A rectangular fast-neutron fluence spectrum with energies 0-40 MeV (resembling a polyethylene filtered p(41)+ Be spectrum) was used. Central axis depth doses and lateral dose distributions were calculated and compared with the corresponding dose distributions from Monte Carlo calculations for homogeneous water and heterogeneous slab phantoms. All absorbed doses were normalized to the reference dose at 10 cm depth for a field of radius 5.6 cm in a 30 × 40 × 20 cm3 water test phantom. Agreement to within 7% was found in both the lateral and the depth dose distributions. The deviations could be explained as due to differences in size between the test phantom and that used in deriving the pencil kernel (radius 200 cm, thickness 50 cm). In the heterogeneous phantom, the TMS, with a directly applied neutron pencil kernel, and Monte Carlo calculated absorbed doses agree approximately for muscle but show large deviations for media such as adipose or bone. For the latter media, agreement was substantially improved by correcting the absorbed doses calculated in TMS with the neutron kerma factor ratio and the stopping power ratio between tissue and water. The multipurpose Monte Carlo code FLUKA was used both in calculating the pencil kernel and in direct calculations of absorbed dose in the phantom.
Zhao, Wei; Tang, Zhenmin; Yang, Yuwang; Wang, Lei; Lan, Shaohua
2014-01-01
This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties. PMID:24741341
Directory of Open Access Journals (Sweden)
Weizhen Rao
2016-01-01
Full Text Available The classical model of vehicle routing problem (VRP generally minimizes either the total vehicle travelling distance or the total number of dispatched vehicles. Due to the increased importance of environmental sustainability, one variant of VRPs that minimizes the total vehicle fuel consumption has gained much attention. The resulting fuel consumption VRP (FCVRP becomes increasingly important yet difficult. We present a mixed integer programming model for the FCVRP, and fuel consumption is measured through the degree of road gradient. Complexity analysis of FCVRP is presented through analogy with the capacitated VRP. To tackle the FCVRP’s computational intractability, we propose an efficient two-objective hybrid local search algorithm (TOHLS. TOHLS is based on a hybrid local search algorithm (HLS that is also used to solve FCVRP. Based on the Golden CVRP benchmarks, 60 FCVRP instances are generated and tested. Finally, the computational results show that the proposed TOHLS significantly outperforms the HLS.
Directory of Open Access Journals (Sweden)
Wei Zhao
2014-01-01
Full Text Available This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes’ moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties.