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Hybrid global-local optimisation algorithms for the layout design of tidal turbine arrays  [PDF]
George L. Barnett,Simon W. Funke,Matthew D. Piggott
Mathematics , 2014,
Abstract: Tidal stream power generation represents a promising source of renewable energy. In order to extract an economically useful amount of power, tens to hundreds of tidal turbines need to be placed within an array. The layout of these turbines can have a significant impact on the power extracted and hence on the viability of the site. Funke et al. formulated the question of the best turbine layout as an optimisation problem constrained by the shallow water equations and solved it using a local, gradient-based optimisation algorithm. Given the local nature of this approach, the question arises of how optimal the layouts actually are. This becomes particularly important for scenarios with complex bathymetry and layout constraints, both of which typically introduce locally optimal layouts. Optimisation algorithms which find the global optima generally require orders of magnitude more iterations than local optimisation algorithms and are thus infeasible in combination with an expensive flow model. This paper presents an analytical wake model to act as an efficient proxy to the shallow water model. Based upon this, a hybrid global-local two-stage optimisation approach is presented in which turbine layouts are first optimised with the analytical wake model via a global optimisation algorithm, and further optimised with the shallow water model via a local gradient-based optimisation algorithm. This procedure is applied to a number of idealised cases and a more realistic case with complex bathymetry in the Pentland Firth, Scotland. It is shown that in cases where bathymetry is considered, the two-stage optimisation procedure is able to improve the power extracted from the array by as much as 25% compared to local optimisation for idealised scenarios and by as much as 12% for the more realistic Pentland Firth scenario whilst in many cases reducing the overall computation time by approximately 35%.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization  [cached]
Yanhua Zhong,Changqing Yuan
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization) and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision, analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization  [cached]
Yanhua Zhong,Changqing Yuan
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization) and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision; analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
Comparative Study of Some New Hybrid Fuzzy Algorithms for Manipulator Control  [PDF]
Sudeept Mohan,Surekha Bhanot
Journal of Control Science and Engineering , 2007, DOI: 10.1155/2007/75653
Abstract: The robot manipulator is a highly complex system, which is multi-input, multi-output, nonlinear, and time variant. Controlling such a system is a tedious and challenging task. In this paper, some new hybrid fuzzy control algorithms have been proposed for manipulator control. These hybrid fuzzy controllers consist of two parts: a fuzzy controller and a conventional or adaptive controller. The outputs of these controllers are superimposed to produce the final actuation signal based on current position and velocity errors. Simulation is used to test these controllers for different trajectories and for varying manipulator parameters. Various performance indices like the RMS error, steady state error, and maximum error are used for comparison. It is observed that the hybrid controllers perform better than only fuzzy or only conventional/adaptive controllers.
Honeybees-inspired heuristic algorithms for numerical optimisation  [PDF]
Muharrem Dü?enci
Computer Science , 2015,
Abstract: Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remains as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this study, two swarm intelligence algorithms inspired of natural honeybee colonies have been overviewed with many respects and two new revisions and a hybrid version have been studied to improve the efficiencies in solving numerical optimisation problems, which are well-known hard benchmarks. Consequently, the revisions and especially the hybrid algorithm proposed have outperformed the two original bee algorithms in solving these very hard numerical optimisation benchmarks.
Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms
Jung-Hsien Chiang, Shih-Yi Chao
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-91
Abstract: In this study, we introduce a Genetic Algorithm-Recurrent Neural Network (GA-RNN) hybrid method for finding feed-forward regulated genes when given some transcription factors to construct cancer-related regulatory modules in human cancer microarray data. This hybrid approach focuses on the construction of various kinds of regulatory modules, that is, Recurrent Neural Network has the capability of controlling feed-forward and feedback loops in regulatory modules and Genetic Algorithms provide the ability of global searching of common regulated genes. This approach unravels new feed-forward connections in regulatory models by modified multi-layer RNN architectures. We also validate our approach by demonstrating that the connections in our cancer-related regulatory modules have been most identified and verified by previously-published biological documents.The major contribution provided by this approach is regarding the chain influences upon a set of genes sequentially. In addition, this inverse modeling correctly identifies known oncogenes and their interaction genes in a purely data-driven way.A regulatory module is a set of genes that is regulated or co-regulated by one or more common transcription factors (TFs). A TF is a protein that binds to a cis-regulatory element (e.g. an enhancer, a TATA box) and thereby, directly or indirectly, positively or negatively affects the initiation of transcription of regulated genes. A cancer-related regulatory module is a set of genes (oncogenes or tumor suppressor genes) that is regulated by one ore more common TFs. Modeling the cancer-related regulatory modules of the cell division cycle in human cells is a critical and fundamental step toward understanding cancers. The aim of this paper is not only to drive cancer-related regulatory modules, but also to identify the relationships of regulations between genes that fit the feed-forward or feedback influences. A feed-forward regulatory module, contains a TF that controls a second
Hybrid Genetic Algorithm with PSO Effect for Combinatorial Optimisation Problems  [PDF]
M. H. Mehta
International Journal of Advanced Computer Research , 2012,
Abstract: In engineering field, many problems are hard to solve in some definite interval of time. These problems known as “combinatorial optimisation problems” are of the category NP. These problems are easy to solve in some polynomial time when input size is small but as input size grows problems become toughest to solve in some definite interval of time. Long known conventional methods are not able to solve the problems and thus proper heuristics is necessary. Evolutionary algorithms based on behaviours of different animals and species have been invented and studied for this purpose. Genetic Algorithm is considered a powerful algorithm for solving combinatorial optimisation problems. Genetic algorithms work on these problems mimicking the human genetics. It follows principle of “survival of the fittest” kind of strategy. Particle swarm optimisation is a new evolutionary approach that copies behaviour of swarm in nature. However, neither traditional genetic algorithms nor particle swarm optimisation alone has been completely successful for solving combinatorial optimisation problems. Here a hybrid algorithm is proposed in which strengths of both algorithms are merged and performance of proposed algorithm is compared with simple genetic algorithm. Results show that proposed algorithm works definitely better than the simple genetic algorithm.
Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems
Liana Napalkova
Scientific Journal of Riga Technical University. Computer Sciences , 2009, DOI: 10.2478/v10143-010-0001-2
Abstract: The paper presents a taxonomic analysis of existing hybrid multi-objective evolutionary algorithms aimed at solving multi-objective simulation optimisation problems. For that, the properties of evolutionary algorithms and the requirements made to solving the problem considered are determined. Finally, a combination of the properties, which allows one to increase the approximation accuracy of the Pareto-optimal front at relatively low computational costs, is revealed.
Optimisation of Quantum Evolution Algorithms  [PDF]
Apoorva Patel
Physics , 2015,
Abstract: Given a quantum Hamiltonian and its evolution time, the corresponding unitary evolution operator can be constructed in many different ways, corresponding to different trajectories between the desired end-points. A choice among these trajectories can then be made to obtain the best computational complexity and control over errors. As an explicit example, Grover's quantum search algorithm is described as a Hamiltonian evolution problem. It is shown that the computational complexity has a power-law dependence on error when a straightforward Lie-Trotter discretisation formula is used, and it becomes logarithmic in error when reflection operators are used. The exponential change in error control is striking, and can be used to improve many importance sampling methods. The key concept is to make the evolution steps as large as possible while obeying the constraints of the problem. In particular, we can understand why overrelaxation algorithms are superior to small step size algorithms.
Comparative Molecular Modeling Study of Arabidopsis NADPH-Dependent Thioredoxin Reductase and Its Hybrid Protein  [PDF]
Yuno Lee, Songmi Kim, Prettina Lazar, Jeong Chan Moon, Swan Hwang, Sundarapandian Thangapandian, Youngsik Shon, Kyun Oh Lee, Sang Yeol Lee, Keun Woo Lee
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0046279
Abstract: 2-Cys peroxiredoxins (Prxs) play important roles in the protection of chloroplast proteins from oxidative damage. Arabidopsis NADPH-dependent thioredoxin reductase isotype C (AtNTRC) was identified as efficient electron donor for chloroplastic 2-Cys Prx-A. There are three isotypes (A, B, and C) of thioredoxin reductase (TrxR) in Arabidopsis. AtNTRA contains only TrxR domain, but AtNTRC consists of N-terminal TrxR and C-terminal thioredoxin (Trx) domains. AtNTRC has various oligomer structures, and Trx domain is important for chaperone activity. Our previous experimental study has reported that the hybrid protein (AtNTRA-(Trx-D)), which was a fusion of AtNTRA and Trx domain from AtNTRC, has formed variety of structures and shown strong chaperone activity. But, electron transfer mechanism was not detected at all. To find out the reason of this problem with structural basis, we performed two different molecular dynamics (MD) simulations on AtNTRC and AtNTRA-(Trx-D) proteins with same cofactors such as NADPH and flavin adenine dinucleotide (FAD) for 50 ns. Structural difference has found from superimposition of two structures that were taken relatively close to average structure. The main reason that AtNTRA-(Trx-D) cannot transfer the electron from TrxR domain to Trx domain is due to the difference of key catalytic residues in active site. The long distance between TrxR C153 and disulfide bond of Trx C387-C390 has been observed in AtNTRA-(Trx-D) because of following reasons: i) unstable and unfavorable interaction of the linker region, ii) shifted Trx domain, and iii) different or weak interface interaction of Trx domains. This study is one of the good examples for understanding the relationship between structure formation and reaction activity in hybrid protein. In addition, this study would be helpful for further study on the mechanism of electron transfer reaction in NADPH-dependent thioredoxin reductase proteins.
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