The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Brian C Omeara - One of the best experts on this subject based on the ideXlab platform.
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new Heuristic Methods for joint species delimitation and species tree inference
Systematic Biology, 2010Co-Authors: Brian C OmearaAbstract:Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining the possible assignments a priori. It introduces a parametric and a nonparametric method, including new Heuristic search strategies, to do this delimitation and tree inference using individual gene trees as input. The new Methods were evaluated using thousands of simulations and 4 empirical data sets. These analyses suggest that the new Methods, especially the nonparametric one, may provide useful insights for systematists working at the species level with molecular data. However, they still often return incorrect results. (Brownie; gene tree parsimony; gene tree species tree; speciation; species delimitation.)
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new Heuristic Methods for joint species delimitation and species tree inference
Systematic Biology, 2010Co-Authors: Brian C OmearaAbstract:Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining the possible assignments a priori. It introduces a parametric and a nonparametric method, including new Heuristic search strategies, to do this delimitation and tree inference using individual gene trees as input. The new Methods were evaluated using thousands of simulations and 4 empirical data sets. These analyses suggest that the new Methods, especially the nonparametric one, may provide useful insights for systematists working at the species level with molecular data. However, they still often return incorrect results.
A Testa - One of the best experts on this subject based on the ideXlab platform.
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on convergence of conventional and meta Heuristic Methods for security constrained opf analysis
ACM Symposium on Applied Computing, 2016Co-Authors: Jagadeesh Gunda, Sasa Z Djokic, R Langella, A TestaAbstract:Security-constrained optimal power flow (SCOPF) studies are used for assessing network performance during both planning and operational stages. The requirements for increased flexibility and improved security necessitate to use robust and computationally efficient SCOPF Methods, which are crucial for "smart grid" applications requiring (close to) real-time network control. Conventional SCOPF Methods solve the corresponding nonlinear power flow equations using gradient-based iterative approaches and are computationally efficient, but sensitive to selection of initial values and might suffer from convergence problems. MetaHeuristic SCOPF Methods are based on various approaches that search over the system state space and do not suffer from convergence problems, but are more computationally demanding. While network planners and operators regularly use conventional SCOPF Methods, meta-Heuristic Methods are rarely implemented in practice, even for off-line analysis during the planning stage. Using as an example the IEEE 30-bus test network, this paper analyses and compares conventional and meta-Heuristic Methods for security-constrained OPF studies, showing that meta-Heuristic Methods can be used when conventional Methods fail to converge and/or to provide a global optimum solution.
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comparison of conventional and meta Heuristic Methods for security constrained opf analysis
AEIT International Annual Conference, 2015Co-Authors: Jagadeesh Gunda, Sasa Z Djokic, R Langella, A TestaAbstract:Development and implementation of accurate, robust and computationally efficient analytical and modelling tolls is very important for the anticipated transformation of existing networks into the future "smart grids". These tools for network analysis are used at both planning and operating stages, in order to ensure optimal design and configuration of power supply systems, in terms of the requirements for higher flexibility, increased security and improved overall techno-economic performance of modelled networks. In this context, particularly important are "smart grid" applications requiring (close to) real-time controls of large and interconnected power supply systems under serious contingency scenarios and other "highly stressed" network operating conditions. This paper provides a detailed discussion and analysis of both conventional and meta-Heuristic Methods for security-constrained optimal power flow (SCOPF) studies. The comparison of performance of two conventional SCOPF Methods and three meta-Heuristic SCOPF algorithms is illustrated on IEEE 14-bus and IEEE 30-bus test networks. The analysis and optimization of objective functions in considered SCOPF Methods include minimization of constraint violations in post-contingency states, as well as minimization of fuel costs, active power losses, and CO2 emissions.
Christoph H Loch - One of the best experts on this subject based on the ideXlab platform.
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Heuristic Methods for updating small world representations in strategic situations of knightian uncertainty
Academy of Management Review, 2020Co-Authors: Alberto Feduzi, Philip Faulkner, Jochen Runde, Laure Cabantous, Christoph H LochAbstract:Recent studies on the construction and use of “small world representations” in strategic decision-making under Knightian uncertainty say little about how such representations should be updated over...
Andrew J Parkes - One of the best experts on this subject based on the ideXlab platform.
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combining monte carlo and hyper Heuristic Methods for the multi mode resource constrained multi project scheduling problem
Information Sciences, 2016Co-Authors: Shahriar Asta, Daniel Karapetyan, Ahmed Kheiri, Ender Ozcan, Andrew J ParkesAbstract:Investigates the novel solution structures arising in multi-project scheduling.Presents specific algorithm components for scheduling of multiple projects.Combines all the algorithm components with a hyper-Heuristic and memetic algorithm.Significantly outperforms other Methods on a set of "hidden" instances.Produces new best solutions for some long-standing multi-mode PSPLIB instances. Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-Heuristic Methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of "hidden" instances, i.e. instances not available at the algorithm design phase.
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combining monte carlo and hyper Heuristic Methods for the multi mode resource constrained multi project scheduling problem
arXiv: Data Structures and Algorithms, 2015Co-Authors: Shahriar Asta, Daniel Karapetyan, Ahmed Kheiri, Ender Ozcan, Andrew J ParkesAbstract:Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-Heuristic Methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of "hidden" instances, i.e. instances not available at the algorithm design phase.
Jagadeesh Gunda - One of the best experts on this subject based on the ideXlab platform.
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on convergence of conventional and meta Heuristic Methods for security constrained opf analysis
ACM Symposium on Applied Computing, 2016Co-Authors: Jagadeesh Gunda, Sasa Z Djokic, R Langella, A TestaAbstract:Security-constrained optimal power flow (SCOPF) studies are used for assessing network performance during both planning and operational stages. The requirements for increased flexibility and improved security necessitate to use robust and computationally efficient SCOPF Methods, which are crucial for "smart grid" applications requiring (close to) real-time network control. Conventional SCOPF Methods solve the corresponding nonlinear power flow equations using gradient-based iterative approaches and are computationally efficient, but sensitive to selection of initial values and might suffer from convergence problems. MetaHeuristic SCOPF Methods are based on various approaches that search over the system state space and do not suffer from convergence problems, but are more computationally demanding. While network planners and operators regularly use conventional SCOPF Methods, meta-Heuristic Methods are rarely implemented in practice, even for off-line analysis during the planning stage. Using as an example the IEEE 30-bus test network, this paper analyses and compares conventional and meta-Heuristic Methods for security-constrained OPF studies, showing that meta-Heuristic Methods can be used when conventional Methods fail to converge and/or to provide a global optimum solution.
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comparison of conventional and meta Heuristic Methods for security constrained opf analysis
AEIT International Annual Conference, 2015Co-Authors: Jagadeesh Gunda, Sasa Z Djokic, R Langella, A TestaAbstract:Development and implementation of accurate, robust and computationally efficient analytical and modelling tolls is very important for the anticipated transformation of existing networks into the future "smart grids". These tools for network analysis are used at both planning and operating stages, in order to ensure optimal design and configuration of power supply systems, in terms of the requirements for higher flexibility, increased security and improved overall techno-economic performance of modelled networks. In this context, particularly important are "smart grid" applications requiring (close to) real-time controls of large and interconnected power supply systems under serious contingency scenarios and other "highly stressed" network operating conditions. This paper provides a detailed discussion and analysis of both conventional and meta-Heuristic Methods for security-constrained optimal power flow (SCOPF) studies. The comparison of performance of two conventional SCOPF Methods and three meta-Heuristic SCOPF algorithms is illustrated on IEEE 14-bus and IEEE 30-bus test networks. The analysis and optimization of objective functions in considered SCOPF Methods include minimization of constraint violations in post-contingency states, as well as minimization of fuel costs, active power losses, and CO2 emissions.
