optimisation algorithms
Model driven engineering of planning and optimisation algorithms for pervasive computing environments
... As evidenced in [10] and [11], there is a growing interest in applying model-driven techniques in pervasive computing environments for purposes such as managing heterogeneity of devices, masking the complexity of dynamic ...
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Convergence rates of stochastic global optimisation algorithms with backtracking : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University
... successful optimisation algorithms that backtracking adaptive search seeks to ...These algorithms, particularly the last named, are discussed in more detail later in this ...global ...
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Model driven engineering of planning and optimisation algorithms for pervasive computing environments
... Rows 2-4 of Table 4 show the optimal results obtained for the extended scenario using a temperature of 500 over 50, 100 and 500 runs. The best result (219.55, 296.41, 22) was obtained over 500 runs. Fig. 11 shows the ...
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II.R EVIEW OF GLOBAL SEARCH OPTIMISATION ALGORITHMS
... Traditional optimisation of portfolios has focused on determining the optimal portfolio given the history of asset returns and assuming that the distribution of returns is Gaussian and stationary over ...any ...
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Can compact optimisation algorithms be structurally biased?
... compact algorithms under investi- gations appeared to be more ‘immune’ to the SB than their population-based equivalents according to the proposed visual ...
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The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
... To save a plottable file containing the average fitness trend of an algorithm over a specific problem, SOS first fills an array of size equal to the computational budget with the values from the FTrend object returned by ...
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Airline Disruption Recovery Using Symbiotic Simulation and Multi fidelity Modelling
... However, high-fidelity simulation models have non-negligible computation time, which proves prob- lematic for search and optimisation algorithms when there is a large solution space and tight time ...
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Algorithms Applied to Global Optimisation – Visual Evaluation
... and optimisation algorithms is subject of large research ...global optimisation and presented approach is based on observation and visual evaluation of Real-Coded Genetic Algorithm, Particle Swarm ...
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Search-Based Synthesis of Probabilistic Models for Quality-of-Service Software Engineering
... Construct validity threats may be due to the simplifica- tions and assumptions we made when modelling the DPM and FX systems. To mitigate this threat, the DPM system, model and requirements are based on a validated ...
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Methodology to Validate Results from European Research Projects: The C2NET Case Study
... of “AvailabilityAmount” and the optimisation algorithms use this concept for managing the data. In the same way, the optimisation’s results are stored in the Plan Data Model, also called PTables that is ...
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Applying a global optimisation algorithm to Fund of Hedge Funds portfolio optimisation
... search optimisation algorithms, both the linear and non-linear constraints are defined as penalty functions added to the objective function and hence are soft constraints rather than hard constraints that ...
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Co evolving memetic algorithms: A learning approach to robust scalable optimisation
... the algorithms is less than 100% for lengths above 32, we per- formed an analysis of variance (ANOVA) on the best fitness at the end of each run, which confirmed that the perfor- mance is statistically ...
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Hyperparameter optimisation for improving classification under class imbalance
... learning algorithms to be more appropri- ate for imbalanced ...classification algorithms involve some hyperparameters, which might influence the performance ...hyperparameter optimisation research ...
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PID controller optimisation using genetic algorithms
... Genetic Algorithms over classical analytical ...for optimisation problems, there needs to be research performed to measure the improvements across the various different categories – perhaps using Haupt and ...
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Test set generation and optimisation using evolutionary algorithms and cubical calculus
... In the context of run-time, the fact that GA-MITS is a relatively slow algorithm should not hinder its possible role in today’s CAD world. For the largest ISCAS-85 circuit, c7552, and an original test set generated by ...
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Genetic Algorithm Optimisation for Finance and Investments
... portfolio optimisation because humans have limited cog- nitive ability and can be inconsistent in decision making; see Kahneman and Tversky ...Genetic algorithms provide one method for the rapid evaluation ...
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Modelling Human like Behavior through Reward based Approach in a First Person Shooter Game
... study algorithms of path finding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank ...
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Multi objective optimisation in air conditioning systems : comfort/discomfort definition by IF sets
... The benefit of the IF optimisation problems is twofold: they give richest apparatus for formulation of optimisation problems and, on the other hand, the solution of IF optimisation probl[r] ...
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Self limitation, dynamic and flexible approaches for particle swarm optimisation
... Area Extended PSO (AEPSO) was introduced several years ago and has been used in several optimisation problems including machine learning and navigation problems (A. Atyabi & Phon-Amnuaisuk, 2007; A. Atyabi ...
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Parameter optimisation of river water quality models using genetic algorithms
... Table 4.7 shows the statistics of estimated incremental flows for each river reach determined from the 10 events (Table 3.9) to give an overall inchcation of the magnitude of flow used[r] ...
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