10,789 research outputs found
Determination of stability constants using genetic algorithms
A genetic algorithm (GA)-simplex hybrid approach has been developed for the determination of stability constants using calorimetric and polarographic data obtained from literature sources. The GA determined both the most suitable equilibrium model for the systems studied and the values of the stability constants and the heats of formation for the calorimetric studies. As such, a variable length chromosome format was devised to represent the equilibrium models and stability constants (and heats of formation). The polarographic data were obtained from studies of cadmium chloride and lead with the crown ether dicyclohexyl-18-crown-6. The calorimetric data were obtained from a study of a two step addition reaction of Hg(CN)2 with thiourea. The stability constants obtained using the GA-simplex hybrid approach compare favourably with the values quoted in the literature
Cuckoo Search Inspired Hybridization of the Nelder-Mead Simplex Algorithm Applied to Optimization of Photovoltaic Cells
A new hybridization of the Cuckoo Search (CS) is developed and applied to
optimize multi-cell solar systems; namely multi-junction and split spectrum
cells. The new approach consists of combining the CS with the Nelder-Mead
method. More precisely, instead of using single solutions as nests for the CS,
we use the concept of a simplex which is used in the Nelder-Mead algorithm.
This makes it possible to use the flip operation introduces in the Nelder-Mead
algorithm instead of the Levy flight which is a standard part of the CS. In
this way, the hybridized algorithm becomes more robust and less sensitive to
parameter tuning which exists in CS. The goal of our work was to optimize the
performance of multi-cell solar systems. Although the underlying problem
consists of the minimization of a function of a relatively small number of
parameters, the difficulty comes from the fact that the evaluation of the
function is complex and only a small number of evaluations is possible. In our
test, we show that the new method has a better performance when compared to
similar but more compex hybridizations of Nelder-Mead algorithm using genetic
algorithms or particle swarm optimization on standard benchmark functions.
Finally, we show that the new method outperforms some standard meta-heuristics
for the problem of interest
Hybrid Optimization Schemes for Quantum Control
Optimal control theory is a powerful tool for solving control problems in
quantum mechanics, ranging from the control of chemical reactions to the
implementation of gates in a quantum computer. Gradient-based optimization
methods are able to find high fidelity controls, but require considerable
numerical effort and often yield highly complex solutions. We propose here to
employ a two-stage optimization scheme to significantly speed up convergence
and achieve simpler controls. The control is initially parametrized using only
a few free parameters, such that optimization in this pruned search space can
be performed with a simplex method. The result, considered now simply as an
arbitrary function on a time grid, is the starting point for further
optimization with a gradient-based method that can quickly converge to high
fidelities. We illustrate the success of this hybrid technique by optimizing a
holonomic phasegate for two superconducting transmon qubits coupled with a
shared transmission line resonator, showing that a combination of Nelder-Mead
simplex and Krotov's method yields considerably better results than either one
of the two methods alone.Comment: 17 pages, 5 figures, 2 table
A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems
This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem
Hybrid Evolutionary Shape Manipulation for Efficient Hull Form Design Optimisation
‘Eco-friendly shipping’ and fuel efficiency are gaining much attention in the maritime industry due to increasingly stringent environmental regulations and volatile fuel prices. The shape of hull affects the overall performance in efficiency and stability of ships. Despite the advantages of simulation-based design, the application of a formal optimisation process in actual ship design work is limited. A hybrid approach which integrates a morphing technique into a multi-objective genetic algorithm to automate and optimise the hull form design is developed. It is envisioned that the proposed hybrid approach will improve the hydrodynamic performance as well as overall efficiency of the design process
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