Instance generators and test suites for the multiobjective quadratic assignment problem

We describe, and make publicly available, two problem instance generators for a multiobjective version of the well-known quadratic assignment problem (QAP). The generators allow a number of instance parameters to be set, including those controlling epistasis and inter-objective correlations. Based on these generators, several initial test suites are provided and described. For each test instance we measure some global properties and, for the smallest ones, make some initial observations of the Pareto optimal sets/fronts. Our purpose in providing these tools is to facilitate the ongoing study of problem structure in multiobjective (combinatorial) optimization, and its effects on search landscape and algorithm performance.

Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-for-purpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided. Copyright © 2010 Royal Meteorological Society and Crown Copyright.

A benchmark method for global optimization problems in structure determination from powder diffraction data

Quasi-Newton-Raphson minimization and conjugate gradient minimization have been used to solve the crystal structures of famotidine form B and capsaicin from X-ray powder diffraction data and characterize the chi(2) agreement surfaces. One million quasi-Newton-Raphson minimizations found the famotidine global minimum with a frequency of ca 1 in 5000 and the capsaicin global minimum with a frequency of ca 1 in 10 000. These results, which are corroborated by conjugate gradient minimization, demonstrate the existence of numerous pathways from some of the highest points on these chi(2) agreement surfaces to the respective global minima, which are passable using only downhill moves. This important observation has significant ramifications for the development of improved structure determination algorithms.

Optimization of protoplast production in white lupin

The influence, was investigated, of abiotic parameters on the isolation of protoplasts from in vitro seedling cotyledons of white lupin. The protoplasts were found to be competent in withstanding a wide range of osmotic potentials of the enzyme medium, however, −2.25 MPa (0.5 M mannitol), resulted in the highest yield of protoplasts. The pH of the isolation medium also had a profound effect on protoplast production. Vacuum infiltration of the enzyme solution into the cotyledon tissue resulted in a progressive drop in the yield of protoplasts. The speed and duration of orbital agitation of the cotyledon tissue played a significant role in the release of protoplasts and a two step (stationary-gyratory) regime was found to be better than the gyratory-only system.

Optimization of electroless copper plating on polyethylene films modified by surface grafting of vinyl ether of monoethanolamine

Metallized plastics have recently received significant interest for their useful applications in electronic devices such as for integrated circuits, packaging, printed circuits and sensor applications. In this work the metallized films were developed by electroless copper plating of polyethylene films grafted with vinyl ether of monoethanoleamine. There are several techniques for metal deposition on surface of polymers such as evaporation, sputtering, electroless plating and electrolysis. In this work the metallized films were developed by electroless copper plating of polyethylene films grafted with vinyl ether of monoethanoleamine. Polyethylene films were subjected to gamma-radiation induced surface graft copolymerization with vinyl ether of monoethanolamine. Electroless copper plating was carried out effectively on the modified films. The catalytic processes for the electroless copper plating in the presence and the absence of SnCl2 sensitization were studied and the optimum activation conditions that give the highest plating rate were determined. The effect of grafting degree on the plating rate is studied. Electroless plating conditions (bath additives, pH and temperature) were optimized. Plating rate was determined gravimetrically and spectrophotometrically at different grafting degrees. The results reveal that plating rate is a function of degree of grafting and increases with increasing grafted vinyl ether of monoethanolamine onto polyethylene. It was found that pH 13 of electroless bath and plating temperature 40°C are the optimal conditions for the plating process. The increasing of grafting degree results in faster plating rate at the same pH and temperature. The surface morphology of the metallized films was investigated using scanning electron microscopy (SEM). The adhesion strength between the metallized layer and grafted polymer was studied using tensile machine. SEM photos and adhesion measurements clarified that uniform and adhered deposits were obtained under optimum conditions.

Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvres

This paper deals with the design of optimal multiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned search space, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.

RBF equalizer design using directional evolutionary multi-objective optimization

Whilst radial basis function (RBF) equalizers have been employed to combat the linear and nonlinear distortions in modern communication systems, most of them do not take into account the equalizer's generalization capability. In this paper, it is firstly proposed that the. model's generalization capability can be improved by treating the modelling problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets. Then, as a modelling application, a new RBF equalizer learning scheme is introduced based on the directional evolutionary MOO (EMOO). Directional EMOO improves the computational efficiency of conventional EMOO, which has been widely applied in solving MOO problems, by explicitly making use of the directional information. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good performance not only on explaining the training samples but on predicting the unseen samples.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.