Solving a bi-objective winner determination problem in a transportation procurement auction

Scheduling is a vital issue in cloud computing in order to facilitate optimized usage of resources. Genetic algorithms are used in optimization techniques as they are class of heuristic approaches that have their inspirations from evolutionary ideas of natural evolution. The cloud scheduling optimization problem is modelled as a population of candidate solutions and the genetic algorithm is applied for benefitting the fittest candidates. This paper presents a broad overview on the formalization of works contributed by variants of Genetic Algorithm to the field of Scheduling which can be applied to cloud computing.

Physical resource coordination in manufacturing systems

Genetic algorithms (GAs) have achieved lots of success in various applications, but the problem to balance exploration and exploitation of population is still up in the air. In this paper, we propose a variant of genetic algorithm with mating operator GASF to alleviate the problem;GASFmeasures the mating attractiveness of individuals from four aspects: gender, age, similarity and fitness. Individuals are assigned gender to facilitate mimicking human reproduction, and contributions of age, similarity and fitness to the attractiveness are respectively quantified and self-adaptively adjusted. Experimental results indicate that the proposed approach can achieve competitive performance with improved convergence.

In this paper we propose a novel software, named ForestTaxator, supporting terrestrial laser scanning data processing, which for dendrometric tree analysis can be divided into two main processes: tree detection in the point cloud and development of three-dimensional models of individual trees. The usage of genetic algorithms to solve the problem of tree detection in 3D point cloud and its cross-sectional area approximation with ellipse-based model is also presented. The detection and approximation algorithms are proposed and tested using various variants of genetic algorithms. The work proves that the genetic algorithms work very well: the obtained results are consistent with the reference data to a large extent, and the time of genetic calculations is very short. The attractiveness of the presented software is due to the fact that it provides all necessary functionalities used in the forest inventory field. The software is written in C# and runs on the .NET Core platform, which ensures its full portability between Windows, MacOS and Linux. It provides a number of interfaces thus ensuring a high level of modularity. The software and its code are made freely available.

Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population

Optimisation of mooring line parameters for offshore floating structures: A review paper

Transverse shear effects in discrete optimization of laminated compressed cylindrical shells

The Vertex Separation Problem (VSP) is an NP-hard combinatorial optimization problem in the context of graph theory. The importance of studying VSP lies in its close relation with other problems. Thus, VSP has important practical applications in the contexts of very large scale integration design, computer language compiler design, natural language processing, order processing of manufactured products and bioinformatics. Up to our knowledge, there are only two trajectory-based metaheuristic algorithms for VSP documented in the literature. The main contribution of this chapter is that we extend the available heuristics to solve VSP by proposing a genetic algorithm (GA). It is of particular interest to study the impact of four different crossover operators in the algorithm performance. The experimental results showed that the order-based crossover is the best. Moreover, the best GA variant was compared with the best algorithm for VSP: GVNS. The results of this comparison showed that GVNS outperforms our best GA variant by approximately 1.54 times in solution quality.

Over the years, rapid prototyping (RP) has been implemented in several companies in the manufacture of three-dimensional (3D) models. This process produces parts in a short time, in small quantities, and with complex geometry and is cheaper. RP can be divided into additive manufacturing and subtractive manufacturing, the former being the most used in 3D object printing. Additive manufacturing is a widely used process consisting of the adding of material layer by layer for building 3D objects from a model projected on a computer. This technology allows the printing of objects with complex shapes and is being increasingly adopted by the aircraft industry, jewelry, footwear, automotive, fashion products and medical implants, among others. When printing 3D objects, the build orientation optimization of 3D models influences the costs and surface quality. In this work, three build orientation optimization problems are studied: the single-objective problem, bi-objective problem and many-objective problem. To this end, three quality measures are applied: the support area, the build time and the surface roughness, for the car hoodvent model. First, a single-objective optimization problem is presented and solved by the genetic algorithm, obtaining optimal solutions for each objective function. Then, a study of the bi-objective optimization problem is carried out for each pair of objectives, and some representative trade-off solutions are identified. Finally, the study of the many-objective optimization problem, considering the three measures optimized simultaneously, is presented with some more optimal solutions found. The bi-objective and many-objective problems are solved by a multi-objective genetic algorithm. For a better analysis and comparison of the solutions found, Pareto fronts are used, enabling a better visualization of the solutions between the objectives. This chapter aims to assist the decision-maker in choosing the best part print orientation angles according to his/her preferences. The proposed approach found optimal solutions confirming its effectiveness.

Conventional biomarker discovery focuses mostly on the identification of single markers and thus often has limited success in disease diagnosis and prognosis. This study proposes a method to identify an optimized protein biomarker panel based on MS studies for predicting the risk of major adverse cardiac events (MACE) in patients. Since the simplicity and concision requirement for the development of immunoassays can only tolerate the complexity of the prediction model with a very few selected discriminative biomarkers, established optimization methods, such as conventional genetic algorithm (GA), thus fails in the high-dimensional space. In this paper, we present a novel variant of GA that embeds the recursive local floating enhancement technique to discover a panel of protein biomarkers with far better prognostic value for prediction of MACE than existing methods, including the one approved recently by FDA (Food and Drug Administration). The new pragmatic method applies the constraints of MACE relevance and biomarker redundancy to shrink the local searching space in order to avoid heavy computation penalty resulted from the local floating optimization. The proposed method is compared with standard GA and other variable selection approaches based on the MACE prediction experiments. Two powerful classification techniques, partial least squares logistic regression (PLS-LR) and support vector machine classifier (SVMC), are deployed as the MACE predictors owing to their ability in dealing with small scale and binary response data. New preprocessing algorithms, such as low-level signal processing, duplicated spectra elimination, and outliner patient's samples removal, are also included in the proposed method. The experimental results show that an optimized panel of seven selected biomarkers can provide more than 77.1% MACE prediction accuracy using SVMC. The experimental results empirically demonstrate that the new GA algorithm with local floating enhancement (GA-LFE) can achieve the better MACE prediction performance comparing with the existing techniques. The method has been applied to SELDI/MALDI MS datasets to discover an optimized panel of protein biomarkers to distinguish disease from control.

Propagation of light through layered metamaterials consisting of a stack of optically linear metal and dielectric slabs may be described as linear spatial filtering. Depending on the point spread function, the metamaterial can be applied for sub-diffraction spatial filtering or for classical filtering operations on the wavefront. We optimize the metamaterial with respect to the shape of the complex amplitude transfer function, the average transmission coefficient and to average reflections. A measure of similarity obtained using the Hölder's inequality is adapted to construct a criterion function. Optimization is conducted using a variant of genetic algorithm. In particular, the metamaterial's transfer function is optimized for high-pass filtering. The metamaterial can be then applied to modify the contrast of the object or to introduce a phase-contrast.

This paper presents the application of the hybridized bat algorithm to the constrained portfolio optimization problem which is a hard optimization problem suitable for stochastic optimization metaheuristics. Bat algorithm is a recent member of the group of nature-inspired algorithms. Hybridization between bat and artificial bee colony metaheuristics was adapted for solving portfolio problem with constraints that extend classical mean-variance portfolio selection formulation. To test the robustness of our hybridized approach, a comparative analysis with other swarm intelligence algorithms, as well as with three variants of genetic algorithm was performed. All algorithms included in comparative analysis were tested on the same portfolio model using the same data set. Results show that proposed hybridized bat algorithm has a great potential for tackling constrained portfolio problem.

Fuzzy Multi-choice Multi-objective Multi-item Five-Dimensional Transportation Problem and Its Solution by Variants of Genetic Algorithm

A method based on a variant of genetic algorithm is proposed to obtain the wavefront aberrations of a real ronchigrams using only one ronchigram without using polynomial fit or trapezoidal integration. The recovery of the aberration coefficients of third order is achieved by assigning random values but controlled in the equation of the optical path difference (OPD) which is given for a lateral shear interferometer. The proposed method retrieves the coefficients of the polynomial of the analyzed Ronchigram in a reliable and accurate way.

Phase reassignment for load balance in low-voltage distribution networks