Search

Large-scale 3D models consume large computing and storage resources. To address this challenging problem, this paper proposes a new method to obtain the optimal simplified 3D mesh models with the minimum approximation error. First, we propose a feature-preservation edge collapse operation to maintain the feature edges, in which the collapsing cost is calculated in a novel way by combining Gauss curvature and Quadratic Error Metrics (QEM). Second, we introduce the edge splitting operation into the mesh simplification process and propose a hybrid 'undo/redo' mechanism that combines the edge splitting and edge collapse operation to reduce the number of long and narrow triangles. Third, the proposed 'undo/redo' mechanism can also reduce the approximation error; however, it is impossible to manually choose the best operation sequence combination that can result in the minimum approximation error. To solve this problem, we formulate the proposed mesh simplification process as an optimization model, in which the solution space is composed of the possible combinations of operation sequences, and the optimization objective is the minimum of the approximation error. Finally, we propose a novel optimization algorithm, WOA-DE, by replacing the exploration phase of the original Whale Optimization Algorithm (WOA) with the mutate and crossover operations of Differential Evolution (DE) to compute the optimal simplified mesh model more efficiently. We conduct numerous experiments to test the capabilities of the proposed method, and the experimental results show that our method outperforms the previous methods in terms of the geometric feature preservation, triangle quality, and approximation error. [ABSTRACT FROM AUTHOR]

Nowadays, railway infrastructure designers rely heavily on computer simulators and expert systems to model, analyze and evaluate potential deployments prior to their installation. This paper presents the railway power consumption simulator model (RPCS), a cloud-based model for the design, simulation and evaluation of railway electric infrastructures. This model integrates the parameters of an infrastructure within a search engine that generates and evaluates a set of simulations to achieve optimal designs, according to a given set of objectives and restrictions. The knowledge of the domain is represented as an ontology that translates the elements in the infrastructure into an electric circuit, which is simulated to obtain a wide range of electric metrics. In order to support the execution of thousands of scenarios in a scalable, efficient and fault-tolerant manner, this paper introduces an architecture to deploy the model in a cloud environment, and a dimensioning model to find the types and number of instances that maximize performance while minimizing the externalization costs. The resulting model is applied to a particular case study, allowing the execution of over one thousand concurrent experiments in a virtual cluster on the Amazon Elastic Compute Cloud. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) have great challenges for traditional Pareto-based multi-objective evolutionary algorithms (MOEAs). One of the main reasons is that Pareto dominance cannot effectively rank/evaluate the solutions, resulting in almost random selection of the new population and no quality guarantee of the solutions in the new population. To overcome this shortcoming, in this paper, a new fitness evaluation mechanism is proposed for MaOPs. It uses a tailor-made integration of convergence and diversity measures, and can well balance convergence and diversity in solution evaluation. Specifically, to cooperate it with the subsequent selection mechanism, a new global convergence strength-based convergence measure is designed to avoid the interference from solution's local information. To overcome the shortcoming that the existing angle-based diversity measure cannot distinguish the contributions to diversity of two solutions which have the same angle with reference direction, a new angle-distance based diversity measure is designed, which can accurately evaluate the contribution of each solution to diversity. Furthermore, to well balance the convergence and diversity, the proposed convergence measure and diversity measure have the same order of magnitude. As a result, the proposed convergence-diversity balanced fitness evaluation mechanism can accurately evaluate each solution's quality and then properly increase the selection pressure. The proposed method is compared with five state-of-the-art many-objective evolutionary algorithms by experiments on 120 test instances of 24 benchmark problems with up to 20 objectives and a real world problem. The results indicate the competitiveness and effectiveness of our proposed algorithm in keeping a good balance between convergence and diversity. [ABSTRACT FROM AUTHOR]

It seems clear that general adoption of electric vehicles is coming in the near future. But this adoption will bring new challenges as, for example, that of recharging the batteries of a large fleet of electric vehicles under power and other technological constraints of the charging infrastructure. Among others, these will require solving challenging scheduling problems as well. In this paper, we study one of such problems derived from a charging station designed to be installed in community parks, which consists in scheduling a set of jobs on a single machine with varying capacity over time and exhibits high computational complexity. We propose the use of meta-heuristics as a means to solving the problem efficiently. Concretely, we propose a memetic algorithm, that combines a genetic algorithm with a local search method specifically designed for the problem. The contributions are analyzed theoretically, with formal proofs of their properties, and evaluated empirically. Experimental results show that the proposed memetic algorithm is very effective at solving the problem, while keeping running times reasonably low. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA). WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation. Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been used to solve a variety of many-objective optimization problems, where these problems contain more than three conflicting objectives. Most existing evolutionary algorithms have shown to perform well on many-objective optimization problems with regular Pareto optimal fronts, their performance, however, will often considerably deteriorate on those whose Pareto optimal fronts are irregular, e.g., discontinuous, degenerated and convex. To address this issue, in this paper, we propose a region division based many-objective optimization evolutionary algorithm, termed RdEA, where a region division approach is suggested to maintain diversity of population for many-objective optimization (especially for problems with irregular Pareto fronts). In the proposed region division based diversity maintaining approach, the geometric information of Pareto optimal fronts is taken into account by using the non-dominated solutions found at each generation, which helps to solve the problems with irregular Pareto optimal fronts better. The proposed RdEA is compared with five state-of-the-art many-objective evolutionary algorithms on 16 test problems from two test suites DTLZ and WFG and a real-world optimization problem. Experimental results on these problems demonstrate that the competitiveness of the proposed algorithm in solving many-objective optimization problems, especially for those with irregular Pareto optimal fronts. [ABSTRACT FROM AUTHOR]

Wind energy production have been increasing in last years, with an annual growth of the installed capacity rate about 20%. It becomes important to develop optimization techniques to improve the effectiveness of the wind farms. One field in which this can be done is in the distribution network design that interconnects the turbines and the substation. This paper proposes two mathematical models to obtain the optimal electrical interconnection configuration of the wind farm turbines, considering technical constraints. One model minimizes the installation costs and the other one minimizes the installation costs and the energy losses costs registered during the wind farm lifetime. This problem corresponds to a capacitated minimum spanning tree with additional constraints. The proposed models were applied in two real wind farms. A sensitivity analysis is performed over two electrical parameters, the power factor and the load factor. The results show that the electrical losses of the wind farm must be taken into account in the optimization process. [ABSTRACT FROM AUTHOR]

Sharing feature-based computer-aided design (CAD) models is a challenging problem that is frequently encountered among heterogeneous CAD systems. In this work, a new asymmetric strategy is presented to enrich the theory of feature-based interoperability, particularly when addressing a singular feature or singular sketch. This paper analyzes the semantic asymmetry singular feature interoperability (SA-SFI) and parameter asymmetry singular sketch interoperability (PA-SSI) in detail. We pay special attention to the problem of PA-SSI, which is universally significant in collaborative product development (CPD). The objective of PA-SSI is to develop an optimized model to exchange a singular sketch (spline) to ensure that the exchanged model both maintains high geometric fidelity and can be effectively edited in the target CAD system. The proposed method applies the estimation of distribution algorithm (EDA) to automatically solve this problem, and a Gaussian mixture model (GMM) is built according to the promising solutions. Furthermore, Hausdorff distance is adopted to calculate the fitness, and a local optimization operator is designed to enhance the global search capability of the population. Experimental results demonstrate that the proposed approach can maintain a sufficiently high geometric fidelity, and ensure that the exchanged model of the target CAD system can be parametrically edited. [ABSTRACT FROM AUTHOR]

We investigate how to make a simpler version of an existing algorithm, named C³E, from Consensus between Classification and Clustering Ensembles, more user-friendly by automatically tuning its main parameters with the use of metaheuristics. In particular, C³E based on a Squared Loss function, C³E-SL, assumes an optimization procedure that takes as input class membership estimates from existing classifiers, as well as a similarity matrix from a cluster ensemble operating solely on the new target data, to provide a consolidated classification of the target data. To do so, two parameters have to be defined a priori, namely: the relative importance of classifier and cluster ensembles and the number of iterations of the algorithm. In some practical applications, these parameters can be optimized via time consuming grid search approaches based on cross-validation procedures. This paper shows that seven metaheuristics for parameter optimization yield classifiers as accurate as those obtained from grid search, but taking half the running time. More precisely, and by assuming a trade-off between user-friendliness and accuracy, experiments performed on twenty real-world datasets suggest that CMA-ES, DE, and SaDE are the best alternatives to optimize the C³E-SL parameters. [ABSTRACT FROM AUTHOR]

One of the main objectives when designing welded products is to reduce strains and deformations. Strains can cause excessive angular distortion. This results in a welded product that does not meet acceptable tolerances. The geometry of the weld bead (height and width) depends on the input parameters (speed, voltage and current), and provides the welded joint with strength and quality. As welded products become increasingly complex, deformations become more difficult to predict as they depend greatly on the welding sequence. This paper shows how a combination of the Finite Element Method, Genetic Algorithms and Regression Trees may be used to design and optimize complex welded products. Initially, Artificial Neural Networks and Regression Trees that are based on heuristic methods and evolutionary algorithms were used in predicting the weld bead geometry according to the input parameters. Then, thermo-mechanical Finite Element models were created to obtain the temperature field and the angular distortion using the weld bead geometry that the best predictive models generated. Finally, optimization techniques that are based on Genetic Algorithms were used to validate these Finite Element models against experimental results, and to subsequently find the optimal welding sequence to use in the manufacture of complex welded products. [ABSTRACT FROM AUTHOR]