Optimized normal and distance matching for heterogeneous object modeling

Clamshell casting is a popular manufacturing technique where liquid is poured into a mold or cast and the cast is removed once the liquid has hardened.The term clamshell refers to the way in which the cast is removed.The two cast parts are removed by rotation through a hinge much like a clamshell.Given an object, modeled by a polyhedron P of arbitrary genus with combinatorial complexity n , we determine when the surface of the polyhedron can be decomposed into two parts s.t. each part can be rotated around a line in space without colliding with each other or intersecting the interior.Such a line is a valid casting line.Specifically, we solve the following problems: (1) Given a line l in space, we determine in ( )O n time whether l is a valid casting line for P .If degeneracies are present, such as a reflex edge or face of P is perpendicular to l , the running time increases slightly to ( log ) O n n .(2) In 4 ( ( )) α O n n time, where ( ) α n is the inverse Ackermann function, we report all combinatorially distinct valid casting lines.The running time is 4 ( log ) O n n in the presence of degeneracies.All of the running times are shown to be almost optimal.

Assessment of the performance of metaheuristic methods used for the inverse identification of effective heat capacity of phase change materials

Big-Bang and Big-crunch (BB-BC), a heuristic optimization method is based on the concept of universal evolution. FireFly optimization (FFO), also a recent heuristic optimization method, is based on the concept of flashing behaviour of lightingbugs. Both the optimization methods are applied to obtain the solution of the Optimal Power Flow (OPF) with continuous and discrete control variables for quadratic generator output cost functions. The continuous control variables are generating unit active power outputs and generator bus voltage magnitudes, while the discrete ones are transformer- tap settings and switchable shunt devices. A number of functional constraints such as load bus voltage magnitudes, line flows and reactive power capabilities are included as quadratic penalties in the optimization function. A comparative simulation results for Ward -Hale 6 bus system with seven control variables and IEEE 30 bus system with twenty-three control variables are presented. F : total operating cost,NB: number of buses, NG: number of generator buses, NT: number of Transformers, NL: number of lines (branches),NSH: number of switchable shunts, NPQ: number of load buses NTR: number of transformers, ௜ : active power injection at bus i, ௜ : reactive power at bus i,NP: population size (number of fireflies in FFO/number of Big- Bangs BB-BC)NC: number of control variables (co-ordinates of fireflies FF0/dispersions in BB-BC)

In engineering practice, a problem is quite often faced in which the number of unknown parameters exceeds the number of conditions or requirements or, otherwise, there are too many requirements for too few parameters to design. Such under- or over-defined tasks are sometimes not possible to solve using a direct approach. The number of solutions to such problems is multiple, and it is most rational to search for the optimal one by numerical methods since the more unknown design parameters there are to be designed, the more potential solutions there are. This article discusses a way to find an optimal solution to such an underdetermined problem by heuristic optimization methods on the basis of the example of designing a composite wing skin of an aircraft. Several heuristic approaches, specifically gradient descent and Tabu search, are studied to solve the design problem and to locate an optimal solution. They are also compared to a conventional direct approach. The examined composite lamina is optimized by the target function of minimum weight with the constraints of strength and buckling failure criteria. In most of the observed cases, the heuristic method designed structures which were considerably better than the structures that were obtained by conventional direct approaches in terms of the weight to load ratio.

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

A spatially explicit agent-based simulation model of a reservoir host of cutaneous leishmaniasis, Rhombomys opimus

This paper addresses a multi-objective approach for simultaneous dynamic expansion planning of conventional sub-transmission grid and Regional Virtual Power Plant (RVPP). In such a feed-in tariff electricity market, it will assist RVVP’s stakeholders in deciding whether or not they should invest in new equipment’s installation. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm as a heuristic optimization method is proposed to eliminate the conventional centralized planning, which has led to competition between Regional Electric Company (REC) and RVPP for power delivery in sub-transmission system. Two objective functions are considered for simultaneous expansion planning of these two systems. The first one takes the minimum cost of the REC, as the sub-transmission grid operator, into account while the other one considers profit maximization for RVPP. To achieve the goals, MOPSO algorithm is employed to find the best expansion of REC and best location and capacity of RVPP’s resources. Having solved the proposed multi-objective optimization problem, a Pareto front is determined to show the trade-off between REC and RVPPs' contributions in joint optimal expansion planning of conventional sub-transmission grid and the RVPP. To demonstrate the applicability and effectiveness of the proposed approach, a realistic sub-transmission system in Guilan Province, Iran is used as a test system, and the results are evaluated accordingly.

In this paper, the mathematical models of two water distribution network systems, namely a Serial Network and a Branched Network have been considered as nonlinear optimization problems and Particle Swarm Optimization (PSO) method has been used to obtain the global optimal solution in each case. The PSO is a heuristic optimization method. Herein, the PSO has been used to determine the minimum cost of the water distribution network system for a Serial Network and Branched Network. The numerical results indicate the robustness of the PSO method. It suggests that PSO method can be effectively used for any generalized water distribution network system.

SOM-ELM—Self-Organized Clustering using ELM

Interval dynamic response analysis of vehicle-bridge interaction system with uncertainty

The balance of exploration and exploitation is the essence of any successful meta-heuristic. The Multi-armed Bandit Problem represents a simple form of this general dilemma. This paper describes two heuristic optimization methods that use a simple yet efficient allocation strategy for the bandit problem called UCB1 to control the optimization process. The algorithms are applied to the well known Single Machine Total Weighted Tardiness Problem and the results compared to the results of other successful meta-heuristics for this scheduling problem.

The system presented bridges the gap between three different research areas: discrete event simulation, heuristic optimization methods and distributed systems technology. Its goal is to provide a framework which supports an efficient implementation of simulation optimization projects, including heuristic optimum seeking procedures and parallel execution of experiments. It is written completely in Java and only uses components that are publicly available, including software libraries from academic institutions or the Java API from Sun Microsystems. The framework is well applicable for education and further research.

Stochastische Prozesse sind zur Abbildung von Produktionssystemen gut geeignet. Mit ihnen konnen Bewertungsgrosen ermittelt werden. Es entsteht haufig ein Problem, da es sich nur um evaluative Verfahren handelt, in den meisten Fallen aber geeignete Losungen fur ein Planungsproblem gesucht werden. Ein Beispiel hierfur ist das Designproblem bei flexiblen Fertigungssystemen (FFS). Es werden unter zuvor festgelegten Kriterien und Nebenbedingungen die besten Maschinenkonfigurationen gesucht. Werden zur Abbildung weiterhin nur stochastische Prozesse verwendet, so konnen zur Optimierung nur heuristische Optimierungsverfahren eingesetzt werden. Anhand des betrachteten Planungsproblems wird gezeigt, wie ein stochastischer Prozes in eine Optimierungsheuristik integriert werden kann. Gearbeitet wird hierbei mit Simulated Annealing. Durch diese Verbindung konnen die optimale und die nachstbesten Losungen fur das Designproblem ermittelt werden.

Point label placement problem is the problem of labeling points in graphical display systems such as mapbased ones in order to minimize conflicts of labels and thus to maximize legibility. Algorithmic complexity of the general problem is proven to be NP-hard. Addressing this problem is becoming more important in dynamic and real-time environments, where both display properties or point elements are subject to change, since those systems have potential use widely and in large scale. In this study, heuristic optimization methods are proposed to facilitate fast and goodquality labeling in such dynamic environments. The methods are two-phased and comprised of several combinations of greedy construction and local search methods. Performances of the methods are examined in terms of solution quality and run time. The eliminative, two-criteria method outperform other methods in this study and the methods proposed in the literature.

Prediction of stock market is a challenging task that has attracted researchers in various fields including the computational intelligence and finance. Since stock market data sets are intrinsically large, nonlinear and time-varying, it is extremely difficult to design models for forecasting the future directions with an acceptable accuracy. In this paper, an integrative and intelligent machine learning framework is proposed through combining cloud computing, machine learning and heuristic optimization. Essentially, the Support Vector Machine (SVM) method is extended with the Grid Search (GS) or Chemical Reaction Optimization (CRO) as a heuristic optimization method together with Principal Component Analysis (PCA) and Feature Noise Filter (FNF) to construct quantitative investment forecasting models for efficient executions on cloud computing platforms. To demonstrate the effectiveness of the proposed framework, the Hang Seng Index and some major stocks listed on the Hong Kong Exchange are predicted using the constructed models on a daily basis. The empirical results clearly indicate that the proposed integrative approach is promising and gives impressive performance in terms of the prediction accuracy.