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Hardware-software (HW-SW) partitioning plays a vital role in design phase of embedded system. The partitioning is a process to map each computation task in an application to either software or hardware. In general, hardware run faster compared to software, but with significant cost and resources utilization. Thus, current embedded system often incorporates a mix of hardware and software component...
We introduce goSAT, a fast and publicly available SMT solver for the theory of floating-point arithmetic. We build on the recently proposed XSat solver [1] which casts the satisfiability problem to a corresponding global optimization problem. Compared to XSat, goSAT is an integrated tool combining JIT compilation of SMT formulas and NLopt, a feature-rich mathematical optimization backend. We evaluate...
In this paper, the scatter search is modified and combined with the variable-sample technique to deal with the simulation-based optimization problem. First, a new design of scatter search is proposed to deal with the deterministic global optimization problem. Then, the variable-sample technique is combined with the modified scatter search method in order to compose a new global search method that...
In air traffic management, optimization is often restricted to local areas, e.g., to the vicinity of airports. Procedures around these areas stay unchanged, and effects from optimizations concerning ecological efficiency are not considered adequately. Investigating new concepts, this typically results in local gain of efficiency without proving the global benefit. The project World Wide Air Traffic...
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness...
The location of the micro fractures developed during hydraulic stimulation in unconventional reservoirs can be posed as a non-linear optimization problem. We propose a Differential Evolution algorithm (DE) to estimate the position of the microseismic events. A geometry model representing the subsurface is used to construct the cost function to be optimized. We examine two kind of tests, one using...
The large-scale electric vehicles access to the distribution network, the randomness and decentralization of the distribution network management put forward new requirements for the transmission line load capacity also launched a challenge. In order to make the distribution network of electric vehicles take safe, economic and stable operation, according to the characteristics of electric vehicles,...
In order to solve the problem of great memory usage when merging pattern sets into Deterministic Finite Automaton (DFA), an important method is that it divides n regular expressions into m groups in reasonable ways. Through combining minimum interactive rate grouping strategy with the advantage of global search abilities of optimization of artificial fish school algorithm (ASFA), one grouping algorithm...
In this paper, a novel firefly algorithm (FA) is presented to reduce the dependency on parameters. The new FA algorithm is called dynamic step factor based FA (DSFFA), in which the step factor is not fixed and it is dynamically updated during the evolution. Experimental study on several classical benchmark functions show that DSFFA is superior to the basic FA and three other FAs.
Differential evolution (DE) is an efficient and robust evolutionary algorithm, which has been widely and successfully applied to solve global optimization problems. Although many methods have been developed based on the population topology to improve the performance of DE, the effects of population topology interacted with the functions being optimized are not considered in most of the algorithm designs...
GPS is used for outdoor localization in a large variety of applications. Current receivers consume too much power for energy-constrained situations like continuous location tracking on small wearable devices. Mainly, this is due to the large amount of GPS signal that has to be decoded to compute the first position fix. While Coarse-Time Navigation (CTN) can reduce the necessary signal to a few milliseconds,...
Currently, railway systems are electrified to meet the rising demand for faster speed, more stable operation and more passenger traffic. Although special electrical traction systems are utilized to provide power for railway load consumption, major power quality challenges are present. This paper addresses the Volt/VAR control problem in railway power systems (RPS) considering distributed generations...
In this study we introduce a new approach to train a fully recurrent artificial neural network by solving a constraint satisfaction problem using the quotient gradient method. The quotient gradient method is a trajectory based methodology for global optimization that does not suffer from the problem of local minima encountered in Newton based methods. Simulation results show that the network trained...
This paper presents a new interval optimization algorithm (ESIA) combining interval algorithm with evolution strategy in bionics., to improve the search efficiency and make the accelerated tool constructed easier comparing with the traditional interval algorithm (IA), hence it can be applied to high dimensional optimization problems better. The ESIA employed the evaluation strategy to construct accelerated...
In this paper, we introduce a computational approach for solving optimal driving strategies for trains in which the controllers action are confined to select from discrete modes. Since the controllers operate in succession, in order to avoid the undesirable Zeno phenomenon, we assume that the only one controller can be selected at a time and each active controller duration requires a minimum non-negligible...
The aim of the paper is to present a general approach to the identification of nonlinear stochastic systems based on information-theoretic measures of dependence. In the paper, an identification problem statement using an information-theoretic criterion under rather general conditions is proposed. It is based on a parameterized description of the model of a system under study combined with a corresponding...
We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does...
In this paper, a novel particle swarm optimizer is developed by introducing projection operators described by projection matrices into the algorithm. Under the projection operators, the particles will oscillate along the directions determined by the projection operators to enhance global explorations. At the same time, the particles explore locally the optimal solutions when they are close to the...
A performance and time efficient 2.1D sketch extraction from a given monocular image is proposed in a global optimization framework that exploits the divided rectangles (DIRECTs) but otherwise extracted by heuristic global optimization methods, like genetic algorithms, particle swarm evolution algorithms, and simulated annealing. An appeal of these algorithms is that they are guaranteed to yield the...
This paper looks into efficient implementation of one dimensional low pass Finite Impulse Response filters using certain commonly used and state-of-the-art optimization techniques. Methods like Parks-McClellan (PM) equiripple design, Quantum-behaved Particle Swarm Optimization (QPSO) and Cuckoo Search Algorithm (CSA) with Levy Flight are employed and overall performance is further improved by hybridization...
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