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How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with...
We present a novel computational method for Multiple Sequence Alignment (MSA), a fundamental problem in computational biology. In contrast to other known approaches, our method searches for an optimal alignment — structurally and evolutionarily — by inserting or deleting gaps from a set of initial candidates in an efficient manner. Our method called a Universal Partitioning Search (UPS) approach for...
We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
Optimization of biodiesel production from non-edible Carica papaya seed oil was experimentally investigated in this study. Biodiesel production process parameters such as methanol: oil molar ratio, catalyst type and their concentrations, reaction temperature and reaction time were evaluated. It was found that the optimum process parameters for transesterification of the Carica papaya seed oil at an...
The success of deep neural networks usually relies on a large number of labeled training samples, which unfortunately are not easy to obtain in practice. Unsupervised domain adaptation focuses on the problem where there is no labeled data in the target domain. In this paper, we propose a novel deep unsupervised domain adaptation method that learns transferable features. Different from most existing...
The existence of non-line-of-sight (NLOS) errors will considerably degrade the localization accuracy. Therefore, the NLOS node localization is investigated. In this article, we propose a NLOS node localization algorithm that utilizes the firefly algorithm. The objective function is established by the probability of propagation of NLOS and LOS according to the approximate maximum likelihood method...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
With the steady increase of offered cloud storage services, they became a popular alternative to local storage systems. Beside several benefits, the usage of cloud storage services can offer, they have also some downsides like potential vendor lock-in or unavailability. Different pricing models, storage technologies and changing storage requirements are further complicating the selection of the best...
With the application of modern communication, control and measurement technology based on microelectronics and computer, a large number of microelectronic devices have been applied in the power generation, transmission and production links of power plants. In order to ensure the safe operation of all kinds of electronic equipment, it is necessary to install surge protector (SPD). In this paper, MATLAB...
In this study, the effect of distributions of solution candidates on the problem space in the meta-heuristic search process and the performance of algorithms has been investigated. For this purpose, solution candidates have been created with random and gauss (normal) distributions. Search performance is measured separately for both types of distribution of algorithms. The performances of the algorithms...
Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in...
Smart objects (SOs) have been utilized widely to transform the physical environment around us to a digital world using the Internet of things (IoT) vision. Integrating a huge number of these devices into the Internet presents a significant necessity for an efficient discovery mechanism with high capability of an autonomous configuration and detection for theses devices and their provided services...
This paper introduces compact music-inspired computing. We propose a music-inspired optimization technique with minimal computational cost. The aim is to reduce the memory storage capacity required by the classical harmony search algorithm (HSA) while improving their performance. Therefore, we propose three compact harmony search algorithms. The main idea is to represent the harmonies stored in the...
Implementing a successful warehouse management system from scratch contains many challenges of different nature. The most important aspect is the financial status of the company that is implementing the system. How many resources is the company willing to spend is always a difficult dilemma. The second important issue is the actual usefulness of the developed solution. If the system cannot be implemented...
Compact evolutionary algorithms (cEAs) are optimization algorithms that require minimal computational cost. They do not require the storage of the population but they represent it by a distribution function. In all known cEAs, normal probability of density function (N-PDF) is used. In this paper, in order to improve the performance of cEAs and to reduce their complicity, we propose a more simple distribution...
We propose a new algorithm for portfolio optimization based on statistical arbitrage, that uses a multi-criteria decision making approach to obtain the most preferred assets. A preference flow graph of financial assets is constructed at each time step, with the aid of statistical arbitrage algorithm that describes preferences among the assets. Then, the individual preferences for each asset are obtained...
Dynamic programming languages are becoming increasingly popular, and this motivates the need for just-in-time (JIT) compilation to close the productivity/performance gap. Unfortunately, developing custom JIT-optimizing virtual machines (VMs) requires significant effort. Recent work has shown the promiseofmeta-JITframeworks, which abstract the language definition from the VM internals. Meta-JITs can...
Exploration and exploitation are two strategies used to search the problem space in Evolutionary Algorithms (EAs). To significantly increase the performance of these optimization techniques in terms of the solution optimality is to strike the right balance between exploration and exploitation. Firefly is one of the most favored EAs. In this study, we introduce an entire fuzzy system to tune dynamically...
Multithread programming tools become popular for exploitation of high performance processing with the dissemination of multicore processors. In this context, it is also popular to exploit compiler optimization to improve the performance at execution time. In this work, we evaluate the performance achieved by the use of flags -O1, -O2, and -O3 of two C compilers (GCC and ICC) associated with five different...
Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative...
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