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The simulation of parallel heterogeneous architectures such as multi-cores and GPUs sets new challenges in the programming language/framework domain. Applications for simulators need to be expressed in a way that can be easily adapted for the specific architectures, effectively tuned for on each of them while preventing from introducing biases due to non-uniform hand-made optimizations. The most common...
Glowworm Swarm Optimization Algorithm (GSO) is one of new swarm intelligence optimization algorithms in recent years. Its main idea comes from the cooperative behavior source among individuals during the process of courtship and foraging. In this paper, in order to improve convergence speed in the late iteration, avoid the algorithm falling into local optimum, and reduce isolated nodes, the Adaptive...
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments...
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to...
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering...
Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work, we propose a novel graph-based formulation that links and clusters person hypotheses...
An Interval numbers based particle swarm optimization algorithm (IPSO) is presented in the paper. In this work, interval numbers is used to alter real number for numerical function optimization problems. The results produced by IPSO and PSO have been compared. The results showed that IPSO outperforms PSO.
The execution of HPC applications in multicore environments can occasionally use the resources in an inefficient way. There are idle times during the application execution that can be caused by synchronization or message passing collisions. We define this idle time as an application inefficiency and may be caused by the message passing collisions at different types of interconnections in the compute...
Competitive swarm optimizer (CSO) has shown promising results for solving large scale global optimization problems proposed recently. However, CSO shows insufficient exploitation of the population. In this paper, a competitive swarm optimizer integrated with Cauchy and Gaussian mutation (CGCSO) is proposed for large scale optimization. The new algorithm does not only update the losers' positions with...
Fruit fly optimization algorithm (FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm (SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value...
A new hybridized algorithm is developed to solve the combined heat and power economic dispatch (CHPED) problem. Due to the non-convexity of the CHPED problem, the global optimum is difficult to achieve. The presented approach combines the algorithms of harmony search (HS) and Nelder-Mead (NM), and the hybridized algorithm is called the NM-HS algorithm. The hybridized NM-HS algorithm improves the efficiency...
This paper describes an approach to generate a dense disparity map between the stereo pairs using the multilayer superpixel with the different size. We assume the pixels within superpixel belong to the same 3D surface. The pixel-wise matching costs are computed with census and gradient features. Then, the input stereo images are segmented into M layers superpixel. SLIC algorithm is used to obtain...
Object detection aims to identify instances of semantic objects of a certain class in images or videos. The success of state-of-the-art approaches is attributed to the significant progress of object proposal and convolutional neural networks (CNNs). Most promising detectors involve multi-task learning with an optimization objective of softmax loss and regression loss. The first is for multi-class...
Due to the simplicity of its implementation and the impressive performance, Extreme Learning Machine (ELM) has been widely used in applications of machine learning. However, there are two potential problems in ELM: 1) lack of an efficient method for minimizing error; 2) consideration of little inherent structural information about correlations among output components. To overcome those problems, this...
Grey wolf optimizer (GWO) is an efficient optimization approach in the generation of swarm intelligence based techniques. GWO algorithm relies on the leadership quality and hunting mechanism shown by grey wolves. Half of the iteration in GWO are dedicated to exploration and the rest half are used for exploitation. This article presents a modified GWO approach, known as Howling mechanism based grey...
Moth Swarm Algorithm (MSA) is one of the newest developed nature-inspired heuristics for optimization problem. Nevertheless MSA has a drawback which is slow convergence. Chaos is incorporated into MSA to eliminate this drawback. In this paper, ten chaotic maps have been embedded into MSA to find the best numbers of prospectors for increase the exploitation of the best promising solutions. The proposed...
This work addresses the problem of optimizing graph-based programs for multicore processors. We use three graph benchmarks and three input data sets to characterize the importance of properly partitioning graphs among cores at multiple levels of the cache hierarchy. We also exhaustively explore a large design space comprised of different parallelization schemes and graph partitionings via detailed...
This paper proposed a new metaheuristic algorithm, Hybrid Multi-swarm with Harmony Search algorithm which combines two famous metaheuristics, particle swarm optimization (PSO) and Harmony Search algorithm (HS). The main advantage of PSO is its convergence speed while its main drawback is trapping in local optimum problem. To improve PSO performance, this research use HS to increase PSO diversity and...
Particle Swarm Optimization (PSO) is a powerful algorithm that can search a solution for a function which contains a large number of peaks and valleys. However, PSO might encounter a difficulty when the function gets more complex or the number of attributes (dimensions) grows larger. This paper proposes a modification of PSO by using the incremental attribute strategy along with the centroid of particle's...
The performance of an advanced Extremum Seeking Control (ESC) scheme will be evaluated in this paper based on multimodal benchmark pattern proposed in the literature to test the classical ESC schemes. The performance indicators which are usually used in real-time optimization (RTO) strategy for Fuel Cell (FC) systems or photovoltaic (PV) systems will be also used here. The advanced ESC scheme will...
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