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Real-world datasets consist of data representations (views) from different sources which often provide information complementary to each other. Multi-view learning algorithms aim at exploiting the complementary information present in different views for clustering and classification tasks. Several multi-view clustering methods that aim at partitioning objects into clusters based on multiple representations...
This paper presents a new approach to hierarchically synthesize analog circuits. In general, behavioral models are preferred at intermediate levels to reduce total synthesis time. However, there are problems associated with the usage of behavioral models such as significantly sacrificing the accuracy and costly preparation time for model generation. Therefore, a model-free approach is proposed, in...
While kernel methods using a single Gaussian kernel have proven to be very successful for nonlinear classification, in case of learning problems with a more complex underlying structure it is often desirable to use a linear combination of kernels with different widths. To address this issue, this paper presents a classification algorithm based on a jointly convex constrained optimization formulation...
Intelligent algorithms have been applied to solving conditional nonlinear optimal perturbation (CNOP), which plays an important role in the study of weather and climate predictability. Single particle intelligent optimization algorithms can get similar CNOP to adjoint method, and show higher time efficiency in solving CNOP. However, swarm intelligent optimization algorithms can only get similar CNOP,...
These days, most of the real-world problems have become multi-criteria in nature and the demand for an effective multi-objective optimization algorithm has been significantly increased. This paper presents a new Multi-Objective Self-Regulating Particle Swarm Optimization (MOSRPSO) algorithm whereby the SRPSO algorithm originally developed for single objective problems has been modified to tackle with...
In this paper, a new meta-heuristic optimization algorithm called Grey Wolf Optimizer (GWO) is applied to offshore crane design. An offshore crane is a pedestal-mounted elevating and rotating lifting device used to transfer materials or personnel to or from marine vessels, barges and structures whereby the load can be moved horizontally in one or more directions and vertically. Designing and building...
In cognitive radio technology, constant changes in the environment like change in background noise, movements of the users or transmitters, interferences, etc. require the spectrum sensing parameters like detection threshold to be changed. Else, errors in spectrum sensing arises which either causes interference between transmission from primary and secondary users, or the secondary user misses an...
Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy. Although a number of structured sparsity models have been explored, such as trees, groups, clusters, and paths, connected subgraphs have been rarely explored in the current literature. One of...
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on...
In this research, ant colony algorithm for proportional plus integral (PI) optimal tuning to design load frequency controllers is introduced. A mathematical model of a network with DFIG based wind turbine in two area frequency control is developed. Time domain simulations of the various interconnected power system areas subjected to major disturbances are investigated. A method to enhance the system...
As the foremost step for designing a cellular manufacturing system, cell formation is a hard optimization problem. It is necessary and significant to develop methods to find near-optimal solutions in a reasonable time. In this paper, the first hybrid discrete Cuckoo Search (HDCS) algorithm is designed to address the cell formation problem. Alternative process routings and operation sequence of parts...
Hybrid methods of fuzzy clustering and particle swarm optimization (PSO) are important techniques for image segmentation. The spatial credibilistic clustering (SCC) shows better performance than traditional fuzzy clustering, because of the “typicality” represented by credibility memberships degree is much more accurate than the “sharing” represented by probability membership degree to characterize...
Solving a system of linear algebraic equations is a fundamental problem, especially when there is a large number of design variables. To this purpose, we consider a collaborative framework with multiple interconnected agents that are distributed among different nodes of a network, and each agent maintains a state vector to compute the solution. Under local interactions, we propose an iterative algorithm...
Given an initial marking and final marking for a Petri net model, an optimal firing sequence problem is defined as the problem to find an optimal transition firing sequence to minimize the objective function. For the purpose of analysis of general integer programming problems, we propose a Petri net representation and reachability analysis of integer programming problems. In the proposed method, an...
Energy extraction from the solar irradiance by the use of solar cell Module is very important in the field of renewable energy. The Electrical properties derived from the nonlinear Current-voltage (I-V) curve of the solar cell module play a vital role in the exploration of device performance and overall efficiency. In this paper, two recently developed heuristic algorithm Cuckoo search optimization...
Thresholding is a popular image segmentation method that converts gray-level image into binary image. The problem of thresholding has been quite extensively studied for many years in order to get an optimum threshold value. The multi-level thresholding becomes very computationally challenges. In this paper, a novel multilevel thresholding method based on particle swarm optimization (PSO) algorithm...
The Fish School Search (FSS) algorithm has a very useful engaging mechanism to avoid the simple reactive agents of being trapped into local minima. In 2014, a binary version of the FSS was proposed and applied for feature selection. In this paper we propose some improvements in the Binary Fish School Search algorithm (BFSS). We show that the BFSS with these modifications outperformed the original...
The classical nonlinear guidance algorithm for a fixed-wing flying robot has the shortcoming of the unchangeable guidance length. To improve the algorithm, a novel guidance method is proposed based on the guidance point optimized online. The kinetic equations and the classical nonlinear guidance algorithm are introduced. The effects of the guidance length to the tracking performance are analyzed....
Discrete optimization models and methods, in particular, the apparatus of integer programming, are often used for solving and analysis of many decision-making problems in computers design, productions planning and management, information technologies, engineering. In this paper we investigate some cutting plane algorithms for solving the set packing problem, which has a lot of applications in the...
In this paper we present a heuristic algorithm for the The Band Collocation Problem (BCP) which may have some applications in the field of telecommunication. First, we give the definition the BCP. Second, we explain how we create the problem instances with known optimal solutions as a library. Third, we propose the heuristic algorithm. Then, we analysis and interpret the results of the proposed algorithm...
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