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Quadratic assignment problems one of combinatorial optimization problems that pays to number of facilities to number of places. The objective is to minimize the cost. QAP is one of the degree of complexity of hard problems deterministic algorithms are able to solve smaller sample problem. Fast local search used to solve qap. but despite the wider space exploration. There are not certain views for...
In this paper, a cooperative device to device (D2D) communication system is studied while two transmitting devices communicate with one destination device in the presence of a passive eavesdropper. The second transmitting device cooperates with the first transmitting device and hence the transmission takes place is two phases. In the first phase, the first device transmits its signal to the second...
Many methods have been introduced as economic load dispatchers. However, all these methods depend on the optimization field, where most of these studies mainly focus on how to strengthen the optimization algorithms themselves. This means the mystery key that segregate between the good and bad ELD solvers is the optimization algorithm itself. However, there is a practical fact known in many real power...
This paper investigates the impact of demand response (DR) management on improving the social welfare of remote communities by integrating renewable energy (RE) resources into their diesel generator-based isolated microgrids. A multiple-year planning optimization problem is developed to determine the optimal combination of RE resources and energy storage system (ESS) while considering demand response...
The coordination problem of directional overcurrent relays (DOCRs) is considered as a highly constrained, nonlinear, and non-convex optimization problem. The summation of the operating times of all DOCRs, when they act as primary protective devices, is taken as the objective function that needs to be minimized. This stiff problem is mostly optimized based on IEC standard inverse time-current characteristic...
Multipartite table methods offer a high speed, low area implementation of commonly used functions for up to 24 bits of accuracy. Currently the parameters which dictate the configuration of these tables are chosen using a worst-case rounding approximation scheme which often generates sub-optimal results. This paper will show that it is possible to perform a full exhaustive search to find the minimum...
Sparsity in the weights of deep convolutional networks presents a tremendous opportunity to reduce computational requirements. In order to optimize flow of traffic systems, any viable solution must be able to operate at real-time. Existing computation frameworks do not yet realize the full potential speedup afforded by sparse neural networks. Meanwhile, the power consumption for a GPU is too great...
Redesigning the large and complex software systems requires very high costs time and money. Hence, automatic management and automatic adaptability with minimal human intervention is unavoidable. Self-management is the greatest level of self-adaptive which include all self-adaptive details. Scalability and dynamic analysis support in the face of change are the main challenges of self-management. Due...
SimRank is an effective structural similarity measurement between two vertices in a graph, which can be used in many applications like recommender systems. Although progresses have been achieved, existing methods still face challenges to handle large graphs. Besides huge index construction and maintenance cost, the existing methods require considerable search space and time overheads in the online...
Photovoltaic power systems lose significant amount of energy due to partial shading which occurs when a part of a PV system is shaded while the rest is fully illuminated. These losses appear in the form of mismatch power losses. Minimizing these losses is fortunately possible through reconfiguring the connections of PV modules in a PV system, as reported recently in the literature. However, the available...
We consider a class of mixed integer programs where the problem is convex except for a vector of discrete variables. Two methods based on the Alternating Direction Method of Multipliers (ADMM) are presented. The first, which has appeared in the recent literature, duplicates the discrete variable, with one copy allowed to vary continuously. This results in a simple projection, or rounding, to determine...
A new direct method for solving general systems of linear algebraic equations originated by the author is applied to the repealed solution of a given system when changes of some rows occur in the system matrix. Such repeated computations are needed in numerous practical problems, e.g. in various imaging devices (electromagnetic, acoustic, etc.) or in linear optimization. The solution method is based...
In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy...
A new computing using Ising model that effectively solves combinatorial optimization problems is proposed. The computing maps problems to an Ising model, a model to express the behavior of magnetic spins, and solves the problems by its own convergence property. We fabricated a prototype computing chip and confirmed the power efficiency of the chip is 1800-times higher than that of the conventional...
Oata Grid provides transparent access to grid user. The grid data stored across distributed storage resources whilst fulfilling the Confidentiality, Integrity, Availability (C,I,A). The stored data is replicated in data grid to increase the availability. Replica Manager uses replica replacement algorithm to decide which replica to be replaced for the new replica when there is not enough space for...
This work addresses the problem of developing a synthesis algorithm for clock spine networks, which is able to systematically explore the clock resources and clock variation tolerance. The idea is to transform the problem of allocating and placing clock spines on a plane into a slicing floorplan optimization problem, in which every candidate of clock spine network structures is uniquely expressed...
We consider networks the nodes of which are interconnected via directed edges, each able to admit a flow within a certain interval, with nonnegative end points that correspond to lower and upper flow limits. The paper proposes and analyzes a distributed algorithm for obtaining admissable and balanced flows, i.e., flows that are within the given intervals at each edge and are balanced (the total in-flow...
This paper dealt with the optimal distribution network planning (DNP) under loads growth condition where the Particle Swarm Optimization with Constriction Factor (PSO-CF) was considered. This paper aimed at ensuring that the consumers' load demands which always increase with time can be satisfied in an optimum way by the distribution network reconfiguration (DNR). The planning problem was addressed...
This paper proposes an approach of recommending micro-learning path based on improved ant colony optimization algorithm. Micro-learning is a new learning style, which can be used to support learning in short time because of its micro-learning units. Each micro-learning unit consists of a small knowledge unit that can be learned at fragmented time. Meanwhile, micro-learning is more flexible than other...
In this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by using a different class ordering. We investigate criteria including accuracy, ensemble size, and the role of starting point in the search. Fusion is done by averaging. Using 22 data...
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