Software-Defined Networking (SDN) has gained tremendous attention in the past few years for its advantages over network controllability. Nonetheless, the deployment of SDN in legacy network is likely to span multi-per...
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Software-Defined Networking (SDN) has gained tremendous attention in the past few years for its advantages over network controllability. Nonetheless, the deployment of SDN in legacy network is likely to span multi-periods over months or years for budget consideration. Network operators, especially for a network consists of thousand or more nodes, are eager to understand how legacy networks can be deploying gradually towards SDN with variety of constraints. The reliability of network should be the utmost concern during SDN deployment in legacy network. Primarily, node exclusion is essential in SDN deployment process as the exclusion of critical node during the process greatly improves network stability and reliability. Therefore, we propose a heuristic algorithm for multi-periods SDN node migration in legacy network with respect to node exclusion and budget. We first formulate the aforementioned problem in Integer Linear Programming (ILP) model, and we evaluate the proposed ILP model and our proposed heuristic algorithm with regard to solution quality and processing time. The results show that our proposed algorithm maintains its overall correctness with O(N-2) polynomial time complexity. In addition to migration sequence computation, the results of our proposed algorithm reveal the impact of each node in SDN deployment as Key Node, Critical Node and Diminishing Return Analysis. These information offers better decision for network operator during hybrid SDN deployment. Ultimately, the results of our proposed algorithm offers valuable insight in which significantly reduce up to 83% of investment while reaping the most performance gain out of SDN deployment in legacy network.
In this letter, we propose a heuristic algorithm for the point of presence (POP) design problem in Internet Protocol (IP) networks, where a POP is a node composed of several interconnected co-located routers. This pro...
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In this letter, we propose a heuristic algorithm for the point of presence (POP) design problem in Internet Protocol (IP) networks, where a POP is a node composed of several interconnected co-located routers. This problem consists of selecting the number of routers and their types, selecting the interface card types, connecting the access and the backbone links to the ports and selecting the link types between the co-located routers. A systematic set of experiments is designed to assess the performance of the proposed heuristic algorithm. The results show that quasioptimal solutions can be found with the proposed heuristic.
Modern computer networks consist of backbone networks and local access networks. Typical local area networks (LANs) can be defined as end-user-nodes of the local access networks. The problem is composed of finding the...
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Modern computer networks consist of backbone networks and local access networks. Typical local area networks (LANs) can be defined as end-user-nodes of the local access networks. The problem is composed of finding the best way to link nodes to a central node site and, in graph-theoretical terms, it is to determine a minimal spanning tree with a capacity constraint (CMST). In this paper, A heuristic algorithm with two phases is presented. Computational experience confirms that our algorithm improves the solutions achieved by the existing algorithm and requires the relatively short running time. The algorithm can be applied to design of local area network in an organisation or centralised network.
A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA ...
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A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA is based on a leadership strategy. Using the leadership strategy as reference, we have designed the proposed algorithm by simulating the preying actions of dolphin herds. Several intelligent behaviors, such as "producing leaders," "group gathering," "information sharing," and "rounding up prey," are abstracted by LDHA. The proposed algorithm is tested on 15 typical complex function optimization problems. The testing results reveal that compared with the particle swarm optimization and the genetic algorithms, LDHA has relatively high optimization accuracy and capability for complex functions. Further, it is almost unaffected by the inimicality, multimodality, or dimensions of functions in the function optimization section, which implies better convergence. In addition, ultra-high-dimensional function optimization capabilities of this algorithm were tested using the IEEE CEC 2013 global optimization benchmark. Unfortunately, the proposed optimization algorithm has a limitation in that it is not suitable for ultra-high-dimensional functions.
This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neu...
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This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm's execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies.
In this paper a 0-1 linear programming model and a solution heuristic algorithm are developed in order to solve the so-called Master Surgical Schedule Problem (MSSP). Given a hospital department made up of different s...
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In this paper a 0-1 linear programming model and a solution heuristic algorithm are developed in order to solve the so-called Master Surgical Schedule Problem (MSSP). Given a hospital department made up of different surgical units (i.e. wards) sharing a given number of Operating Rooms (ORs), the problem herein addressed is determining the assignment among wards and ORs during a given planning horizon, together with the subset of patients to be operated on during each day. Different resource constraints related to operating block time length, maximum OR overtime allowable by collective labour agreement and legislation, patient length of stay (LOS), available OR equipment, number of surgeons, number of stay and ICU beds, are considered. Firstly, a 0-1 linear programming model intended to minimise a cost function based upon a priority score, that takes into proper account both the waiting time and the urgency status of each patient, is developed. Successively, an heuristic algorithm that enables us to embody some pre-assignment rules to solve this NP-hard combinatorial optimisation problem, is presented. In particular, we force the assignment of each patient to a subset of days depending on his/her expected length of stay in order to allow closing some stay areas during the weekend and hence reducing overall hospitalisation cost of the department. The results of an extensive computational experimentation aimed at showing the algorithm efficiency in terms of computational time and solution effectiveness are given and analysed.
作者：
Vasylchuk, Y. V.Deutsch, C., VUniv Alberta
CCG Dept Civil & Environm Engn Markin CNRL Nat Resources Engn Facil 5 070 Edmonton AB T6G 2W2 Canada Univ Alberta
CCG Dept Civil & Environm Engn Donadeo Innovat Ctr Engn 6 232 Edmonton AB T6G 1H9 Canada
This paper presents an algorithm for optimizing the classification of surface mine material subject to excavating constraints. High-resolution expected profit models are input and optimized to classification maps subj...
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This paper presents an algorithm for optimizing the classification of surface mine material subject to excavating constraints. High-resolution expected profit models are input and optimized to classification maps subject to site-specific rectangular excavating constraints. This optimization problem defies traditional closed-form analytical solutions;a practical heuristic algorithm has been developed to quickly determine the optimal final destination for the material subject to realistic constraints. The optimization is fast and generates results that achieve up to 98-99% of the total expected profit achieved with free selection. This algorithm provides a fast viable option for practical application in short-term grade control and in managing multiple realizations in long-term resource estimation.
Increasingly popular use of verification methods based on specific characteristics of people like eyeball, fingerprint or voice makes inventing more accurate and irrefutable methods of that urgent. In this work we pre...
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Increasingly popular use of verification methods based on specific characteristics of people like eyeball, fingerprint or voice makes inventing more accurate and irrefutable methods of that urgent. In this work we present voice verification based on Gabor transformation. Proposed approach involves creation of spectrogram, which serves as a habitat for the population in selected heuristic algorithm. The use of heuristic allows for feature extraction to enable identity verification using classical neural network. The results of the research are presented and discussed to show efficiency of the proposed methodology.
Computational models and methods for predicting secondary structure of RNA sequence are in demand. Based on MFE principle and the relative stability of the n-stems in RNA molecules, Minimum Free Energy method is adopt...
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Computational models and methods for predicting secondary structure of RNA sequence are in demand. Based on MFE principle and the relative stability of the n-stems in RNA molecules, Minimum Free Energy method is adopted widely to predict RNA secondary structure. An improved heuristic algorithm is presented to predict RNA pseudoknotted structure, and it can compute arbitrary pseudoknots. The algorithm requires O(n(3)) time and O(n(2)) space. This algorithm not only reduces the time complexity to O(n(3)), but also widens the maximum length of the RNA sequence. The preliminary experimental test on the RNA families in PseudoBase shows that the algorithm is more effective than the existing algorithms.
The circular packing problem with equilibrium constraints is an optimization problem about simplified satellite module layout design.A heuristic algorithm based on tabu search is put forward for solving this *** algor...
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The circular packing problem with equilibrium constraints is an optimization problem about simplified satellite module layout design.A heuristic algorithm based on tabu search is put forward for solving this *** algorithm begins from a random initial configuration and applies the gradient method with an adaptive step length to search for the minimum energy *** jump out of the local minima and avoid the search doing repeated work,the algorithm adopts the strategy of tabu *** the process of tabu search,we improve the traditional neighboring solutions,tabu objects and the acceptance criteria of the current solution *** test two sets of benchmarks consisting of 11 representative instances from the current *** numerical results show that the proposed algorithm breaks the records in seven out of 11 instances,and obtains the optimal solutions for the other four instances.
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