In this paper, an optimization algorithm based on membrane system is proposed for numerical optimization problems. In the proposed algorithm, we designed two mechanisms to simulate the movement of molecules in arbitra...
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ISBN:
(纸本)9781728124858
In this paper, an optimization algorithm based on membrane system is proposed for numerical optimization problems. In the proposed algorithm, we designed two mechanisms to simulate the movement of molecules in arbitrary direction and a certain direction to balance global exploration and local exploitation. To test the performance of the proposed algorithm, eight benchmark functions were chosen. The simulation results show that the proposed algorithm is more advantageous than other experimental algorithms in solving numerical optimization problems.
In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during re...
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ISBN:
(纸本)9781538652619
In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i. e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.
With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more dem...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed a new optimization algorithm, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previously perceived. The effort here builds upon this previously developed optimization algorithm to include multiple infill sampling capability that combines the concept of generalized expected improvement function, unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGO with the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficult engineering design optimization problems. The results reveal the performance of the new approach is problem dependent. When applied to a ten-bar truss problem, the newly proposed multiple infill strategy consistently leads to a better design solutions when compared to the existing CPTV method (implemented with the context of the AMIEGO algorithm). On the other hand, when applied to a mixed-integer high fidelity wing topology optimization problem - MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferior solution as compared to CPTV approach. These results also reveal that a small number of starting points, in general, are sufficient to lead to a good overall solution.
Industrial Wireless Sensor Networks (IWSNs) are emerged as flexible and cost-efficient alternatives to the traditional wired networks in various monitoring and control applications within the industrial domain. Low de...
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ISBN:
(纸本)9781728112688
Industrial Wireless Sensor Networks (IWSNs) are emerged as flexible and cost-efficient alternatives to the traditional wired networks in various monitoring and control applications within the industrial domain. Low delay is a key feature of delay-sensitive applications as the data is typically valid for a short interval of time. If data arrives too late it is of limited use which may lead to performance drops or even system outages which can create significant economical losses. In this paper, we propose a decentralized optimization algorithm to minimize the End-to-End (E2E) delay of multi-hop IWSNs. Firstly, we formulate the optimization problem by considering the objective function as the network delay where the constraint is the stability criteria based on the total arrival rate and the total service rate. The objective function is proved to be strictly convex for the entire network, then a Decentralized Primal-Dual (DeP-D) algorithm is proposed based on the sub-gradient method to solve the formulated optimization problem. The performance of the proposed DeP-D is evaluated through simulations and compared with WirelessHART network and the results show that the proposed DeP-D can achieve at least 40% reduction in the average E2E delay.
In the wireless ubiquitous environment, this paper analyses and models the dynamic volume of access to VOD business. This paper also proposes a wireless business optimization algorithm that enables the CDN edge server...
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ISBN:
(纸本)9781728121840
In the wireless ubiquitous environment, this paper analyses and models the dynamic volume of access to VOD business. This paper also proposes a wireless business optimization algorithm that enables the CDN edge server to help the core network to divert pressure of user's access best. Firstly, a probability model of the daily volume of access is defined, based on video's score and video's online time. Further, a function relationship between the parameter of the model and the joint conditions above is established to accurately reflect the average value of the daily volume of access. On this basis, the predicted value of the daily volume of access to any video under a certain guarantee probability is obtained. Then, an optimization model for distribution and download of videos is built for the edge server of CDN with limited storage space. A distribution and download scheme of videos based on 0-1 knapsack algorithm is proposed. The simulation results verify the correctness of the model of volume of access and the effectiveness of the optimization algorithm.
The past decades have seen an extensive investigation of evolutionary algorithms. The recombination operators of most evolutionary algorithms are either single-point crossover or multi-point crossover for solving 0-1 ...
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ISBN:
(纸本)9781728160924
The past decades have seen an extensive investigation of evolutionary algorithms. The recombination operators of most evolutionary algorithms are either single-point crossover or multi-point crossover for solving 0-1 combinatorial optimization problems. There is a little studies on excavating the variable relationship to improve the efficiency of the recombination operator. Hence, we propose an optimization algorithm based on excavating variable relationship for solving 0-1 combinational problems. The aim of the recombination operator is fully utilizing the inherent information from every slice component of all individuals. We compared the proposed algorithm with the classic evolutional algorithm with single-point crossover operation on several 0-1 knapsack problems, which is a classical combinatorial optimization problem. The simulation results show the convergence efficiency of the proposed algorithm.
Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, t...
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ISBN:
(纸本)9781728104669
Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, the performance of AMO is degraded rapidly when the dimensionality is larger than 30. In order to overcome this shortcoming, an improved animal migration algorithm (IAMO) based on interactive learning behavior is proposed in this paper. First, we introduce an interactive learning behavior that individuals will learn from each other by exchanging information. During the search process, the search step is dynamically adjusted. In this case, the intelligence of IAMO is higher than AMO. Second, a refined search method is used to search around the current solutions, and this method can enhance the search ability of the algorithm. Third, a birth-and-death mechanism is designed to avoid local optimum. The effectiveness of IAMO is verified on 100 dimensional benchmark functions, and the empirical results show that the performance of IAMO is promising.
In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor re...
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ISBN:
(纸本)9781728101057
In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor reconstruction (FVR-kNN) has been proposed. However, dealing with fault problem caused by multiple variables, the algorithm needs to exhaustive the arrangement of all variables, resulting in high complex computation. And the multivariate estimation in FVR-KNN is not accurate. Thus, this paper proposes a variable recognition optimization algorithm based on FVR-kNN (OFVR-kNN). It optimizes the estimation steps of FVR-kNN in reconstructing multivariate, guaranteeing that the estimations of these potential fault variables have no mutual influence. According to the fault magnitude in corresponding direction, the fault variables are selected in turn. OFVR-kNN does not need to exhaustive all the combinations, greatly reducing the number of reconstructions in fault sample. In this paper, the validity of the optimization method is proved in Tennessee Eastman process.
Management of video content distribution through files allocation or caching in content delivery networks with some degree of reliable security measures is representing a big issue in video service delivery and user&#...
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ISBN:
(纸本)9783030011680;9783030011673
Management of video content distribution through files allocation or caching in content delivery networks with some degree of reliable security measures is representing a big issue in video service delivery and user's requirements for quality of experience provision are constantly tightened. Operators are looking for new ways to efficiently deliver video content to specific customers or classes of customers, which allow the transfer of large amounts of traffic with the appropriate quality of experience. Mobile Edge Computing (MEC), initiated as an Industry Specification Group (ISG) within ETSI, is quickly gaining traction as a disruptive technology that promises to bring applications and content closer to the network edge, a move that will reduce latency and make new services optimization possible. The aim of this thesis is to provide optimization algorithms for accessing IPTV video services in managed way over Software-defined Networking (SDN) to meet the high Quality of service (QoS) reducing network latency and, ultimately, improving the end consumer's quality of experience (QoE). We also show the positive impact of SDN network using our algorithm noticeably reducing video delay.
This paper discusses the long-term planning problem of oilfield development. The planning aims to deploy the workload of stimulation treatments with uncertain indicators. And the uncertainties of initial stimulation e...
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This paper discusses the long-term planning problem of oilfield development. The planning aims to deploy the workload of stimulation treatments with uncertain indicators. And the uncertainties of initial stimulation effect, annual effect error and new recoverable reserves are considered. With their uncertainty sets and the adaptability of decisions, a multiobjective multistage robust integer optimization model is constructed. In this model, the total development cost is minimized and the total new recoverable reserves is maximized. And the model makes decisions on the workload of each stimulation treatment in each year under constraints of annual oil production and workload balance. In addition, an algorithm for solving multiobjective multistage robust integer optimization model is proposed, which can obtain the finitely adaptive robust efficient solution set. Finally, a numerical example of long-term oilfield development planning is presented. The planning model with given uncertainty sets is solved, to verify the validity of the proposed model and algorithm.
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