The main challenges faced by Telecom Operators are to increase the network capacity and performance required by new services while reducing the CAPEX and so well the OPEX charges. The Cloud-Radio Access Network (C-RAN...
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ISBN:
(纸本)9781509043729
The main challenges faced by Telecom Operators are to increase the network capacity and performance required by new services while reducing the CAPEX and so well the OPEX charges. The Cloud-Radio Access Network (C-RAN) is a proposed solution that will reduce the overall needed processing capacity and power consumption as well as improve the network capacity and quality of services thus reducing the costs. To achieve the goals of this solution, the Remote Radio Heads (RRHs) from different locations will be clustered and assigned to a common Base Band Unit (BBU) pool in order to minimize the exchanged signaling between sites and then reducing the delay in the network. However, the statistical multiplexing gain is a result of BBU clustering which is an NP-Hard problem. To overcome this problem, many heuristic and metaheuristic clustering algorithms are proposed. In this work, we are mainly interested by the clustering problem modeling and evaluation with Tabu Search (TS) metaheuristic algorithm, which has been investigated and evaluated.
In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different prod...
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In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different products, convene as a product development team, and that team completes a product design only after all the modules have been designed. In real-life situations, a customer order can have some requirements including due dates, weights of jobs, and unequal ready times. Once encountering different ready times, waiting for future order or job arrivals to raise the completeness of a batch is an efficient policy. Meanwhile, the literature releases that few studies have taken unequal ready times into consideration for order scheduling problem. Motivated by this limitation, this study addresses an OS scheduling model with unequal order ready times. The objective function is to find a schedule to optimize the total completion time criterion. To solve this problem for exact solutions, two lower bounds and some properties are first derived to raise the searching power of a branch-and-bound method. For approximate solution, four simulated annealing approaches and four heuristic genetic algorithms are then proposed. At last, several experimental tests and their corresponding statistical outcomes are also reported to examine the performance of all the proposed methods.
The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces t...
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The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS), improved harmony search (IHS), and other self-adaptive variants for large-scale optimization problems and constrained problems.
This work proposes a methodology to optimize a reinforced concrete structure. For this, the Whale Optimization Algorithm (WOA) algorithm was used, an algorithm from the group of metaheuristic algorithms, which present...
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This work proposes a methodology to optimize a reinforced concrete structure. For this, the Whale Optimization Algorithm (WOA) algorithm was used, an algorithm from the group of metaheuristic algorithms, which presents an easy computational implementation. As a study object, a frame structure adapted from a real reinforced concrete building was used, subjected to the dynamic action of artificially generated synoptic wind. The objective function is to reduce the volume of concrete of the structure. For that, the dimensions of the cross-sections were used as design variables, and the maximum displacement at the top imposed by the ASCE/SEI 7-10 standard as a lateral constraint, as well as the maximum story drift between floors. In addition to this structural optimization, it was also proposed the use and optimization of Tuned Mass Dampers (TMD), in different quantities, positions and parameters, improving the dynamic response of the reinforced concrete building. The results show that for this situation it was possible to reduce the concrete volume of the structure by approximately 24%, respecting the maximum limit of displacement at the top required by the standard.
The purpose of the present study is to predict and draw up non-grain cultivated land (NCL) susceptibility map based on optimized Extreme Gradient Boosting (XGBoost) model using the Particle Swarm Optimization (PSO) me...
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The purpose of the present study is to predict and draw up non-grain cultivated land (NCL) susceptibility map based on optimized Extreme Gradient Boosting (XGBoost) model using the Particle Swarm Optimization (PSO) metaheuristic algorithm. In order to, a total of 184 NCL areas were identified based on historical records, and a total of 16 NCL susceptibility conditioning factors (NCLSCFs) were considered, based on both a systematic literature survey and local environmental conditions. The results showed that the XGBoost model optimized by PSO performed well in comparison to other machine learning algorithms;the values of sensitivity, specificity, PPV, NPV, and AUC are 0.93, 0.89, 0.88, 0.93, and 0.96, respectively. Slope, rainfall, fault density, distance from fault and drainage density are most important variables. According to the results of this study, the use of meta-innovative algorithms such as PSO can greatly enhance the ability of machine learning models.
In the paper is considered a developed applied software intended to solve practical packing and cutting problems. In a basis of this software lays using of a created unified class library designed for solving various ...
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In the paper is considered a developed applied software intended to solve practical packing and cutting problems. In a basis of this software lays using of a created unified class library designed for solving various optimization problems of resource allocation. The software may be in solving of a wide set of NPcompleted optimization problems including one dimensional cutting and packing problems, two-dimensional and three-dimensional orthogonal bin packing problems, knapsack problems, cutting stock problems, strip packing problems and many others. The described software may be used for analysis of effectiveness of various metaheuristic optimization algorithms that are used in solving of packing problems.
The authors discuss the paper 'Ant Colony Optimization (ACO) for Multimode Resource-Constrained Project Scheduling' by Hong Zhang, published in the April 2012 issue. Topics covered include the examples used by...
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The authors discuss the paper 'Ant Colony Optimization (ACO) for Multimode Resource-Constrained Project Scheduling' by Hong Zhang, published in the April 2012 issue. Topics covered include the examples used by the author to compare the ACO method with metaheuristic methods such as genetic algorithm and particle swarm optimization and several runs of each metaheuristic method conducted. Also mentioned is the effectiveness of the ACO algorithm in planning construction projects.
Until now, most, if not all, of the metaheuristic algorithms have been extremely sensitive to the initial solutions and may even converge to a local optimum at early iterations for most optimization problems. This pap...
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ISBN:
(纸本)9781479938414
Until now, most, if not all, of the metaheuristic algorithms have been extremely sensitive to the initial solutions and may even converge to a local optimum at early iterations for most optimization problems. This paper introduces an effective and efficient framework, called multiple-search multi-start (MSMS), to mitigate the impact of these problems. To evaluate the performance of the proposed framework, we apply it to k-means and particle swarm optimization for the clustering problem and compare the results with those of several well-known clustering algorithms. The experimental results show that the proposed framework can significantly enhance the performance of not only single-solution-based but also population-based metaheuristic algorithms in terms of both the quality and the computation time.
Cloud computing is a technique of utilizing resources over internet to achieve a certain goal. With massive increase in the number of users and their variable demand of different services, hundreds to thousands of ser...
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ISBN:
(纸本)9781509007752
Cloud computing is a technique of utilizing resources over internet to achieve a certain goal. With massive increase in the number of users and their variable demand of different services, hundreds to thousands of servers have to be deployed. Equally massive cooling systems are required to keep the servers working under normal temperatures. This leads to high C02 emission and following current trends it is only expected to rise in the coming years. Thus cloud computing services are directly or indirectly contributing to global warming. Green computing is the solution to this problem in which same amount of weightage is given to makespan optimization as to the energy consumption and greenhouse gas emissions of a machine. In this paper, we perform energy analysis of a makespan-efficient algorithm Discrete Symbiotic organisms search (DSOS) and based on the results, we conclude that this algorithm can be made more energy-efficient which is also our area of future research.
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