A joint optimizationproblem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is cons...
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A joint optimizationproblem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is considered and investigated in this paper. First, a normalized multi-objective optimization problem (MOP) is formulated and transformed into a single-objectiveoptimizationproblem (SOP), by applying the weighted sum method. The formulated SOP is then proved to be continuously differentiable and strictly quasiconvex in the optimum average input power, which turns out to be a cup shape curve. Furthermore, the weighted quasiconvex tradeoff problem is solved by first using Charnes-Cooper transformation and then applying Karush-Kuhn-Tucker (KKT) conditions. The proposed optimal power allocation, which includes the optimal strategy for the link-layer EE-maximization problem and the EC-maximization problem as extreme cases, is proved to be sufficient for the Pareto optimal set of the original EE-EC MOP. Moreover, we prove that the optimum average power level monotonically decreases with the importance weight, but strictly increases with the normalization factor, the circuit power and the power amplifier efficiency. Simulation results confirm the analytical derivations and further show the effects of fading severeness and transmission power limit on the tradeoff performance.
Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of th...
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Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of the final front, hinder their applications in real-world situation. This paper proposes a surrogate-assisted interval multi-objective memetic algorithm (SA-IMOMA) that incorporates a surrogate model into the local search. In the framework of interval multi-objective memetic algorithms (IMOMAs), the fitness function of a local search is first defined by both the contribution of an individual to hyper-volume and the imprecision of the individual, and then a support vector machine (SVM) is trained and employed to evaluate local individuals so as to cut down the high computational cost of IMOMAs and further reduce the imprecision of the final front. The proposed algorithm was tested on 10 benchmark IMOPs and an IMOP in solar desalination. The empirical results indicate that SA-IMOMA is more economical than non-surrogate IMOMAs and superior to non-local-search IP-MOEA.
multi-objective optimization problems are a kind of problems optimizing simultaneously several conflicting objectives and keeping a balance between the diversity and the convergence of solutions. In this paper, some n...
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multi-objective optimization problems are a kind of problems optimizing simultaneously several conflicting objectives and keeping a balance between the diversity and the convergence of solutions. In this paper, some novel techniques are designed to improve the efficiency of multi-objective evolutionary algorithms. Firstly, a specific sub-function is separated from a series of objectives, which is applied to provide an approximate search direction and speed the convergence of the algorithm. Then, the crowding degree scheme, as in NSGA-II, is used to select potential promising solutions in the process of iterations such that Pareto solution set has more uniform and extensive distribution. Finally, a novel multi-objective evolutionary algorithm is presented by embedding these schemes intoMOEA/D. The simulation results show the proposed algorithm is feasible and efficient.
Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multiobjective optimal model of train operation adjustment, whose optimizationobjective is to r...
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Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multiobjective optimal model of train operation adjustment, whose optimizationobjective is to reduce the train delay time and the numbers of delay train. Since the model is established as an NP complete problem, a multi-objective particle swarm optimization algorithm (MPSO) is proposed to solve the complex problem. Considering the strategy of dispatcher' preference, MPSO can get a set of Pareto solutions in the actual train operation adjustment problems. The actual experiment, taking Beijing-Shanghai highspeed railway as example, is conducted to validate the feasibility of the algorithm compared with the basic particle swarm optimization algorithm (PSO). Results demonstrate that the model can capture the characteristics of the practical dispatching problem. MPSO is efficient for train operation adjustment and provides better solutions than the traditional approaches. (C) 2016 Published by Elsevier Ltd.
This project for vocational skills certification examination in different professions,different types and different levels has developed vocational skills certification exam management *** Ant colony optimization(ACO)...
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ISBN:
(纸本)9781509001668
This project for vocational skills certification examination in different professions,different types and different levels has developed vocational skills certification exam management *** Ant colony optimization(ACO)algorithm intelligently make skills identification papers has solved the multi-objectiveproblem;so determine vocational skills certification examination test of the different professions,different types,different levels of difficulty with normal distribution,meet different groups requirements of vocational skills certification examination.
In order to solve the contradiction between wireless communication service demand and spectrum resource shortage and enhance the utilization rate of spectrum, Cognitive Radio technology is necessary. Firstly, this pap...
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ISBN:
(纸本)9781509013456
In order to solve the contradiction between wireless communication service demand and spectrum resource shortage and enhance the utilization rate of spectrum, Cognitive Radio technology is necessary. Firstly, this paper presents a cognitive engine framework structure, and then the concept of bio- inspired and its application in CR computing were emphatically introduced. Finally, in order to solve spectrum parameters problem, this paper proposed based on autonomously search algorithm. Based on population evolution, ASA algorithm employs the foraging, reproduction, selection and mutation operators, and was tested under the multicarrier simulation environment. The experiment results show that ASA algorithm can better adjust each subcarrier communication parameters according to the requirement of cognitive engine parameters optimization, which include transmitted power, modulation mode, and bit-error-rate and so on, and finally satisfy the channel condition and the dynamic changes of the user service.
As a result of important practical significance in real-world engineering applications, multi-objective optimization problem has been one of scientific problems concerned by many researchers. In recent years, genetic ...
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ISBN:
(纸本)9781479970162
As a result of important practical significance in real-world engineering applications, multi-objective optimization problem has been one of scientific problems concerned by many researchers. In recent years, genetic algorithm (GA) has begun to be widely used to solve a variety of multi-objective optimization problems due to its population-based search mechanism. In this paper, NSGA-II, which is a most classical multi-objective GA, is investigated and discussed in detail. In order to address the problem of exploitation lacking in the search process of NSGA-II, a local search strategy, which is able to applied in multi-objectiveoptimization domain, is proposed and led into NSGA-II efficiently. Based on a set of benchmark test functions, the experimental results show that the proposed algorithm has demonstrated superior to NSGA-II in terms of convergence and distribution.
Because energy consumption in data centers is getting larger, the use of green energy for running data centers draws attention as a mean of reducing energy cost. The amount of green energy generated near data centers ...
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ISBN:
(纸本)9781467389853
Because energy consumption in data centers is getting larger, the use of green energy for running data centers draws attention as a mean of reducing energy cost. The amount of green energy generated near data centers depends on environment of the data centers. When more green energy is generated, more jobs should be processed by using the generated green energy to reduce energy cost. This policy of scheduling job is called green-aware job scheduling. On the other hand, it is necessary to minimize delay of processing jobs from their deadline. In this paper, we propose a model of green-aware job scheduling based on 2-objectiveoptimizationproblem to minimize energy cost and delay. The model shows that the green-aware job scheduling problem consists of linear objective functions, constraints of linear inequalities and integer constraints. By relaxing the integer constraints, it is possible to obtain approximated solutions of the problem.
Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multi-objective optimal model of train operation adjustment, whose optimizationobjective is to r...
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Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multi-objective optimal model of train operation adjustment, whose optimizationobjective is to reduce the train delay time and the numbers of delay train. Since the model is established as an NP complete problem, a multi-objective particle swarm optimization algorithm (MPSO) is proposed to solve the complex problem. Considering the strategy of dispatcher’ preference, MPSO can get a set of Pareto solutions in the actual train operation adjustment problems. The actual experiment, taking Beijing-Shanghai high-speed railway as example, is conducted to validate the feasibility of the algorithm compared with the basic particle swarm optimization algorithm (PSO). Results demonstrate that the model can capture the characteristics of the practical dispatching problem. MPSO is efficient for train operation adjustment and provides better solutions than the traditional approaches.
In this paper, a multi-objectiveoptimization approach for multi-carrier energy networks is discussed. A multi-carrier energy network is a system consists of various types of energy carrier such as electricity, natura...
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In this paper, a multi-objectiveoptimization approach for multi-carrier energy networks is discussed. A multi-carrier energy network is a system consists of various types of energy carrier such as electricity, natural gas, and heat. Minimizing the total cost of operation of such a system is a typical objective for optimization while another important objective is to minimize the total emission generated by the whole network. It is shown in the paper that the cost and emission functions are two opposite objectives that decreasing one of them would increase the other one and vice versa. Therefore, a multi-objectiveoptimization should be utilized to obtain the global optima of the problem based on the priority of each objective. According to the large size of the problem in actual networks, this could be a non-linear, non-convex, non-smooth, and high-dimension optimizationproblem that mathematical techniques could be trapped in local minima. Hence, it is better to use evolutionary techniques instead. To do so, a fuzzy decision making method is proposed in this paper which is merged with the well-known modified teaching-learning based optimization algorithm. This approach is implemented and applied to a typical multi-carrier energy network to verify the proposed methodology. (C) 2015 Elsevier Ltd. All rights reserved.
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