作者:
Dong, BeiWu, JiansheJiao, LichengXidian Univ
Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China
Jointly consider routing and spectrum selection is essentially and necessary in multi-hop cognitive radio networks. System cost and throughput are commonly used to evaluate performance of routing and spectrum selectio...
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Jointly consider routing and spectrum selection is essentially and necessary in multi-hop cognitive radio networks. System cost and throughput are commonly used to evaluate performance of routing and spectrum selection schemes. Traditional methods mostly translate these metrics into a single objective function, and corresponding weights are allocated to each metric representing impact on the entire network performance. Optimal solutions of these approaches are sensitive to the weight settings which are usually hard to appropriately chosen. In this work, the task of routing and channel allocation is modeled as a two-objective optimization problem. Two conflicting metric functions system total throughput and total cost are optimized simultaneously, and a novel memetic algorithm which adopts a new neighborhood search procedure is proposed to solve this problem. Incorporated with robustness consideration on routing, a new robustness metric is also presented to work as a decision mechanism to ensure the robustness of the entire network. The aim of this task is to find the best compromise routing and channel allocation scheme on system throughput, cost and robustness among the feasible solution set. Simulation results demonstrate that the optimal solution set obtained by the memetic algorithm can clearly show the conflicting relationship of the system cost and throughput when choosing different routing and channel selection schemes. The best solution made by the additional robustness metric among these solutions can achieve the best performance of the cognitive radio network.
Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly int...
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Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly intractable. In order to cope with those problems, several (meta) heuristic procedures have been developed during the last decade whose aim is to obtain a good discrete approximation of the Pareto-front. In this vein, a new multi-objective evolutionary algorithm, called FEMOEA, which can be applied to many nonlinear multi-objective optimization problems, has recently been proposed. Through a comparison with an exact interval branch-and-bound algorithm, it has been shown that FEMOEA provides very good approximations of the Pareto-front. Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D. Comprehensive computational studies have shown that, among the studied algorithms, FEMOEA was the one providing, on average, the best results for all the quality indicators analyzed. However, when the set approximating the Pareto-front must have many points (because a high precision is required), the computational time needed by FEMOEA may not be negligible at all. Furthermore, the memory requirements needed by the algorithm when solving those instances may be so high that the available memory may not be enough. In those cases, parallelizing the algorithm and running it in a parallel architecture may be the best way forward. In this work, a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. The results show that FEMOEA-Paral is able to maintain the effectiveness of the sequential version and this by reducing the computational costs. Furthermore, the parallel version shows good scalability. The efficiency results have been analyzed by means of a profiling and tracing toolkit for performance analysis. (C) 2014 Elsevier Inc. All
Robot path planning is an important content in the field of robot research. Robot Path Planning is a typical multi-objective optimization problem. The path length, the degree of path smoothness and the degree of secur...
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ISBN:
(纸本)9781510830363
Robot path planning is an important content in the field of robot research. Robot Path Planning is a typical multi-objective optimization problem. The path length, the degree of path smoothness and the degree of security are the optimization objectives in this work. And an improved multi-objective PSO method is used for optimization. In this method, in order to make the particle population multi-objective particle swarm optimization algorithm can quickly converge to the Pareto optimal boundary, an environment selection and a matching selection strategy are proposed. At each iteration of the algorithm, in order to improve the population information exchange and reduce the randomness, the environmental selection and matching selection strategy of SPEA2 are used for multi-objective PSO method, and the particle population can faster convergence to the Pareto optimal boundary. The simulation results verify the method, and the result of proposed method is better than that of multi-objective PSO method, and the simulations indicates that the proposed model is practical for robot path planning.
Software requirements selection is the engineering process in which the set of new requirements which will be included in the next release of a software product are chosen. This NP-hard problem is an important issue i...
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Software requirements selection is the engineering process in which the set of new requirements which will be included in the next release of a software product are chosen. This NP-hard problem is an important issue involving several contradictory objectives that have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to cover the highest number of customer requirements. Furthermore, in real instances of the problem, the requirements tackled suffer interactions and other restrictions which complicate the problem. In this paper, we use an adapted multi-objective version of the differential evolution (DE) evolutionaryalgorithm which has been successfully applied to several real instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer's satisfaction, and with three interaction constraints. On the other hand, the original DE algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out to demonstrate the effectiveness of the multiobjective proposal and the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature under a set of public datasets. (c) 2014 Elsevier Inc. All rights reserved.
Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing researc...
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Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing research on the testing resource allocation problem takes a single-objective optimization approach, which may not adequately address all the concerns in the decision-making process. In this paper, an architecture-based multi-objective optimization approach to testing resource allocation is proposed. An architecture-based model is used for system reliability assessment, which has the advantage of explicitly considering system architecture over the reliability block diagram (RBD)-based models, and has good flexibility to different architectural alternatives and component changes. A system cost modeling approach which is based on well-developed software cost models is proposed, which would be a more flexible, suitable approach to the cost modeling of software than the approach adopted by others which is based on an empirical cost model. A multi-objective optimization model is developed for the testing resource allocation problem, in which the three major concerns in the testing resource allocation problem, i.e., system reliability, system cost, and the total amount of testing resource consumed, are taken into consideration. A multi-objective evolutionary algorithm (MOEA), called multi-objective differential evolution based on weighted normalized sum (WNS-MODE), is developed. Experimental studies are presented, and the experiments show several results. 1) The proposed architecture-based multi-objective optimization approach can identify the testing resource allocation strategy which has a good trade-off among optimization objectives. 2) The developed WNS-MODE is better than the MOEA developed in recent research, called HaD-MOEA, in terms of both solution quality and computational efficiency. 3) The WNS-MODE seems quite robust from the sensitivity analysis results.
Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper...
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Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper presents a model-driven approach supporting the development of promising adaptation pathways, and illustrates the approach using a hypothetical case. We use robust optimization over uncertainties related to climate change, land use, cause-effect relations, and policy efficacy, to identify the most promising pathways. For this purpose, we generate an ensemble of possible futures and evaluate candidate pathways over this ensemble using an Integrated Assessment Meta Model. We understand 'most promising' in terms of the robustness of the performance of the candidate pathways on multiple objectives, and use a multi-objective evolutionary algorithm to find the set of most promising pathways. This results in an adaptation map showing the set of most promising adaptation pathways and options for transferring from one pathway to another. Given the pathways and signposts, decision-makers can make an informed decision on a dynamic adaptive plan in a changing environment that is able to achieve their intended objectives despite the myriad of uncertainties.
A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bia...
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A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values;five classifiers divided into three classes: rule induction learning, lazy learning and approximate models;and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains. (C) 2015 Elsevier B.V. All rights reserved.
Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum retu...
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ISBN:
(纸本)9781510835429
Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum return. Papers define portfolio optimization problems, analyze the shortcomings of the existing portfolio model and the corresponding solution ideas, introduce multi-objective genetic algorithm and demonstrate the feasibility of multi-objective genetic algorithm in the application portfolio. algorithm design process, detailed process of constructing and building a portfolio investment in line with the actual situation, put forward five objective optimization model from all angles, through a reasonable, optimized simulation test, and then select the algorithm selected from a number of programs better overall performance of the program.
In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider-crank. Novel techniques are p...
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In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider-crank. Novel techniques are presented for formulating the design variables in the kinematic optimization that guarantee satisfaction of the Grashof condition and of transmission angle requirements without the need for an explicit constraint function. Additionally, a nested generalization of the popular NSGA-II algorithm is presented that allows simultaneous optimization of the kinematic, dynamic, and thermodynamic properties of the mechanism. This approach successfully solves the complex six-objective optimization problem, with challenges for future refinement including improvement of the combustion simulation to attain better accuracy without prohibitive computational expense.
In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entro...
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ISBN:
(纸本)9781467386609
In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entropy function is presented. By using the model, we can find tradeoffs between risk, return and the diversification degree of portfolio. Thirdly, a new multi-objective evolutionary algorithm is designed to solve the proposed model. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed model and the corresponding algorithm.
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