作者：
Wang, ShuaiLiu, JingXidian Univ
Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China
The directedness of links is of significance in complex networks, and much attention has been paid to study the dynamics of directed networks recently. In networked systems, where the emergence of cooperation and robu...
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The directedness of links is of significance in complex networks, and much attention has been paid to study the dynamics of directed networks recently. In networked systems, where the emergence of cooperation and robustness are two hot issues in recent decades. Previous studies have indicated that the structures for promoting these two properties are opposite, which also reveals the great impact of structures on the functionalities of networks. Moreover, several realistic problems also reflect the importance of simultaneously promoting the robustness and cooperation maintaining ability on directed networks, however, few studies have focused on solving this urgent problem. Therefore, in this paper, concentrating on optimizing the cooperation maintaining ability together with controllable robustness on directed networks, we first model this issue as a multi-objective optimization problem, and then a multi-objective evolutionary algorithm, labeled as MOEA-Net(cc), has been devised to solve this problem. In the experiments, the performance of MOEA-Net(cc) is validated on both synthetic and real networks, and the results show that MOEA-Net(cc) can not only achieve balanced optimal results without changing degree distribution of networks;but also create diverse Pareto fronts, which provide various potential candidates for decision makers to deal with social and economic dilemmas.
The team orienteering problem with time windows (TOPTW) is a well-known variant of the orienteering problem (OP) originated from the sports game of orienteering. Since the TOPTW has many applications in the real world...
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The team orienteering problem with time windows (TOPTW) is a well-known variant of the orienteering problem (OP) originated from the sports game of orienteering. Since the TOPTW has many applications in the real world such as disaster relief routing and home fuel delivery, it has been studied extensively. In the classical TOPTW, only one profit is associated with each checkpoint while in many practical applications each checkpoint can be evaluated from different aspects, which results in multiple profits. In this study, the multi-objective team orienteering problem with time windows (MOTOPTW), where checkpoints with multiple profits are considered, is introduced to find the set of Pareto optimal solutions to support decision making. Moreover, a multi-objective evolutionary algorithm based on decomposition and constraint programming (CPMOEA/D) is developed to solve the MOTOPTW. The advantages of decomposition approaches to handle multi-objective optimization problems and those of the constraint programming to deal with combinatorial optimization problems have been integrated in CPMOEA/D. Finally, the proposed algorithm is applied to solve public benchmark instances. The results are compared with the best-known solutions from the literature and show more improvement. (C) 2018 Elsevier B.V. All rights reserved.
Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple...
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Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper.
An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning...
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An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study. (C) 2017 Elsevier Ltd. All rights reserved.
This paper presents a multi-objective evolutionary algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection p...
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ISBN:
(纸本)9783319668307;9783319668291
This paper presents a multi-objective evolutionary algorithm (MOEA) for tuning type-2 fuzzy sets and selecting rules and conditions on Fuzzy Rule-Based Classification Systems (FRBCS). Before the tuning and selection process, the Rule Base is learned by means of a modified Wang-Mendel algorithm that considers type-2 fuzzy sets in the rules antecedents and in the inference mechanism. The multi-objective evolutionary algorithm used in the tuning process has three objectives. The first objective reflects the accuracy where the correct classification rate of the FRBCS is optimized. The second objective reflects the interpretability of the system regarding complexity, by means of the quantity of rules and is to be minimized through selecting rules from the initial rule base. The third objective also reflects the interpretability as a matter of complexity and models the quantity of conditions in the Rule Base. Finally, we show how the FRBCS tuned by our proposed algorithm can achieve a considerably better classification accuracy and complexity, expressed by the quantity of fuzzy rules and conditions in the RB compared with the FRBCS before the tuning process.
Aiming at the deployment optimization of complex Internet of Things(IoT) systems, we propose a new multi-objective optimization algorithm using multiple indicators with reinforcement learning, called MIEA-RL. In MIEA-...
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Aiming at the deployment optimization of complex Internet of Things(IoT) systems, we propose a new multi-objective optimization algorithm using multiple indicators with reinforcement learning, called MIEA-RL. In MIEA-RL, a set of evaluation indicators are employed to guide the evolution of population, while a Q-learning method is designed to manage these indicators in an efficient way during the search. To be specific, each candidate indicator is determined by the performance improvement of the population selected by the current indicator. Moreover, the search biases of different indicators can be adaptively balanced according to a Q-learning table. Accordingly, the convergence and diversity can be maintained effectively while the algorithm complexity is not increased. Finally, the MIEA-RL is applied to resolve the real-world IoT optimization instances in the experiment. Results show the proposed algorithm is effective and efficient to handle with these problems.
The main drawbacks of mean-variance model are to generate corner solutions and low diversity in the portfolios. To overcome these defects, firstly, we propose a new proportion entropy function as an objective function...
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The main drawbacks of mean-variance model are to generate corner solutions and low diversity in the portfolios. To overcome these defects, firstly, we propose a new proportion entropy function as an objective function to generate well-diversified portfolio. Secondly, considering the transaction cost and liquidity, we present a new fuzzy mean-variance-entropy multi-objective portfolio selection model to find tradeoffs between risk, return and the diversification degree of portfolio, which is able to address a more realistic portfolio selection problem. Thirdly, we combined several efficient schemes to form an efficient algorithm to maintain the diversity of obtained solutions and to solve the presented multi-objective portfolio selection model. The proposed multi-objective portfolio model combined with the multi-objective evolutionary algorithm can overcome these defects fundamentally. Finally, to demonstrate the efficiency and effectiveness of the proposed model and algorithm, the designed algorithm is compared with two famous algorithms: multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGA-II) through some simulations based on the data of the Shanghai Stock Exchange Market. Simulation results show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto fronts than the other two algorithms, and our proposed portfolio model can yield good performance of portfolio.
Community structure is an important topological property of complex networks, which has great significance for understanding the function and organization of networks. Generally, community detection can be formulated ...
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Community structure is an important topological property of complex networks, which has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a single-objective or multi-objective optimization problem. Most existing optimization-based community detection algorithms are only applicable to disjoint community structure. However, it has been shown that in most real-world networks, a node may belong to multiple communities implying overlapping community structure. In this paper, we propose a multi-objective evolutionary algorithm for identifying overlapping community structure in complex networks based on the framework of non-dominated sorting genetic algorithm. Two negatively correlated evaluation metrics of community structure, termed as negative fitness sum and unfitness sum, are adopted as the optimization objectives. In our algorithm, link-based adjacency representation of overlapping community structure and a population initialization method based on local expansion are proposed. Extensive experimental results on both synthetic and real-world networks demonstrate that the proposed algorithm is effective and promising in detecting overlapping community structure in complex networks.
The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi period dynamic emergency resource schedul...
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The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II). (C) 2017 Elsevier Ltd. All rights reserved.
Sentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more robust classification schemes. However, most approaches in the fi...
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Sentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more robust classification schemes. However, most approaches in the field incorporated feature engineering to build efficient sentiment classifiers. The purpose of our research is to establish an effective sentiment classification scheme by pursuing the paradigm of ensemble pruning. Ensemble pruning is a crucial method to build classifier ensembles with high predictive accuracy and efficiency. Previous studies employed exponential search, randomized search, sequential search, ranking based pruning and clustering based pruning. However, there are tradeoffs in selecting the ensemble pruning methods. In this regard, hybrid ensemble pruning schemes can be more promising. In this study, we propose a hybrid ensemble pruning scheme based on clustering and randomized search for text sentiment classification. Furthermore, a consensus clustering scheme is presented to deal with the instability of clustering results. The classifiers of the ensemble are initially clustered into groups according to their predictive characteristics. Then, two classifiers from each cluster are selected as candidate classifiers based on their pairwise diversity. The search space of candidate classifiers is explored by the elitist Pareto-based multi-objective evolutionary algorithm. For the evaluation task, the proposed scheme is tested on twelve balanced and unbalanced benchmark text classification tasks. In addition, the proposed approach is experimentally compared with three ensemble methods (AdaBoost, Bagging and Random Subspace) and three ensemble pruning algorithms (ensemble selection from libraries of models, Bagging ensemble selection and LibD3C algorithm). Results demonstrate that the consensus clustering and the elitist pareto-based multi-objective evolutionary algorithm can be effectively used in ensemble pruning. The experimental analysis with
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