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An Overlapping Community Detection Based multi-objective evolutionary algorithm for Diversified Social Influence Maximization

An Overlapping Community Detection Based Multi-Objective Evo...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Zhang, Lei Sun, Fengjiao Cheng, Fan Ma, Haiping Sun, Xiaoyan Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230039 Peoples R China Anhui Univ Inst Phys Sci Hefei 230039 Peoples R China Anhui Univ Inst Informat Technol Hefei 230039 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9781728169293

Influence maximization refers to selecting a group of nodes from a social network, which obtains the largest influence spread under a cascade model. However, most of the existing works only focused on the influence and ignored the diversity of influenced crowd. Thus, scholars have raised the issue of diversified social influence maximization recently, using the category information of nodes to design diversity indicator and introducing a trade-off parameter to balance the two objectives influence and diversity as one single objective for optimization. In fact, the category information of nodes in the network is usually difficult to be collected, thus the definition of diversity based on nodes' categories is not very general and accurate. In addition, it is very difficult to set the trade-off parameter, especially when there is no prior knowledge in real applications. To this end, we employ overlapping community structure information to design the diversity of nodes without any node's additional (e.g. category) information. Due to the two objectives of influence and diversity may be conflicting, a multi-objective evolutionary algorithm named MOEA-DIM is proposed to optimize the two objectives simultaneously, which does not need to set the trade-off parameter between the two objectives. In MOEA-DIM, a network reduction strategy based on overlapping community structure is suggested to greatly reduce the search space. In addition, a population initialization strategy based on random walk is designed to accelerate the convergence of the algorithm. Experiments on six real-world datasets show that the proposed algorithm MOEA-DIM has promising performance in terms of both effectiveness and efficiency.

关键词： Influence maximization diversity overlapping community detection multi-objective evolutionary algorithm

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Remote Sensing Image Fusion Based on multi-objective evolutionary algorithm

Remote Sensing Image Fusion Based on Multi-objective Evoluti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhou, Xiuling Song, Mengxin Guo, Ping Yao, Li Beijing Normal Univ Lab Image Proc & Pattern Recognit Beijing 100875 Peoples R China Beijing City Univ Aftificial Intelligence Inst Beijing Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China

ISBN: (纸本)9781424465880

The purpose of fusion the multispectral (MS) and panchromatic (PAN) remote sensing images is to obtain high spatial resolution and quality of the PAN image as well as to preserve spectral information of the MS image. The parameter selection of fusion rule will directly affect the fusion result. In this paper, a new fusion method is presented based on multi-objective evolutionary algorithm (called SMS-EMOA). First, the MS image is converted from the RGB color space into the HSI (Hue-Saturation- Intensity) color space. Then, by applying Contourlet transform to the PAN image and the Intensity component of the MS image, the weighted model is used to fuse the sub-images, and the SMS-EMOA is adopted for optimal parameter selection. Finally, a fusion image is obtained by the inverse Contourlet and HSI transform. The experimental results show that the proposed fusion rule optimization method not only can gain the spatial resolution, but also can preserve the spectral information of the original MS image very well.

关键词： image fusion contourlet transform multi-objective evolutionary algorithm hypervolume

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A multi-objective evolutionary algorithm for Emergency Logistics Scheduling in Large-scale Disaster Relief

A Multi-objective Evolutionary Algorithm for Emergency Logis...

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IEEE Congress on evolutionary Computation (CEC)

作者： Gan, Xiaohui Liu, Jing Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781509046010

The emergency logistics scheduling (ELS) is to enable the dispatch of emergency supplies to the victims of disasters timely and effectively, which plays a crucial role in large-scale disaster relief. In this paper, we first design a new multi-objective model that considers both the total unsatisfied time and transportation cost for the ELS problem in large-scale disaster relief (ELSP-LDR), which is on the scenery of multi-disasters and multi-suppliers with several kinds of resources and vehicles. Then, a modified non-dominated sorting genetic algorithm II (mNSGA-II) is proposed to search for a variety of optimal emergency scheduling plans for decision-makers. With the intrinsic properties of ELSP-LDR in mind, we design three repair operators to generate improved feasible solutions. Compared with the original NSGA-II, a local search operator is also designed for mNSGA-II, which significantly improves the performance. We conduct two experiments (the case of Chi-Chi earthquake and Great Sichuan Earthquake) to validate the performance of the proposed algorithm.

关键词： emergency logistics scheduling disaster relief multi-objective evolutionary algorithm non-dominated sorting

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CC-MOEA: A Parallel multi-objective evolutionary algorithm for Recommendation Systems 20th

CC-MOEA: A Parallel Multi-objective Evolutionary Algorithm f...

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20th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Wei, Guoshuai Wu, Quanwang Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Recommender systems have nowadays been widely used in a variety of applications such as Amazon and Ebay. Traditional recommendation techniques mainly focus on recommendation accuracy only. In reality, other metrics such as diversity and novelty also play a key role for modern recommendation systems. Although some works based on multi-objective evolutionary algorithm have been proposed for multi-objective recommendation, they are usually very time-consuming because of the large data size of the RSs and the long-term evolution iterations and hence it greatly limits their application in practice. To address these shortcomings, this paper first designs a multi-objective recommendation system, taking into account diversity and novelty as well as accuracy. Then, a novel parallel multi-objective evolutionary algorithm called CC-MOEA is proposed to optimize these conflicting metrics. CC-MOEA is devised grounded on NSGA-II and a cooperative coevolutionary island model, and a parallel global non-dominated selection method is introduced to reduce the runtime of finding the global optimal individuals. Furthermore, a new initialization method and a crossover operator are specifically designed. The experimental results reveal that CC-MOEA outperforms some state-of-the-art algorithms in terms of hypervolume and runtime.

关键词： Accuracy Cooperative coevolutionary multi-objective evolutionary algorithm Parallel Recommendation system

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Use of multi-objective evolutionary algorithm Optimization for Low Impact Development Placement

Use of Multi-Objective Evolutionary Algorithm Optimization f...

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International Low Impact Development Conference (LID)

作者： Giacomoni, Marcio H. Univ Texas San Antonio Dept Civil & Environm Engn One UTSA Circle San Antonio TX 78249 USA

ISBN: (数字)9780784479025

ISBN: (纸本)9780784479025

The transformation of natural land cover to urban areas severely alters the hydrologic flow regime of watersheds. The negative impacts include the increase of surface runoff and decrease of infiltration rates, which can result in more frequent and intense flood events and the reduction of groundwater recharge. Low Impact Developments (LIDs) are strategies designed to better mimic the natural flow regime by promoting higher infiltration and the treatment of stormwater. Examples of LID structures are bio-gardens, green roofs, bio-swales and pervious pavements. While the expansion of LIDs in urban catchments would be desirable, retrofitting large urban watersheds with LIDs can be cost prohibitive. This study combines hydrologic simulation with a multi-objective evolutionary algorithm (MOEA) to find solutions in terms of LID design and location in urban catchments that maximize the environmental benefits and characterize the tradeoffs between LID performance indicators and costs. The solutions are evaluated in terms of costs, peak flow, and the Hydrologic Footprint Residence (HFR). The HFR is a new metric designed to quantify the impacts of urbanization in the hydrologic regime by representing the dynamics of inundated areas and residence time of flood waves. The results provide a basis for better stormwater management and planning because tradeoff curves provide a wider spectrum of designs and placement guidelines, improving the sustainability of urban watersheds.

关键词： flow regime Urban watersheds Hydrological regime multi-objective evolutionary algorithm low impact algorithm optimization land cover tradeoffs HFR REACTOR

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Evolving Temporal Fuzzy Association Rules from Quantitative Data with a multi-objective evolutionary algorithm

Evolving Temporal Fuzzy Association Rules from Quantitative ...

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6th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Matthews, Stephen G. Gongora, Mario A. Hopgood, Adrian A. De Montfort Univ Ctr Computat Intelligence Leicester LE1 9BH Leics England

ISBN: (纸本)9783642212185

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets.

关键词： multi-objective evolutionary algorithm fuzzy association rules temporal association rules NSGA-II hyrbid

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A multi-objective evolutionary algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems 24th

A Multi-objective Evolutionary Algorithm Approach for Optimi...

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24th International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Prince, Michael H. DeHaan, Kristian Tauritz, Daniel R. Auburn Univ BONSAI Lab Auburn AL 36830 USA

ISBN: (纸本)9783030726980;9783030726997

Motivated by a real-world application, we consider an Assembly Job Shop Scheduling Problem (AJSSP), with three objectives: product quality, product quantity, and first product lead time. Using real-world inspection data, we demonstrate the ability to model product quality transformations during assembly jobs via genetic programming by considering the quality attributes of subparts. We investigate integrating quality transformation models into an AJSSP. Through the use of the de facto standard multi-objective evolutionary algorithm, NSGA-II, and a novel genotype to handle the constraints, we describe an evolutionary approach to optimizing all stated objectives. This approach is empirically shown to outperform random search and hill climbing in both performance and usability metrics expected to be valuable to administrators involved in plant scheduling and operations.

关键词： Assembly Job Shop Scheduling evolutionary algorithm Genetic programming Manufacturing multi-objective evolutionary algorithm

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A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

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Environment Systems and Decisions 2022年第2期42卷 177-188页

作者： Antonello, Federico Baraldi, Piero Zio, Enrico Serio, Luigi Energy Department Politecnico di Milano Via Lambruschini 4 Milan 20156 Italy MINES ParisTech PSL Research University CRC Sophia Antipolis 75006 France Technology Department CERN Geneva 23 1211 Switzerland

In this work, a multi-objective evolutionary algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms;this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies. © 2022, The Author(s).

关键词： Abnormal behaviours Alarm data Complex technical infrastructures Functional dependencies Genetic algorithm multi-objective evolutionary algorithm Novelty search Particle accelerator

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A multi-objective evolutionary algorithm Based on Cone Heart Point Adjustment Vectors to Solve Concave-convex Problems 34

A Multi-objective Evolutionary Algorithm Based on Cone Heart...

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34th Chinese Control and Decision Conference (CCDC)

作者： Li, Xin Li, Xiaoli Wang, Kang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Beijing Key Lab Computat Intelligence & Intellige Engn Res Ctr Digital Commun Beijing 100124 Peoples R China

ISBN: (纸本)9781665478960

Decomposition-based multi-objective optimization algorithms have been shown to be successful in solving multi-objective optimization problems (MOPs). Since the shape is unknown before the search, the classical decomposition method relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. However, the the distribution of reference vectors produced by the Das and Dennis systematic approach is difficult to maintain the diversity of (extremely) concave and (extremely) convex problems. To remedy this issue, a multi-objective evolutionary algorithm based on cone heart point adjustment vectors is proposed to solve concave and convex problems (CHP-MOEA). In this paper, the concavity or convexity is first according to the intermediate objective vector. Then, the distribution of reference vectors are adjusted according to the cone heart point on the basis of the traditional vectors. Finally, experimental results on 31 benchmark problems show that the proposed algorithm exhibits better performance than other state-of-the-art algorithms.

关键词： Cone heart point Concavity or convexity multi-objective evolutionary algorithm

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Modifying Belief Propagation MIMO Detector with multi-objective evolutionary algorithm 5

Modifying Belief Propagation MIMO Detector with Multi-Object...

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5th Information Communication Technologies Conference (ICTC)

作者： Wang, Zihe Tan, Xiaosi Zhou, Wenyue Huang, Yongming Zhang, Chuan Southeast Univ Lab Efficient Architectures Digital Commun & Sign Natl Mobile Commun Res Lab Nanjing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9798350373530;9798350373523

This paper proposes modified-BP (mBP), a novel approach that leverages a multi-objective evolutionary algorithm (MOEA) to enhance belief propagation (BP) for massive multi-pleinput multiple-output (MIMO) detection. mBP optimizes the BP algorithm by determining the optimal search space size and number of iterations for improved computational efficiency. Numerical results demonstrate the significant reduction in computational complexity of mBP compared to the state-of-the-art belief-selective propagation (BsP) algorithm. Additionally, mBP shows negligible performance degradation in most scenarios, with some configurations even exhibiting slight performance enhancement, which highlights mBP's potential as an efficient and effective detector for MIMO systems.

关键词： Massive MIMO belief propagation multi-objective evolutionary algorithm NSGA-II

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