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An improved clustering based multi-objective evolutionary algorithm for influence maximization under variable-length solutions

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KNOWLEDGE-BASED SYSTEMS 2022年第0期256卷

作者： Biswas, Tarun K. Abbasi, Alireza Chakrabortty, Ripon K. Univ New South Wales Sch Engn & Informat Technol Canberra 2600 Australia Jashore Univ Sci & Technol Dept Ind & Prod Engn Jashore 7408 Bangladesh

Knowledge of influential actors on real networks is essential for designing policies for boosting the spread of positive things (e.g., news and innovation) and preventing the spread of negative things (e.g., disease and misinformation). Finding an optimum set of nodes with maximum influence, known as Influence Maximization (IM), aims to achieve this, which requires consideration of both magnitude of the influence spread and other important factors such as the cost and size of the seed set. However, most previous research has viewed the IM problem as a single objective problem, wherein decision-makers are required to make decisions on other criteria without having a clear view of them. In this study, the IM problem is formulated as a multi-objective Influence Maximization Problem (MIMP) with three conflicting objectives and real-world constraints. An improved version of theta-Dominance based evolutionary algorithm, named as theta DEA-II, is proposed to solve the MIMP. To achieve a better balance between divergence and convergence of the proposed theta DEA-II, a gap-based selection operator is introduced along with some modifications in the sorting and normalization process. Moreover, because the proposed MIMP deals with variable-length solutions, all of the investigated algorithms, including the baseline algorithms, are constructed with a novel crossover mechanism capable of producing offsprings that are different in size from their parents. Experimental results on six real-world datasets demonstrate that the proposed MIMP formulation as well as the theta DEA-II algorithm are effective for finding an optimal solution. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Influence maximization multi-objective evolutionary algorithm Variable-length solutions ?-dominance Social network analysis

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Decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies for shop floor multi-crane scheduling problems

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10719-10739页

作者： Zhou, Binghai Liao, Xiumei Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

Since the environment-friendly production has attracted extensive attention of many manufacturing enterprises, the energy consumption has become one of the core indices to evaluate production processes. Under this circumstance, the main purpose of this paper is to propose an efficient scheduling method for multi-objective shop floor multi-crane scheduling problems. In this research, the objectives of minimizing total weighted tardiness and total energy consumption are considered simultaneously. Owing to the NP-hard nature of the investigated problem, an improved decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies (2-echelon iMOEA/D) is developed to solve the problem. The upper echelon optimizer of the proposed algorithm extends the advantages of computing resources allocation and adaptive neighborhood adjustment to accelerate the convergence speed, while the lower echelon optimizer utilizes energy-efficient local search strategies to realize deep optimization of the algorithm. The performance of the proposed method is compared with two other high-efficient multi-objective optimization algorithms. The computation results indicate that the proposed 2-echelon iMOEA/D achieves better performance both on solutions' quality and diversity.

关键词： multi-objective evolutionary algorithm Energy-efficient local search strategy Shop floor multi-crane scheduling Deep optimization

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multi-objective Optimal Reactive Power Dispatch of Power Systems by Combining Classification-Based multi-objective evolutionary algorithm and Integrated Decision Making

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IEEE ACCESS 2020年 8卷 38198-38209页

作者： Zhang, Meng Li, Yang Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (CPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using CPSMOEA;in the second step, integrated decision-making by combining fuzzy c-means algorithm (FCM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PICEAg) and the third evolution step of generalized differential evolution (GDE3).

关键词： Optimization Decision making Reactive power Linear programming evolutionary computation Security Optimal reactive power dispatch multi-objective evolutionary algorithm integrated decision-making best compromise solution fuzzy c-means algorithm grey relation projection

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Discovering generalized design knowledge using a multi-objective evolutionary algorithm with generalization operators

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 143卷 1页

作者： Bang, Hyunseung Selva, Daniel Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA

The early-phase design of complex systems is a challenging task, as a decision maker has to take into account the intricate relationships among different design variables. A popular way to help decision makers easily identify important design features is to use data mining. However, many of the existing algorithms output design features that are too complex (e.g., conjunction of many literals with unrelated predicates), making it difficult for a user to understand, remember, and apply these features to find better designs. In this paper, we introduce a new data mining method that extracts compact design features through knowledge generalization. The proposed method performs a search over the space of features using a multi-objective evolutionary algorithm that contains a set of generalization operators in addition to conventional evolutionary operators. Both variables and feature types are generalized by using an ontology defining a set of domain-specific concepts and relationships. Generalization leads to more compact and insightful features, as generalized knowledge encompasses wider concepts. A comparative experiment is conducted on a real-world system architecting problem to demonstrate the gain in compactness of the extracted features without significant reductions in predictive power. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Design space exploration Knowledge discovery Feature extraction Data mining multi-objective evolutionary algorithm Adaptive operator selection

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multi-objective evolutionary algorithm FOR IMAGE SEGMENTATION

MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR IMAGE SEGMENTATIO...

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IEEE SoutheastCon Conference

作者： Abeysinghe, Wajira Wong, Michael Hung, Chih-Cheng Bechikh, Slim Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA Anyang Normal Univ Anyang Henan Peoples R China Univ Tunis Comp Sci Dept ISG Campus Tunis Tunisia

ISBN: (纸本)9781728101378

Clustering is an unsupervised learning technique commonly used for image segmentation. As the outcome of most clustering algorithms is heavily dependent on the initial cluster centers, it is necessary to consider optimization during the process of segmentation. The multi-objective evolutionary algorithm (MOEA) was used for optimization in this study, to find optimal cluster centers. It is important to note that the effectiveness of MOEA is dependent upon the selection of objective functions. Two objectives were considered;namely, the minimization of intra-cluster compactness and the maximization of inter-cluster separation to determine the optimal initial cluster centers. Xie-Beni index (XBI) was used to measure the compactness and separation of cluster centers while the Average Inter-Cluster Separation (AIS) measure ensures the minimal overlapping of clusters. The MOEA will generate a set of non-dominated solutions. The Davies-Bouldin Index (DBI) is then employed to determine the most optimal solution for the cluster centers. Experimental results demonstrate that this method of image segmentation performs better than single-objective optimization (SOO)and Possibilistic Clustering algorithm (PCA).

关键词： possibilistic clustering algorithm multi-objective evolutionary algorithm pareto-optimal cluster validity measures image segmentation

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A multi-objective evolutionary algorithm for steady-state constrained multi-objective optimization problems

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APPLIED SOFT COMPUTING 2021年 101卷 107042-107042页

作者： Yang, Yongkuan Liu, Jianchang Tan, Shubin Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Northeastern Univ Dept Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Many multi-objective evolutionary algorithms (MOEAs) are developed to solve constrained multiobjective optimization problems (CMOPs). However, they encounter low efficiency for steady-state CMOPs which are to optimize a known feasible solution named the current steady-state operation point. This paper proposes a multi-objective evolutionary algorithm (MOEA), termed FACE, for tackling the steady-state CMOPs. In FACE, the known feasible solution is maintained in the second population and evolves together with the main population. The main population is evolved by global search without handling constraints to accelerate the convergence. And the second population is evolved by local search to hold and achieve more feasible solutions. Two kinds of performance comparison between FACE and five representative MOEAs are made: the first using two cases of steady-state multisource compressed-air pipeline optimization problems to evaluate the performance of FACE on real life applications, and the second using a set of bench-mark test problems of CMOPs assigned with a known feasible solution to further assess the scalability of FACE. The results demonstrate the efficiency and the scalability of FACE with a potential for steady-state CMOPs. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Steady-state constrained multi-objective optimization Co-evolution

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

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

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第34届中国控制与决策会议

作者： Xin Li Xiaoli Li Kang Wang Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

Decomposition-based multi-objective optimization aliorithms 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 ***,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 *** 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 ***,the distribution of reference vectors are adjusted according to the cone heart point on the basis of the traditional ***,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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An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery

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KNOWLEDGE-BASED SYSTEMS 2021年 218卷 106881-106881页

作者： Zou, Wen-Qiang Pan, Quan-Ke Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Matrix manufacturing workshop Automated guided vehicles Scheduling Pickup and delivery multi-objective evolutionary algorithm

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An interactive filter-wrapper multi-objective evolutionary algorithm for feature selection

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SWARM AND evolutionary COMPUTATION 2021年 65卷 100925-100925页

作者： Liu, Zhengyi Chang, Bo Cheng, Fan Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal ProcMinist Edu Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China

As an important task in data mining, feature selection can improve the performance of classification by eliminating the redundant or irrelevant features in original data. It is mainly divided into filter method and wrapper method, and each one has its own advantages. To make full use of the advantages of two methods, in this paper, an interactive filter-wrapper multi-objective evolutionary algorithm, named GR-MOEA is proposed, where guiding and repairing strategies are used to select feature subsets with high quality. To be specific, a wrapper population and a filter population are evolved simultaneously in the proposed algorithm. To utilize the merits of two populations, an interactive scheme is designed, which includes a wrapper to filter guiding strategy and a filter to wrapper repairing strategy. The guide strategy is to use the good solutions in the wrapper population to steer the filter population towards a better direction. While in the repairing strategy, some features in the wrapper population are repaired by the useful information in filter population, which can avoid the trapping of local optimum in wrapper population. To further enhance the performance of GR-MOEA, two effective initialization strategies are also developed. Empirical studies are conducted by comparing the proposed algorithm with several state-of-the-art on different datasets, and the experimental results demonstrate the superiority of GR-MOEA over the comparison methods in obtaining the feature subsets with higher qualities.

关键词： Feature selection multi-objective evolutionary algorithm Guiding strategy Repairing strategy Initialization strategy

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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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