Description of the states in both before updating and after updating.
Figure gives an example to illustrate our algorithm. Part (a) gives the state of before updating. Part (b) explains the state of ...after updating. Assume there are five peaks in the search space, numbered 1–5 in Part (b). However, only four peaks are detected by the current population, numbered 1–4 in Part (a).
Where, ⊕ denotes a replacer from the memory, Δ denotes a new generated particle around the replacer, and ⊗ denotes a replaced particle in the population. We can see that no more than one particle is replaced in each sub-population.
Since ⊕ in peak 5 is better than the closest seed, i.e. the seed in sub-population 2, and its distance to the closest seed is larger than rs, the sub-population 5 is created to exploit this area.
In sub-population 3, ⊕ is better than the closest seed, i.e. the seed in sub-population 3, and the distance between it to the closest seed is larger than 0.5×rs and less than rs, so only one Δ is created around the ⊕.
In sub-population 2, ⊕ is better than the closest seed, i.e. the seed in sub-population 2, but the distance between it to the closest seed is less than 0.5×rs, so only ⊕ is added to this sub-population.
There is one case that has not been described in this example. Here we suppose the replacer is not better than the worst particle in sub-population 1, so no replacement is conducted in this sub-population.
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•A new population updating method is proposed to enhance a representative algorithm, i.e. the Species-based Particle Swarm Optimization.•Experimental results show that the MSPSO is competitive on MPB, CMPB and DRGDB.•The effect of the memory size on the performance of the proposed algorithm is tested.
Both the species strategy and the memory scheme are efficient methods for addressing dynamic optimization problems. However, the combination of these two efficient techniques has scarcely been studied. Thus, this paper focuses on how to hybridize these two methods. In this paper, a new swarm updating method is proposed to enhance a representative species-based algorithm, i.e., SPSO (Species-based Particle Swarm Optimization), and the new algorithm is named MSPSO. MSPSO has two characteristics. First, the number of replaced particles in the current swarm is set adaptively according to the number of species. To not substantially destroy the exploitation capability of each species, no more than one particle in each species is replaced by the memory. Second, the retrieved memory particles are categorized according to their fitness values and their distances to the seed of the closest species. Aimed at enhancing the search in both promising areas and existing species, each category is processed by different operations. The MPB, Cyclic MPB and DRPBG are used to test the performance of MSPSO. Experimental results demonstrate that MSPSO is competitive for dynamic optimization problems.
Linking ambiguous entity mentions in a text with their true mapping entities in a heterogeneous information network (HIN) is important. Most of existing entity linking methods with HINs assume that ...the entities in a text are independent while ignoring the relationships between the entities in context. Recent studies have shown that collective entity linking methods are more effective than traditional independent entity linking methods because they consider the relationships between different entities in the same text. However, few studies focus on collective entity linking for HINs. Most of collective entity linking methods rely largely on special features in Wikipedia, and may not be suitable for the HINs that are not mapped to Wikipedia. Moreover, existing collective entity linking methods may have high time complexity. Therefore, a Coarse-to-Fine collective Entity Linking algorithm (called CFEL) is proposed for the case the Wikipedia cannot be used. CFEL is composed of a coarse-grained model and a fine-grained model. In the coarse-grained model, a pruning strategy motivated by the human cognition mechanism, is adopted to reduce the number of candidates for each entity mention in texts. The candidates in HINs that are inconsistent with the type of entity mentions can be deleted. In the fine-grained model, we present a probabilistic method that combines the semantic information in a text with the structural information in HINs. The experimental results on four real-world datasets verify the effectiveness of our algorithm compared to the baselines.
Knowledge Graph for China's Genealogy Wu, Xindong; Jiang, Tingting; Zhu, Yi ...
IEEE transactions on knowledge and data engineering,
2021
Journal Article
Community Detection by Fuzzy Relations Luo, Wenjian; Yan, Zhenglong; Bu, Chenyang ...
IEEE transactions on emerging topics in computing,
04/2020, Volume:
8, Issue:
2
Journal Article
Peer reviewed
The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering community structure from a network is one of the classic and significant problems faced ...in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm based on fuzzy relations, i.e., CDFR (Community Detection by Fuzzy Relations), is proposed for non-overlapping community detection. The key idea of CDFR is to find the NGC node (Nearest node with Greater Centrality) for each node and compute the fuzzy relation between them. Then, the community to which a node belongs depends on its NGC node. In addition, the decision graph will be constructed to guide community detection. Experimental results on artificial and real-world networks verify the effectiveness and superiority of our CDFR algorithm.
Graph edge partitioning (GEP), the allocation of edges into different parts through cut vertices, is essential for the analytics of large-scale graphs. Most GEP models cannot be directly applied to a ...time-varying graph unless repartitioning the entire graph, which leads to a large consumption of resources. Although a few studies have focused on time-varying graph edge partitioning, they have ignored the memory consumption during the partitioning process. Therefore, a lightweight edge partitioner, referred to as LocalTGEP, broadening the application to time-varying graphs, is proposed herein. Three superiorities of LocalTGEP are highlighted as follows: 1) A satisfactory partitioning quality for a time-varying graph can be achieved without requiring global information owing to the local edge partitioning. 2) Memory consumption of the partitioner is significantly reduced using a novel storage framework of graph data in LocalTGEP. 3) The quality and efficiency of time-varying graph edge partitioning are optimized by designing the push and pop stages in LocalTGEP. Extensive experimental results obtained on 12 real-world graphs demonstrate that LocalTGEP outperforms rival algorithms in terms of memory consumption, partitioning quality, and efficiency.
Fusing data from different sources to improve decision making in smart cities has received increasing attention. Collected data through sensors usually exist in a multi-modal form, such as values, ...images, and texts. Thus, designing models that handle multi-modal data has an important role in this field. Meanwhile, security and privacy issues cannot be ignored, as the leakage of big data may provide opportunities for criminals. To solve the above challenges, we focus on research on multi-modal entity alignment for knowledge graphs and proposed the Multi-Modal Interaction Entity Alignment model (MMIEA). The model is proposed from the perspective of fusing data from different modalities while maintaining privacy. We determined that the model is privacy-preserving because it does not need to transmit the raw data of each modality (only the vector representation is transmitted). Specifically, we introduce and improve the BERT-INT model for the entity alignment task in multi-modal knowledge graphs. Experimental results on two commonly used multi-modal datasets show that our method outperforms 17 algorithms, including nine multi-modal entity alignment methods.
•The perspective of fusing data from different modalities while maintaining privacy.•An interactive approach to capture the image information of neighboring entities.•Experimental results on two multi-modal datasets outperform 17 compared algorithms.
In evolutionary dynamic optimization (EDO), most of the existing studies have assumed that dynamic optimization problems are black boxes. However, for many real-world problems, the dynamic parameters ...that cause the problems to change are observable. However, determining the utility of these parameters in improving optimization performance has not yet been well studied. In this paper, we propose and compare three strategies for this task: rote learning, fitting data with a feedforward neural network and an ensemble strategy. The main idea of these strategies is to learn the relation between the observable parameters and the optimal solutions and then predict new optima once the environment changes. We also propose a set of test cases representing different kinds of characteristics of real-world problems. In the experiments, the proposed strategies are compared with existing methods that do not use observable parameters, and the results validate our proposed strategies.
Existing population-based Stochastic Search Algorithms (SSAs) are too time-consuming to solve dynamic optimal power flow (OPF). The solution proposed in this paper is to accelerate SSAs with memory. ...Two memory schemes, the similarity retrieval scheme and the mean-based immigrants scheme, are proposed and applied together to the Differential Evolution and Particle Swarm Optimizer, which are two representatives of SSAs. Experiments are conducted on modified IEEE 30-bus and IEEE 118-bus systems with changing load buses and the objective of minimizing real power transmission loss. The results show that the proposed schemes significantly improve the performance of the two existing algorithms, and that SSAs could be practical for tracking optima of dynamic OPF.
As an emerging topic on preference learning, aiming at deducting the linear order of alternatives from the partial ranking, preference completion is to complete the preference of the target agent to ...form a linear order from the preferences of other agents under certain complex requirements. In order to improve the effectiveness and efficiency of preference completion in Big Data environments, firstly the preference graph is introduced to represent the collective preference of the agents over the alternatives with a certain consensus algorithm following the preference of the target agent. This preference graph can preserve rich information between agents. In addition, with the introduction of fuzzy ranking, it can illustrate the fuzziness of the target agent that can include several ranking options of the target agent over alternatives. Then, the satisfied preference can be matched from the preference graph with the fuzzy ranking requested by the target agent via isomorphism-based graph pattern matching. With the matched preference, the preference of the target agent can be completed. If the completed preference is not satisfied, the target agent can modify the fuzzy ranking, process the graph pattern rematching and complete the preference again. The experimental results show that with several real datasets the effectiveness and efficiency of the fuzzy ranking-based preference completion via graph pattern matching can be validated.
