Article
Computer Science, Information Systems
Kun Guo, Zhanhong Chen, Xu Lin, Ling Wu, Zhi-Hui Zhan, Yuzhong Chen, Wenzhong Guo
Summary: In this paper, a novel algorithm is proposed that combines label propagation, multi-objective particle swarm optimization, and graph attention variational autoencoder to achieve community detection. The label propagation strategy is used to speed up the evolution process of the swarm, and the optimal solutions found by the optimization algorithm are embedded into the objective of the autoencoder to improve the quality of embedding vectors. Experimental results show the feasibility and effectiveness of our algorithm compared to state-of-the-art algorithms.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Wenchao Jiang, Shucan Pan, Chaohai Lu, Zhiming Zhao, Sui Lin, Meng Xiong, Zhongtang He
Summary: A hybrid Collaborative Particle Swarm multiobjective Optimization-based Dynamic Overlapping Community Detection (CPSO-DOCD) algorithm is proposed in this paper to address the challenges in detecting overlapping communities in dynamic complex networks. The algorithm outperforms previous methods in terms of hypervolume values and C-metric values, and it can approach the Pareto frontier effectively.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Ying Yin, Yuhai Zhao, He Li, Xiangjun Dong
Summary: The paper proposes an efficient and effective multi-objective method, DYN-MODPSO, which addresses the issues in dynamic community detection by enhancing the traditional evolutionary clustering framework and particle swarm algorithm. The novel strategy and carefully designed operators contribute to the method's superior performance on both real and synthetic dynamic networks, outperforming competitors in terms of effectiveness and efficiency.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Beatriz C. Silva, Carine Menezes Rebello, Aliirio E. Rodrigues, Ana M. Ribeiro, Alexandre F. P. Ferreira, Idelfonso B. R. Nogueira
Summary: This study proposes a novel strategy for the design of adsorption heat pumps, which involves simultaneous optimization and material screening using the particle swarm optimization (PSO) approach. The proposed framework effectively evaluates different adsorbents and temperature intervals to find the optimal solution in terms of maximum performance and minimum heat supply cost. This approach provides a fast and intuitive evaluation of multiple design and operation variables.
Article
Engineering, Electrical & Electronic
Yu Zhou, Lin Gao, Dong Wang, Wenhui Wu, Zhiqiang Zhou, Tingqun Ye
Summary: In this study, an improved localized feature selection method based on multiobjective binary particle swarm optimization was proposed to address fault diagnosis by utilizing the local distribution of data without the need for balancing strategies.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Ahmed Mahdi Jubair, Rosilah Hassan, Azana Hafizah Mohd Aman, Hasimi Sallehudin
Summary: This study developed a new algorithm named SC-MOPSO for solving the challenging optimization problems with Multiobjective Optimization and Variable Length nature. Experimental results showed that SC-MOPSO outperformed benchmarks in terms of solving WSND problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Shouping Guan, Xiaoyu Yu
Summary: This article proposes a data-based interval neural network optimization modeling method for interval modeling of uncertain systems. The method combines interval analysis with a neural network and particle swarm optimization algorithm to model uncertain systems under an unknown-but-bounded error condition. Two optimization objectives, interval coverage and interval width, are constructed to improve the accuracy and reliability of prediction. The method effectively solves constraints such as the model structure and known error bounds, and provides a new approach to data-based interval system modeling. Experimental results demonstrate the effectiveness of the method in modeling linear and nonlinear systems.
Article
Physics, Multidisciplinary
Huidong Ling, Xinmu Zhu, Tao Zhu, Mingxing Nie, Zhenghai Liu, Zhenyu Liu
Summary: This paper proposes a parallel multiobjective PSO weighted average clustering algorithm based on Apache Spark. The algorithm divides the entire dataset into multiple partitions and caches the data in memory using distributed parallel and memory-based computing of Apache Spark. The local fitness value of each particle is calculated in parallel according to the data in each partition, reducing the communication of data in the network. Additionally, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results.
Article
Computer Science, Interdisciplinary Applications
Yifei Sun, Xin Sun, Zhuo Liu, Yifei Cao, Jie Yang
Summary: This study proposes a novel dynamic community detection algorithm based on particle swarm optimization, targeting the classification of nodes with similar attributes in networks that change over time. By calculating the resistance distance of each node, the core nodes in the network are identified and the constant community is formed by nodes associated with these core nodes. Knowledge gained from the evolution of core nodes in consecutive time steps is utilized to determine the constant community to be retained. Experimental results on various networks indicate the higher accuracy and stability of the proposed algorithm compared to other well-known algorithms.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Shihong Yin, Qifang Luo, Guo Zhou, Yongquan Zhou, Binwen Zhu
Summary: This paper proposes a hybrid equilibrium optimizer slime mould algorithm (EOSMA) to efficiently solve the inverse kinematics problem of complex manipulators. A multi-objective version of EOSMA (MOEOSMA) is also introduced. Experimental results comparing with other algorithms reveal that this method performs well in terms of accuracy and computation time.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Yanmin Liu, Shihua Wang, Xi Song, Jie Yang
Summary: In this paper, a novel multiobjective particle swarm optimization algorithm (RCDMOPSO) is proposed, which comprehensively considers spatial target and congestion information of particles. RCDMOPSO introduces a method called global proportional ranking (GPR) and combines it with cyclic distance to design novel external archive maintenance and global selection strategies. Experimental results show that RCDMOPSO outperforms other popular algorithms and is effective in tackling multiobjective optimization problems.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Xiu Kan, Yixuan Fan, Zhijun Fang, Le Cao, Neal N. Xiong, Dan Yang, Xuan Li
Summary: In this paper, a novel IoT network intrusion detection approach based on APSO-CNN is proposed, which optimizes CNN structure parameters using PSO algorithm and introduces a new evaluation method to compare with other algorithms. Experimental results validate the effectiveness and reliability of the proposed method.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Lingren Kong, Jianzhong Wang, Peng Zhao
Summary: Dynamic weapon target assignment (DWTA) is an effective method for solving the multi-stage battlefield fire optimization problem, with a meaningful and effective model established in this paper. The model includes conflicting objectives of maximizing combat benefits and minimizing weapon costs, as well as various constraints. An improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed to solve the complex DWTA problem, showing better convergence and distribution compared to other state-of-the-art algorithms in experimental results.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics
Carine M. Rebello, Marcio A. F. Martins, Daniel D. Santana, Alirio E. Rodrigues, Jose M. Loureiro, Ana M. Ribeiro, Idelfonso B. R. Nogueira
Summary: This work presents a novel approach for multiobjective optimization problems, introducing the concept of the Pareto region to efficiently portray optimal conditions. By applying a clustering strategy, a balanced approach between objectives can be achieved, providing valuable insights for decision-making in process optimization. Benchmark results have shown the effectiveness of the proposed method in illustrating Pareto regions, demonstrating its potential impact on processes optimization and operation decision-making.
Article
Physics, Applied
Xinyue Zhou
Summary: This study introduces a fuzzy community detection algorithm based on pointer and adjacency list, which has been verified for correctness and suitability in experiments. The algorithm can store community partition structure and membership values, showing good performance for large-scale network applications.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2021)