Article
Automation & Control Systems
Guang He, Xiao-li Lu
Summary: In this paper, a modified quantum-behaved particle swarm optimization (QDQPSO) algorithm is proposed to solve the problems of low precision and getting trapped in local optima in high-dimensional complex problems. The algorithm utilizes quasi opposite-based learning in the initialization stage to improve search efficiency and convergence speed. The double evolutionary mechanism is applied to update individual locations during the iteration process for enhancing overall performance. Perturbation at global optimum position and bound constraint handling are considered to help escape local optima and maintain population diversity. Experimental results show that QDQPSO performs better in terms of accuracy and stability of optimal solutions compared to other optimization algorithms. Wilcoxon rank-sum test and Friedman test demonstrate significant advantages of the improved algorithm. QDQPSO also exhibits superior performance in solving five practical optimization problems compared to several optimization methods.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Huan Liu, Junqi Zhang, MengChu Zhou
Summary: This paper proposes an adaptive particle swarm optimizer that combines hierarchical learning with variable population to enhance the performance of the PSO algorithm. By introducing a heap-based hierarchy and adjusting the particle's level based on its current fitness, as well as eliminating redundant particles based on the population's evolution state, the swarm's exploratory and exploitative capabilities are improved.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mengmeng Sheng, Zidong Wang, Weibo Liu, Xi Wang, Shengyong Chen, Xiaohui Liu
Summary: This paper proposes a particle swarm optimizer with multi-level population sampling and dynamic p-learning mechanisms to address large-scale optimization problems. The mechanisms aim to balance exploration and exploitation, appropriately search the solution space, and maintain population diversity. Experimental results demonstrate the superior performance of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Baoxian Liang, Yunlong Zhao, Yang Li
Summary: This paper proposes a hybrid particle swarm optimization algorithm with crossover learning strategy and stochastic example learning strategy to address the balance between local exploitation and global exploration capabilities of PSO. Experimental results show that the algorithm exhibits competitive performance on multiple test suites and real-world problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Mathematical & Computational Biology
Xuepeng Zheng, Bin Nie, Jiandong Chen, Yuwen Du, Yuchao Zhang, Haike Jin
Summary: The paper proposes an improved particle swarm optimization algorithm combined with double-chaos search (DCS-PSO). DCS-PSO narrows the search space and enhances population diversity by incorporating a double-chaos search mechanism and a logistic map. Experimental results demonstrate that DCS-PSO achieves better convergence accuracy and speed in most cases.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Automation & Control Systems
Liangliang Zhang, Sung-Kwun Oh, Witold Pedrycz, Bo Yang, Lin Wang
Summary: In this study, a novel promotive particle swarm optimizer with double hierarchical structures is proposed. The method utilizes successful mechanisms from social and biological systems to ensure fair competition among particles. Experimental results demonstrate that the proposed method improves accuracy and convergence speed, particularly in solving complex problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Sajjad Molaei, Hadi Moazen, Samad Najjar-Ghabel, Leili Farzinvash
Summary: The article introduces a new variant of PSO algorithm, PSOLC, which enhances its search performance through improved learning strategy and crossover operator. By altering exemplar particles, updating parameters, and integrating with genetic algorithm, the algorithm shows significant improvements in exploration and exploitation capabilities.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Hadi Moazen, Sajjad Molaei, Leili Farzinvash, Masoud Sabaei
Summary: This paper proposes a particle swarm optimization algorithm called PSO-ELPM, which balances the exploration and exploitation capabilities of PSO through elite learning, enhanced parameter updating, and exponential mutation operator. The algorithm uses the best-performing particles as exemplars to guide the optimization process and computes self-cognition coefficients based on these elites. It also ensures a smooth distribution of weight among the elites using the inverse of the cube root function, and applies an exponential mutation operator to determine the mutation probability per particle. Comparisons with 10 state-of-the-art PSO variants on the CEC 2017 benchmark functions show that the proposed algorithm achieves higher accuracy with acceptable time complexity.
INFORMATION SCIENCES
(2023)
Article
Physics, Multidisciplinary
Bozena Borowska
Summary: This study proposes a learning competitive swarm optimization algorithm (LCSO) based on particle swarm optimization method and competition mechanism, which improves the search process and achieves higher efficiency compared to other tested methods.
Article
Computer Science, Information Systems
Feng Wang, Xujie Wang, Shilei Sun
Summary: In this paper, a large-scale optimization algorithm called reinforcement learning level-based particle swarm optimization algorithm (RLLPSO) is proposed. RLLPSO constructs a level-based population structure to improve population diversity, employs a reinforcement learning strategy for level number control to enhance search efficiency, and introduces a level competition mechanism to enhance convergence ability. Experimental results demonstrate the superiority of RLLPSO compared to state-of-the-art large-scale optimization algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xinming Zhang, Qiuying Lin
Summary: This paper proposes an improved SL-PSO algorithm, called TLS-PSO, which enhances the optimization performance of PSO through the use of three learning strategies and a hybrid learning mechanism. Experimental results demonstrate that TLS-PSO outperforms state-of-the-art PSO variants and other algorithms on complex functions and engineering problems, indicating its superior performance and potential for practical problem-solving.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Software Engineering
Kang Liang, Xiukai Zhang, Oleg Krakhmalev
Summary: SLSL-QPSO is a software that improves the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers. Experimental results demonstrate its superior performance in finding better optimal solutions compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Dickson Odhiambo Owuor, Thomas Runkler, Anne Laurent
Summary: This paper introduces the research of swarm intelligence and gradual pattern mining, and proposes a numeric encoding method for gradual pattern candidates. Several meta-heuristic optimization techniques are applied to efficiently solve the problem of finding gradual patterns using the defined search space.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Electrical & Electronic
Zhiming Xu, Zhixi Feng, Qiang Wu, Shuyuan Yang
Summary: This article proposes a semi-supervised deep learning-based method called Particle Swarm Joint Classifier with an Autoencoder (PJCA) to deal with limited labeled data in fault diagnosis. It trains a classifier and an autoencoder using labeled and unlabeled data, respectively, and combines two loss functions to train these two models simultaneously. An optimization strategy based on the greedy algorithm and particle swarm optimization (PSO) is also proposed and applied to optimize the weights of the loss function. Experimental results on two popular rotating machinery datasets show that the proposed method achieves a classification accuracy of over 95% with no more than 20 labeled samples per class and the optimization strategy significantly improves classification accuracy while reducing the number of parameters.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Liming Guo, Jun Wang, Jianfeng Zheng
Summary: This study proposes a berth allocation problem considering the impact of weather conditions, develops a two-stage optimization method and a mixed-integer programming model to solve the problem, and designs an efficient particle swarm optimization algorithm with machine learning approach. Numerical experiments demonstrate the effectiveness of the proposed model and the efficiency of the algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
