Article
Computer Science, Artificial Intelligence
Kayvan Asghari, Mohammad Masdari, Farhad Soleimanian Gharehchopogh, Rahim Saneifard
Summary: PSO and WOA are two popular optimization algorithms with different strategies and issues, but by hybridizing and optimizing with chaotic maps, the algorithms can explore more effectively and avoid local optima in the problem search space.
Article
Computer Science, Artificial Intelligence
Ming Li, Linhao Huang, Gangyan Xu, Kong Biao
Summary: This paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools to improve the computing efficiency of PSO in parallel execution. Experimental results show that PAPSO significantly improves PSO's computing efficiency compared to OpenMP and achieves linear speedup. Additionally, nonblocking communication and work-stealing mechanisms are effective in maintaining computing elapsed time and improving performance.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Omer Ali, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri, Imran Ashraf
Summary: This study introduces a competitive coevolution process to enhance the capability of Phasor PSO (PPSO) for global optimization problems. Experimental results show that the improved competitive multi-swarm PPSO (ICPPSO) algorithm achieves a dominating performance, with average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO.
Article
Mathematics
Martin Montes Rivera, Carlos Guerrero-Mendez, Daniela Lopez-Betancur, Tonatiuh Saucedo-Anaya
Summary: Optimizing large-scale numerical problems is challenging, but our proposed improved PSO algorithm (DSRegPSO) has achieved significant success in reducing stagnation in local optimal regions.
Article
Computer Science, Artificial Intelligence
Malik Sh. Braik, Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Abdelaziz I. Hammouri, Raed Abu Zitar
Summary: An Enhanced Chameleon Swarm Algorithm (ECSA) is proposed to solve non-convex Economic Load Dispatch (ELD) problems by integrating roulette wheel selection and Levy flight methods. The performance of ECSA is shown to outperform other methods on complex benchmark functions.
APPLIED INTELLIGENCE
(2023)
Review
Computer Science, Interdisciplinary Applications
Janmenjoy Nayak, H. Swapnarekha, Bighnaraj Naik, Gaurav Dhiman, S. Vimal
Summary: This article presents an in-depth analysis of the Particle Swarm Optimization (PSO) algorithm and its developments in different application domains. PSO is highly popular due to its simple structure and few algorithmic parameters, and it has shown excellent performance in areas such as networking, robotics, and image segmentation. The paper discusses the evolution of PSO and its improved variants, providing a scope for further development and inspiring researchers and practitioners to find innovative solutions for complex problems in various domains using PSO.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Mehmet Beskirli
Summary: The Tree seed algorithm (TSA) is inspired by the relationship between trees and seeds, but it performs poorly for high-dimensional problems. A new TSA based on the roulette wheel strategy (R-TSA) has been proposed to solve high-dimensional problems by diversifying trees and updating seed locations to scan the search space more effectively. Experimental results show that the performance of R-TSA is higher than TSA.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Kamal Z. Zamli, Hussam S. Alhadawi, Fakhrud Din
Summary: This paper introduces a new variant of a metaheuristic algorithm based on the Social Network Search algorithm, called the Roulette Wheel Social Network Search algorithm. The RWSNS algorithm allows proportionate selection of search operators through the use of a roulette wheel. It also incorporates the Piecewise map for high nonlinearity and offers systematic manipulation of candidate solutions for intensification. Experimental results show that RWSNS outperforms other algorithms in S-box criteria and maintains commendable performance in other criteria.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ankita, Sudip Kumar Sahana
Summary: This paper proposes a new balanced PSO algorithm to solve the scheduling problem of computational grid. The algorithm is evaluated using a standard dataset, and its results outperform other considered deterministic and heuristic approaches.
APPLIED INTELLIGENCE
(2022)
Article
Chemistry, Analytical
Suganya Selvaraj, Eunmi Choi
Summary: This paper proposes an improved PSO algorithm, called dynamic sub-swarm PSO, for text document clustering problems. The experimental results show that this algorithm outperforms standard PSO and K-means algorithms in terms of purity and execution time.
Article
Computer Science, Interdisciplinary Applications
Davoud Sedighizadeh, Ellips Masehian, Mostafa Sedighizadeh, Hossein Akbaripour
Summary: The Particle Swarm Optimization (PSO) algorithm, a nature-inspired meta-heuristic, has evolved into various variants due to its flexibility in parameters and concepts. The Generalized Particle Swarm Optimization (GEPSO) algorithm enriches the original PSO by incorporating new terms and dynamic inertia weight updates, leading to improved performance in continuous space optimization.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
Article
Automation & Control Systems
Izaz Ur Rahman, Zidong Wang, Weibo Liu, Baoliu Ye, Muhammad Zakarya, Xiaohui Liu
Summary: This study presents a novel N-state Markovian jumping PSO algorithm based on the evolution of states governed by a Markov chain, which outperforms some popular PSO algorithms in solving optimization problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Tareq M. M. Shami, Seyedali Mirjalili, Yasser Al-Eryani, Khadija Daoudi, Saadat Izadi, Laith Abualigah
Summary: Particle swarm optimization (PSO) is a well-regarded metaheuristic method, but it suffers from slow convergence and local optima entrapment. This study proposes a novel method called velocity pausing PSO (VPPSO) which allows particles to move with the same velocity as the previous iteration. VPPSO demonstrates superior performance in solving high-dimensional problems and can be applied to various real-world optimization problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Ruijie Wang, Xun Wen, Fangmin Xu, Zhijian Ye, Haiyan Cao, Zhirui Hu, Xiaoping Yuan
Summary: Device-to-device (D2D) communication is a promising wireless communication technology that reduces the traffic load of the base station and improves spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further enhance throughput, but interference suppression becomes more complex due to new links. This paper proposes a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization (PSO) to address this challenge.
Article
Computer Science, Information Systems
Daniel Leal Souza, Rodrigo Lisboa Pereira, Mario T. R. Serra Neto, Marco A. F. Mollinetti, Otavio Noura Teixeira, Roberto C. L. De Oliveira
Summary: The Multi-Swarm approach allows for the use of different configurations between two or more populations of particles to improve the optimization process. This article proposes a local/global stochastic interconnection method for the Multi-Swarm algorithm and introduces a local search method for refining previously obtained solutions. The performance of these approaches is evaluated using ten constrained engineering design optimization problems and compared to existing solutions in the scientific literature, showing significant improvements.
Article
Automation & Control Systems
Chao Deng, Changyun Wen, Wei Wang, Xinyao Li, Dong Yue
Summary: This article investigates the consensus problem for high-order nonlinear multi-agent systems (MASs) with an uncertain leader under event-triggered communication. It introduces distributed intermediate parameter estimators based on event-triggered communication mechanism to estimate the unknown parameters of the uncertain leader. To ensure the existence of high-order derivatives, the estimators are modified by using the Hermite interpolation method. Moreover, novel high-order filters are proposed to generate local reference signals and ensure the existence of high-order derivatives of the filter states. A backstepping-based decentralized adaptive controller is developed based on the developed filters, and it is proved that consensus errors are asymptotically convergent with the developed method. Simulation examples are provided to demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Jianping Cai, Changyun Wen, Lantao Xing, Qiuzhen Yan
Summary: In this article, a decentralized adaptive control scheme is proposed based on backstepping techniques for uncertain interconnected systems with modeling errors and interactions. Unlike existing results, the functions bounding modeling errors and interactions are allowed to depend on all the states of the interconnected system, without the requirement of meeting the triangular condition. It is demonstrated that the proposed scheme can ensure bounded signals in the closed-loop system, which is verified by simulation studies.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Xiaolei Li, Changyun Wen, Ci Chen, Qianwen Xu
Summary: This article addresses the resilience problem in distributed secondary voltage and frequency control in an isolated AC microgrid in the presence of communication faults. A novel adaptive observer is proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults, with sufficient conditions derived to ensure stability and accurate power sharing regardless of the faults. Simulation results are provided to validate the effectiveness of the proposed secondary control approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Green & Sustainable Science & Technology
Lantao Xing, Yang Qi, Xiao-Kang Liu, Changyun Wen, Meiqin Liu, Yu-Chu Tian
Summary: DC microgrids have found wide use in industry due to their reliability, efficiency, and access to sustainable energy. Various distributed secondary control methods have been developed for these microgrids, but most rely on continuous-time controllers. This paper presents a discrete-time distributed secondary control strategy to account for the practical implementation of digital controllers. The proposed strategy ensures flexible current sharing, accurate voltage regulation, and can handle different types of loads. Simulation and experiments are used to demonstrate its effectiveness.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Automation & Control Systems
Xiaolei Li, Jinsong He, Xinyao Li, Changyun Wen
Summary: This paper proposes a resilient secondary control scheme to solve the voltage and frequency restoration problem caused by irregular time delays in an AC microgrid. Sufficient conditions are established using algebraic gains to ensure system stability and achieve asymptotic restoration. Simulation and experimental studies demonstrate the effectiveness of the proposed control scheme in practical applications.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Fanghong Guo, Chao Deng, Changyun Wen, Xiangkang Zheng, Zhijie Lian, Wentao Jiang
Summary: This paper designs a partially decentralized secondary controller to achieve current sharing and DC bus voltage restoration in islanded DC microgrid systems. Compared to existing centralized or distributed control methods, the proposed approach greatly reduces communication and implementation cost by only requiring DC bus voltage information in the local decentralized controller design. The theoretical analysis and case studies conducted in a real-time DC microgrid test system validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Automation & Control Systems
Xianfu Zhang, Xiandong Chen, Changyun Wen
Summary: This article investigates the global feedback regulation problems of triangular nonlinear systems perturbed by matched disturbances and modeling uncertainties with unknown system parameters. New integral controllers are constructed to ensure the boundedness and asymptotic convergence to zero of all states in the uncertain systems. The construction involves introducing an integral dynamic and a state transformation to convert the triangular nonlinear systems into auxiliary intermediate systems. The Lyapunov stability theorem and the characteristics of the designed dynamic parameter are used to demonstrate the global feedback regulation of the considered systems. Two physical system examples are provided to illustrate the effectiveness of the proposed controllers.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Xinyao Li, Changyun Wen, Xiaolei Li, Jinsong He
Summary: This article investigates the adaptive fractional-order backstepping control problem for high-order integer-order systems with uncertainties and unknown external disturbance. Fractional-order calculus is integrated into the conventional backstepping controller design procedure to improve control performance. Theoretical proof based on the Lyapunov stability theorem is provided to ensure the global stability of the closed-loop system. Numerical simulation and experimental results demonstrate the effectiveness of the proposed adaptive fractional-order backstepping control scheme.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Shuyan Zhou, Yongduan Song, Changyun Wen
Summary: This work focuses on event-triggered practical prescribed time tracking control for a type of uncertain nonlinear systems, considering actuator saturation, unmeasurable states, and time-varying unknown control coefficients. A state observer is constructed using neural network technology to estimate the unmeasurable system states, and an event-triggered output feedback control scheme is developed to steer the tracking error to a predefined accuracy within a preassigned settling time.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Chao Deng, Weinan Gao, Changyun Wen, Zhiyong Chen, Wei Wang
Summary: This article proposes a solution to the problem of resilient practical cooperative output regulation (RPCORP) for multiagent systems facing denial-of-service attacks and actuator faults. The article introduces a novel data-driven control approach to handle the unknown system parameters and develops resilient distributed observers for each follower under DoS attacks. It also presents a resilient communication mechanism and a time-varying sampling period to ensure neighbor state availability and prevent targeted attacks. By leveraging a data-driven algorithm, a model-based fault-tolerant and resilient controller is designed. Rigorous analysis proves the ability of the closed-loop system to achieve practical cooperative output regulation, and a simulation example is provided to demonstrate the effectiveness of the approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Civil
Xingshuo Hai, Huaxin Qiu, Changyun Wen, Qiang Feng
Summary: This paper presents a novel approach to address the problem of distributed situation awareness consensus for unmanned aerial vehicle swarm systems. Due to the complexity and antagonism of the mission environment, traditional centralized architectures fail to achieve distributed consensus. To tackle this issue, a systematic distributed SA consensus scheme is proposed, including a distributed optimization-based consensus reaching model, dual-loop decision-making framework, and novel coordination algorithm. Convergence analysis is conducted on the proposed algorithm, and comparative simulations confirm the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Chenliang Wang, Lei Guo, Changyun Wen, Yukai Zhu, Jianzhong Qiao
Summary: This article proposes new adaptive anti-disturbance control schemes for uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme guarantees asymptotic tracking and L$_{\infty}$ tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, allowing the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zongcheng Liu, Jiangshuai Huang, Changyun Wen, Xiaojie Su
Summary: In this article, the distributed control of uncertain multiagent systems (MAS) with unknown nonlinearities, time-varying control coefficients, and multiple control directions is investigated. A new theorem with novel Nussbaum functions is presented to solve the problem of controlling nonlinear systems with unknown time-varying control coefficients and signs. The global consensus of MAS with unknown time-varying control coefficients and system nonlinearities under a directed graph is achieved, and the transient tracking performance is guaranteed. A novel filter is proposed for each agent that does not require prior knowledge of the time derivative of the leader. Simulation results demonstrate the effectiveness of the proposed control schemes.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Zhen Gao, Yongduan Song, Changyun Wen
Summary: In this work, a neuroadaptive PI control scheme is developed for challenging MIMO nonlinear systems with uncertain models and actuation faults. By integrating filtered variables into the design process, the proposed control achieves asymptotic tracking, adjustable rate of convergence, and bounded performance. It is applicable to various MIMO systems with unknown and time-varying control gain matrices, and remains robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults with only one online updating parameter.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhen Gao, Yongduan Song, Changyun Wen, Frank L. Lewis
Summary: This paper investigates the problem of reliable control for nonlinear systems with uncertainties and abnormal sensoring and actuating units. By using an adaptive control solution and a strategy of backup actuators, the system can achieve stable operation even under sensor failures and malicious attacks.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)