Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning

Tang, Yichao; Wei, Bo; Xia, Xuewen; Gui, Ling

doi:10.1109/ssci44817.2019.9002680

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…For minor and significant conflicts, Controllers for PI with PSO optimization result in considerable damping plus high overshoot [25]. Although It converges quickly, it has poor accuracy [26][27]. XS Yang and others [28] compared both PSO and Bacteria Foraging Optimization (BFO), and PSO produced better results than (BFO).…”

Section: Literature Reviewmentioning

confidence: 99%

Mountain Gazelle Algorithm-Based Optimal Control Strategy for Improving LVRT Capability of Grid-Tied Wind Power Stations

Magdy,

Hasanien,

Sabry

et al. 2023

IEEE Access

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The large-scale wind energy conversion systems (WECS) based on a doubly fed induction generator (DFIG) have recently gained attention due to their numerous economic and technological advantages. However, the rapid integration of WECS with standing power networks severely influenced the system's reliability and stability; also, the DFIG rotor circuit experiences a substantial overcurrent due to grid voltage fluctuations. Indeed, these problems emphasize the significance of a DFIG's low-voltage ride-through (LVRT) capacity in maintaining the stability of the electrical grid during voltage fluctuations. To solve these challenges simultaneously, this research employs a metaheuristic optimization technique to regulate a doubly fed induction generator's (DFIG) operation via a wind turbine (WT) system. The article proposes a novel Mountain Gazelle Optimizer (MGO) to optimize the proportional-integral (PI) controller gains for the DFIG system's active and reactive power control to enhance the LVRT capability of Wind turbines linked to the power grid. In the proposed scheme, LVRT improvement is proportional to undershoot or overshoot, settlement time, and steady-state inaccuracy of voltage responses. The proposed control method is implemented in MATLAB by a detailed model of 9MW wind turbine, and its performance is validated and compared with traditional optimization control approaches. The suggested MGO method's efficacy is demonstrated by the assessment and comparison to classic optimization-based PI controllers under various fault scenarios. The simulation results show that the optimized control method improved performance in terms of three-phase terminal voltage output responses, active power, reactive power demand to networks, and DC-Link voltage.INDEX TERMS doubly fed induction generator, low-voltage ride-through capability, metaheuristic optimization, mountain gazelle optimizer, Wind energy conversion systems.

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Section: Literature Reviewmentioning

confidence: 99%

Mountain Gazelle Algorithm-Based Optimal Control Strategy for Improving LVRT Capability of Grid-Tied Wind Power Stations

Magdy,

Hasanien,

Sabry

et al. 2023

IEEE Access

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“…As a result, many remarkable advanced learning strategies [9][10][11][12][13][14] have been developed. Roughly speaking, existing learning strategies of PSO can be divided into two main categories, namely topology-based learning strategies [15][16][17] and exemplar construction based learning strategies [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

Yang

Gao

et al. 2022

Mathematics

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Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interacting variables and many wide and flat local basins. To alleviate this issue, this paper proposes a differential elite learning particle swarm optimization (DELPSO) by differentiating the two guiding exemplars as much as possible to direct the update of each particle. Specifically, in this optimizer, particles in the current swarm are divided into two groups, namely the elite group and non-elite group, based on their fitness. Then, particles in the non-elite group are updated by learning from those in the elite group, while particles in the elite group are not updated and directly enter the next generation. To comprise fast convergence and high diversity at the particle level, we let each particle in the non-elite group learn from two differential elites in the elite group. In this way, the learning effectiveness and the learning diversity of particles is expectedly improved to a large extent. To alleviate the sensitivity of the proposed DELPSO to the newly introduced parameters, dynamic adjustment strategies for parameters were further designed. With the above two main components, the proposed DELPSO is expected to compromise the search intensification and diversification well to explore and exploit the solution space properly to obtain promising performance. Extensive experiments conducted on the widely used CEC 2017 benchmark set with three different dimension sizes demonstrated that the proposed DELPSO achieves highly competitive or even much better performance than state-of-the-art PSO variants.

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The main challenges of adaptability of swarm intelligence algorithms

Рабійчук,

Фечан

2024

SBUNFU

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Analyzed three swarm intelligence algorithms, namely Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), Particle Swarm Optimization (PSO) and the adaptability of these algorithms to a dynamic environment. Firstly, the ACO algorithm was analyzed, the behavior of ants in nature, the purpose of the algorithm, and its shortcomings in a dynamic environment. Then the existing modifications of this algorithm to changing environments were investigated, namely AСO with dynamic pheromone updating (AACO), ACO with adaptive memory (ACO-AP), ACO with multi-agent system (MAS-ACO), ACO with machine learning algorithms (MLACO). The advantages and disadvantages of these modifications are also discussed in detail. The software tools that implement the functionality of this algorithm, such as AntTweakBar, AntOpt, EasyAnt have been mentioned. These software tools provide an opportunity to develop new modifications of the ACO algorithms and to study existing ones. Furthermore, the capabilities of the BCO algorithm were clarified and the behavior and parameters of this algorithm were described, its pros and cons in a dynamic environment were investigated. The following BCO modifications were considered: Group Bee Algorithm (GBA), Artificial Bee Colony (ABC), and open source software: PySwarms, PyABC. The third part of the article investigates the work of the PSO algorithm, its advantages and disadvantages of adaptation to dynamic environments. Dynamic Particle Swarm Optimization with Permutation (DPSO-P), Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning (DMS-P50-EL) are considered as modifications of PSO to adapt to dynamic environments. The libraries for work such as SciPy, DEAP, PyGAD, Particleswarm, JSwarm (has a wide API and well-written documentation), Dlib have been mentioned. Finally, a comparative table with the most important properties (resistance to environmental changes, complexity of implementation, the possibility of using for a UAV swarm, etc.) for all three algorithms was created, a brief description of similar articles comparing algorithms of swarm intelligence was also made, and the conclusions of the study were drawn.

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Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning

Cited by 3 publications

References 20 publications

Mountain Gazelle Algorithm-Based Optimal Control Strategy for Improving LVRT Capability of Grid-Tied Wind Power Stations

Mountain Gazelle Algorithm-Based Optimal Control Strategy for Improving LVRT Capability of Grid-Tied Wind Power Stations

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

The main challenges of adaptability of swarm intelligence algorithms

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