A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization

Lim, Wei Hong; Isa, Nor Ashidi Mat; Tiang, Sew Sun; Tan, Teng Hwang; Natarajan, Elango; Wong, C. J.; Tang, Jing Rui

doi:10.1109/access.2018.2878805

Cited by 26 publications

(12 citation statements)

References 40 publications

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“…PSO is modeling the cooperative behavior for the swarming of birds or fish. Besides, the PSO procedure has been effectively implemented across a wide area, such as forecasting of traffic flow [27], optimization power losses in the distribution system [28], the self-adaptive mechanism [29], and fault diagnosis in power transformers [30].…”

Section: A Fault Feature Extraction By Saementioning

confidence: 99%

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

2021

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The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unlabeled and difficult to be identified by traditional fault approaches. Hence, in the present study, a novel deep learning (DL) framework is proposed to fill the gap by balancing the three stages of fault feature extraction, fault detection, and parameter optimization based on the long short term memory-recurrent neural networks (RNN-LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques. The suggested framework focuses on failure detection through a sequence of numerous features for the unlabeled historical data and unknown anomaly. To validate the effectiveness of the proposed DL framework, an experiment for failure detection of the electrical generator was conducted for the data of risky environment at Yemen oil and gas plant. The experimental results compared with the earlier studies validate that, the DL framework can address the faults for vibration signals of the electrical generator in a well-diagnosis performance effectively. INDEX TERMSDeep learning (DL), Fault detection, Long short-term memory (LSTM), Oil and gas plant, Recurrent neural networks (RNN), Stacked autoencoders (SAE) This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: A Fault Feature Extraction By Saementioning

confidence: 99%

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

2021

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“…Exploration can prevent particles from falling into a local optimum, but it also leads to low convergence accuracy and a slow convergence speed. Exploitation can accelerate the convergence speed and improve the convergence accuracy, but it makes the algorithm converge prematurely or become trapped in a local optimum easily [ 29 ]. These two functions greatly influence the performance of PSO.…”

Section: Particle Swarm Optimization Algorithmsmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

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“…Opposite mechanisms were observed from PSO with increasing topology connectivity (PSO-ITC) [43] because all particles were initially connected with the ring topology and their connectivity were gradually increased throughout the search process until all particles were fully connected. Two PSO variants known as PSO with adaptive time varying topology connectivity (PSO-ATVTC) [44] and PSO with self-adaptively topologically connected-based PSO (SATCPSO) [45] were proposed to further enhance the performance of PSO-ITC. In contrary to PSO-ITC, the topology modification schemes incorporated into both PSO-ATVTC and SATCPSO can offer better flexibility because the particles can choose to increase, decrease, maintain or shuffle their neighbourhood members based on their track records.…”

Section: ) Modification Of Neighborhood Structuresmentioning

confidence: 99%

Hovering Swarm Particle Swarm Optimization

2021

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PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.

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A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization

Cited by 26 publications

References 40 publications

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Hovering Swarm Particle Swarm Optimization

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