PSO-ANE: Adaptive Network Embedding With Particle Swarm Optimization

Chen, Jinyin; Wu, Yangyang; Xu, Xuanheng; Zheng, Haibin; Ruan, Zhongyuan; Xuan, Qi

doi:10.1109/tcss.2019.2914935

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…The track to which the iroute is reassigned is determined using the following cost function. addCost(ir, tr)= 0.1 × wlCost ir,tr + α × overlapCost ir,tr +β × blkCost ir,tr + historyCost ir,tr (7) where wlCost ir,tr is wirelenth cost when iroute ir is on track tr. overlapCost ir,tr and blkCost ir,tr are the increased overlap cost and the increased blockage cost when ir is assigned to track tr, respectively.…”

Section: Definition 8 (History Cost)mentioning

confidence: 99%

“…Swarm Intelligence (SI) is an important category of optimization techniques and provides new ideas for solving a variety of complex operations optimization problems [2,3]. It is inspired by simple behaviors and self-organized interactions among intelligent individuals, such as ant colony foraging, bird flocking, flock effect, bacterial growth, and fish swarming [4][5][6][7][8][9]. Each SI technique has its unique advantages.…”

Section: Introductionmentioning

confidence: 99%

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SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

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In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). First, the proposed algorithm considers local nets to better guide detailed routing and an effective encoding method is designed to adapt the evolutionary algorithms better. Second, the social learning mode based on example pool mechanism is presented to improve the algorithm performance. Finally, a negotiation-based refining strategy is utilized to further reduce the overlap. Experimental results show that SLDPSO-TA can obtain the best overlap cost optimization among similar works, and reduce the congestion in key routing areas.

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Section: Definition 8 (History Cost)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Particle Swarm Optimization (PSO) algorithm, as a heuristic algorithm, is widely used in fields such as robot path planning, image recognition, vehicle scheduling, and flight planning [26]. The research in [27] introduces improvement strategies for the PSO algorithm, including introducing inertia weights, limiting maximum speed, and other strategies to improve the algorithm's search ability and optimization performance. It also analyzes the convergence and stability of the PSO algorithm.…”

Section: B Swarm Intelligence Algorithmmentioning

confidence: 99%

The GSO-Deep Learning-based Financial Risk Management System for Rural Economic Development Organization

Chen

2023

IJACSA

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Financial risk management has always been a key concern for major enterprises. At the same time, with the continuous attention to impoverished rural areas worldwide, financial risk management tools have become an important component of rural economic development organizations to avoid financial risks. With the rapid development of artificial intelligence technologies such as neural networks and deep learning, and due to their strong learning ability, high adaptability, and good portability, some financial risk management tools are gradually adopting technologies such as neural networks and machine learning. However, existing financial risk management tools based on neural networks are mostly developed for large enterprises such as banks or power grid companies, and cannot guarantee their full applicability to rural economic development organizations. Therefore, this study focuses on the financial risk management system used for rural economic development organizations. In order to improve the accuracy of deep learning algorithms in predicting financial risks, this paper designs an improved Glowworm Swarm Optimization (IGSO) algorithm to optimize Deep Neural Networks (DNN). Finally, the effectiveness of the financial risk management tool based on IGSO-DNN proposed in this article was fully validated using data from 45 rural economic development organizations as a test set.

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“…Dynamic multiobjective optimization problems (DMOPs) composed of conflicting objective functions are inevitably encountered in many real-world scenes [9], [14], [62], [66], [67], where both the objectives and constraints may change with time [5], [12], and this has attracted wide research attention to the design of effective dynamic multiobjective optimization algorithms (DMOAs) [6], [15], [20], [32], [34], [44]. The population-based evolutionary algorithms have been proven to be effective under various optimization scenarios in searching for the optimal solutions [2], [4], [7], [38], [40], [54], [56]. Particularly, owing to the wide existence of dynamic behaviors, the DMOAs are required to timely update the obtained Pareto solutions to ensure the convergence in each environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

Wang

Lan

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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In this paper, a novel dynamic multi-objective optimization algorithm (DMOA) is proposed based on a designed hierarchical response system (HRS). Named as HRS-DMOA, the proposed algorithm mainly aims at integrating merits from the mainstream ideas of dynamic behavior handling (i.e., the diversity-, memory-, and prediction-based methods) so as to make flexible responses to environmental changes. In particular, by two pre-defined thresholds, the environmental changes are quantified as three levels. In case of a slight environmental change, the previous Pareto set-based refinement strategy is recommended, while the diversity-based re-initialization method is applied in case of a dramatic environmental change. For changes occurring in a medium level, the transfer-learning-based response is adopted to make full use of the historical searching experiences. The proposed HRS-DMOA is comprehensively evaluated on a series of benchmark functions, and the results show an improved comprehensive performance as compared with four popular baseline DMOAs in terms of both convergence and diversity, which also outperforms other two state-of-the-art DMOAs in 10 out of 14 testing cases, exhibiting the competitiveness and superiority of the algorithm. Finally, extensive ablation studies are carried out, and from the results, it is found that as compared with randomly selecting the response methods, the proposed HRS enables more reasonable and efficient responses in most cases. In addition, the generalization ability of the proposed HRS as a flexible plug-and-play module to handle dynamic behaviors is proven as well.

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PSO-ANE: Adaptive Network Embedding With Particle Swarm Optimization

Cited by 13 publications

References 34 publications

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

The GSO-Deep Learning-based Financial Risk Management System for Rural Economic Development Organization

A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

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