25 Years of Particle Swarm Optimization: Flourishing Voyage of Two Decades

Nayak, Janmenjoy; Swapnarekha, H.; Naik, Bighnaraj; Dhiman, Gaurav; Vimal, S.

doi:10.1007/s11831-022-09849-x

Cited by 75 publications

(28 citation statements)

References 352 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of using GA and PSOA for the optimal control of the nonlinear disorder system of cancer cells showed that GA method is excellent, and PSOA method is also a very successful method and its results are very close to the results of GA method (Mohammadi and Hejazi 2023). PSOA is the most successful optimization algorithm among the existing nature-inspired algorithms, such as GA, Differential Evolution, Firefly, Cuckoo and so on due to its high efficiency and ability to be adjusted in different dynamics (Nayak et al 2023.…”

Section: Theoretical Backgroundmentioning

confidence: 85%

Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

Kheirdast,

Jozi,

Rezaian

et al. 2023

Preprint

View full text Add to dashboard Cite

Considering the importance of "time" in the process of dispatching forces to reach the fire or accident site, GA or PSO models can be used as artificial intelligence alternatives. Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSOA) models can be used. This research shows which of these two models is more appropriate in this case study. With the hypothesis that GA and PSOA have positive effects on the allocation and scheduling of the stations, this research seeks to compare them in order to find which one of these two methods is more appropriate to shorten the time to reach fire/incident site in the Region 19 of Tehran. This is an applied type of research. Data analysis was carried out using NFPA standards and MATLAB software. The statistical population includes 8 fire stations and 250 personnel of the stations selected in a simple way, and the sampling volume was obtained using Morgan's table (n = 148). At first, the algorithm of dispatching forces to reach the site of fire/incident was designed and implemented based on PSOA, GA and the time to response the incident according to NFPA1720 standards. After writing the assumptions of the problem and running the mathematical model from nonlinear to linear, the data was entered into the MATLAB software, and finally by comparing the performance improvement of PSOA and GA, appropriate results were obtained. In order to efficiently assign and schedule fire stations to arrive at the site, a linear numerical programming model was presented with the aim of minimizing the arrival time and taking into account the effect of firemen's fatigue (α = 0.1). The findings of the research showed that the operation processing time (of fire extinguishing) had a normal distribution with a mean of 40 minutes and a variance of 10 minutes, independent of the severity of the incident. Also, fatigue coefficient was calculated 0.1 by analyzing the sensitivity of the solution time of the algorithm with changes [0–1]. The initial standard travel time, with an average speed of 47 km/h and a density factor of 1.24, was 5min:20s. Solving the problem in large and small dimensions showed that the initial power effect of each fire station is 0.36 according to the fatigue level of the forces. Based on the obtained results, GA performs better in terms of problem solution time, and the improved PSOA also has higher quality answers.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 85%

Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

Kheirdast,

Jozi,

Rezaian

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…PSO has better global search ability, but its local search optimisation ability is weak, and is apt to fall into premature convergence. Researchers has put numerous efforts to improve the performance of PSO, but fundamental problems still exist, and the global optimal path often cannot be obtained in complex environments [44][45][46].…”

Section: Swarm Intelligence Optimisation Algorithmmentioning

confidence: 99%

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

2023

View full text Add to dashboard Cite

The environmental monitoring of cultural relics based on wireless sensor networks in museums demands for the transmission and processing of massive data, which in turn leads to problems, such as heavy network traffic, high time delay, and unbalanced node energy consumption. To solve these problems, an energy balanced routing protocol which can minimise the network energy consumption is proposed. The improved swarm intelligence optimisation algorithm which combined particle swarm optimisation with ant colony optimisation is adopted to construct an environmental monitoring system for cultural relics preservation. Simulation results show that the decrease of the path length for data transmission under the proposed algorithm is 2.5% higher than that of other classical algorithms, indicating that the proposed algorithm can effectively reduce the length of network path and improve the transmission performance of network system. In view of optimisation rounds and simulation time, the energy consumption of the network system under our algorithm is 57.5% less compared with other classical algorithms, and the average residual energy variance is 57.1% less. The cultural relics environmental monitoring system based on the proposed routing protocol has better network load balancing performance.

show abstract

“…Liu et al [65] proposed the Multifold Bayesian Kernelization (MBK) algorithm, where a Bayesian framework derives kernel weights and synthesis analysis provides the diagnostic probabilities of each biomarker. Zhang et al [66] proposed the extraction of the eigenbrain using Welch's t-test (WTT) [67] combined with a polynomial kernel SVM [68] and particle swarm optimization (PSO) [69].…”

Section: The Deep Learning Approachmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

View full text Add to dashboard Cite

Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

show abstract

25 Years of Particle Swarm Optimization: Flourishing Voyage of Two Decades

Cited by 75 publications

References 352 publications

Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

A Survey of Deep Learning for Alzheimer’s Disease

Contact Info

Product

Resources

About