Particle Swarm Optimization in Bioinformatics, Image Processing, and Computational Linguistics

2021 ◽  
Vol 12 (4) ◽  
pp. 25-44
Author(s):  
Badal Soni ◽  
Satashree Roy ◽  
Shiv Warsi
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Particle Swarm Optimization ◽  
Digital Image Processing ◽  
Computational Linguistics ◽  
Digital Image ◽  
Focal Point ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Engineering Problems

Since its inception, particle swarm optimization and its improvement has been an active area of research, and the algorithm has found its application in multifarious domains such as highly constrained engineering problems as well as artificial intelligence. The focal point of this paper is to make the reader aware of the innumerable applications of particle swarm optimization, especially in the field of bioinformatics, digital image processing, and computational linguistics. This review work is designed to serve as a comprehensive look-up guide and to navigate through the algorithm's scope and application in recent times in the aforementioned fields.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

2013 ◽  
Vol 46 (11) ◽  
pp. 1465-1484 ◽  
Author(s):  
Weian Guo ◽  
Wuzhao Li ◽  
Qun Zhang ◽  
Lei Wang ◽  
Qidi Wu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Engineering Problems

scholarly journals New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

2018 ◽  
Author(s):  
Aman Chandra Kaushik ◽  
Shiv Bharadwaj ◽  
Ajay Kumar ◽  
Avinash Dhar ◽  
Dongqing Wei
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

scholarly journals Using particle swarm optimization to solve test functions problems

2021 ◽  
Vol 10 (6) ◽  
pp. 3422-3431
Author(s):  
Issa Ahmed Abed ◽  
May Mohammed Ali ◽  
Afrah Abood Abdul Kadhim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Speed Of Convergence ◽  
Test Functions ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Engineering Problems

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

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