Design optimization of deep groove ball bearings using crowding distance particle swarm optimization

Jana, Rabin K.; Shesu, R. Venkat; Bhattacharjee, Prasun

doi:10.1007/s12046-017-0775-9

Cited by 14 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Particle Swarm Optimization (PSO) is another AIenabled searching methodology that emulates the social actions of bees through communicating the information related to the universal and limited finest solutions [14]. The BPSO is a variant of PSO that considers every particle as a bit string.…”

Section: 4mentioning

confidence: 99%

A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

2021

Self Cite

View full text Add to dashboard Cite

Although India presently holds the global fourth-biggest instated Wind Power Generation (WPG) capability, it necessitates advancing more rapidly to satisfy the rising energy requirement of its evolving economy while restraining the consequential greenhouse gas emission. To accomplish the impressive target of setting up 140 GW WPG competence by 2030 as proposed by the Government of India, a greater number of financially viable wind farms are required to function all over the country without further ado. This paper focuses on finding the optimal cost for WPG in the Tirumala area of Andhra Pradesh. Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) have been employed concurrently with four randomly chosen terrain conditions. The research outcomes demonstrate the superior capability of BPSO to attain the most optimal cost of energy.

show abstract

Section: 4mentioning

confidence: 99%

A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Internationally, studies have been conducted to lessen the WPG expense and realize the carbon neutrality target proposed in the 2015 Paris Accord (Bhattacharjee et al, 2021). Because of the calculating competence, artificial intelligence (AI) has been utilized to resolve different engineering optimization challenges including the WPG outlay minimization problem (Jana and Bhattacharjee, 2017;Duggirala et al, 2018;Bhattacharjee et al, 2021). A neural network-abetted genetic algorithm (GA) was implemented to foresee WPG outlay (Huang, 2007).…”

Section: Introductionmentioning

confidence: 99%

Improving the Yearly Profit of Wind Farm with Artificial Intelligence Technique

Bhattacharjee,

Jana,

Bhattacharya

2022

MJST

View full text Add to dashboard Cite

Owing to the escalating environmental and social problems linked to climate change and the hastily depleting stock of hydrocarbon-based fuels, renewable power generation modes have attained massive prominence. Wind power is an important renewable energy generation technology that contributed to 5% of the planet’s power generation in 2020. However, for sustaining the Paris Agreement targets, the global wind power generation sector necessitates evolving at a fleeter pace. To expand the green switch of the worldwide power generation businesses, wind farms are expected to remain financially more advantageous than fossil fuel-based power plants. The present work focused on elevating the annual profit of wind farms by employing an amended genetic algorithm (GA). A fresh approach to dynamically apportioning the crossover and mutation prospects for a GA-enabled profit growth algorithm was suggested to amplify the capability of the GA. Three dissimilar terrain conditions with diverse obstruction configurations and a randomly generated non-uniform wind flow pattern were used for assessing the competence of the proposed algorithm for profit maximization. The results showed that the annual yields for Terrain Layouts 1, 2 and 3 obtained by the amended GA were higher by 10.34, 5.09 and 0.51%, respectively, than the typical one, which substantiated the superior proficiency of the former.

show abstract

“…The non-dominated sorting and C.D. sorting strategies of NSGA-II have been widely used in other MOP algorithms, such as the particle swarm optimization algorithm [4][5][6], and so on.…”

Section: Introductionmentioning

confidence: 99%

An Improved NSGA-II Algorithm Based on Crowding Distance Elimination Strategy

Liu¹,

Chen²

2019

IJCIS

View full text Add to dashboard Cite

Aiming at the diversity of Nondominated Sorting Genetic Algorithm II (NSGA-II) in screening out nondominated solutions, a crowding distance elimination (CDE) method is proposed. Firstly, the crowding distance is calculated in the same level of nondominated solutions, and the solution of minimum crowding distance is eliminated; secondly, the crowding distance of residual solutions is recalculated, and the solution of minimum crowding distance is also eliminated. Repeat the above process, and stop the cycle when the nondominated solutions reaches the set number. In order to verify the effectiveness of the algorithm, experiments are carried out with the representative test functions: ZDT1, ZDT2, and ZDT3. The comparative experiments of NSGA-II, -Multi-objective particle swarm optimization algorithm (MOPSO), Non dominated sorting particle swarm optimization algorithm (NSPSO), and CDE were carried out respectively. By analyzing the diversity and convergence of the four algorithms, the strategy of nondominant solutions selection based on CDE has better performance.

show abstract

Design optimization of deep groove ball bearings using crowding distance particle swarm optimization

Cited by 14 publications

References 30 publications

A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

Improving the Yearly Profit of Wind Farm with Artificial Intelligence Technique

An Improved NSGA-II Algorithm Based on Crowding Distance Elimination Strategy

Contact Info

Product

Resources

About