Parameter settings in particle swarm optimisation algorithms: a survey

2022 ◽  
Vol 16 (2) ◽  
pp. 164
Author(s):  
Shi Cheng ◽  
Jing Li
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Optimisation Algorithms

Parallel multiobjective optimisations of draft gear designs

2017 ◽  
Vol 232 (3) ◽  
pp. 744-758 ◽  
Author(s):  
Qing Wu ◽  
Colin Cole ◽  
Maksym Spiryagin ◽  
Tim McSweeney
Keyword(s):  
Genetic Algorithm ◽  
Fatigue Damage ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Round Trip ◽  
Draft Gear ◽  
Train Dynamics ◽  
Dynamics Simulations ◽  
Optimisation Algorithms ◽  
Operational Time

This paper presents the methodology and results of the parallel multiobjective optimisations of draft gear designs. The methodology used white-box draft gear models, whose parameters were used as the optimisation variables. Two optimisation algorithms were used: genetic algorithm and particle swarm optimisation. All the optimised draft gear designs were constrained by impact tests to ensure that the optimised designs also comply with the current acceptance standards for draft gears. The performance of draft gears was assessed using whole-trip longitudinal train dynamics simulations and coupler fatigue damage calculations. Each simulation covered a round trip (loaded one way, empty on return) over a total of 640 km of track, which involved about 10 h of operational time. Three optimisation objectives were considered: minimal fatigue damage for wagon connection systems of loaded trains, minimal in-train forces for loaded trains, and minimal longitudinal wagon accelerations for empty trains. Two case studies were presented, which optimised two types of draft gears (single-stage and double-stage draft gears) using genetic algorithm and particle swarm optimisation, respectively.

EVOLVING THE UPDATE STRATEGY OF THE PARTICLE SWARM OPTIMISATION ALGORITHMS

2007 ◽  
Vol 16 (01) ◽  
pp. 87-109
Author(s):  
LAURA DIOŞAN ◽  
MIHAI OLTEAN
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Complex Model ◽  
Particle Swarm Optimisation ◽  
Hybrid Technique ◽  
Real World Problem ◽  
Different Dimensions ◽  
Update Strategy ◽  
Optimisation Algorithms ◽  
Better Than

A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions.

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