Fast solution technique for unit commitment by particle swarm optimisation and genetic algorithm

2007 ◽  
Vol 5 (4) ◽  
pp. 440 ◽  
Author(s):  
Y.M. Chen ◽  
Wen Shiang Wang
Keyword(s):  
Genetic Algorithm ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Solution Technique ◽  
Fast Solution

Increasing efficiency of the robust deformation analysis methods using genetic algorithm and generalised particle swarm optimisation

Survey Review ◽  
2020 ◽  
pp. 1-13
Author(s):  
Mehmed Batilović ◽  
Zoran Sušić ◽  
Željko Kanović ◽  
Marko Z. Marković ◽  
Dejan Vasić ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Deformation Analysis ◽  
Analysis Methods

Intelligent Layout Design of Ship Pipeline Using a Particle Swarm Optimisation Integrated Genetic Algorithm

2021 ◽  
Vol 163 (A2) ◽  
Author(s):  
Yunlong Wang ◽  
Hao Wei ◽  
Guan Guan ◽  
Kai Li ◽  
Yan Lin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm ◽  
Regional Model ◽  
Particle Swarm Optimisation ◽  
Parameter Setting ◽  
Space Model ◽  
Pipe Model ◽  
Mutation Strategies ◽  
Crossover And Mutation ◽  
Automatic Pipeline

This paper proposes a Particle Swarm Optimisation Integrated Genetic (PSOIG) algorithm to define ship pipeline layout, where the pipeline layout environment is complex and changeable. The pipeline layout space model includes a cabin model, an obstacle model, a pipe model and a regional model of layout. Given the characteristics of ship pipeline layout, the direction guidance mechanism for automatic pipeline layout is introduced, and a direction parameter setting are put forward to further improve the efficiency of the algorithm. At the same time, the crossover and mutation strategies of the genetic algorithm are introduced into the particle swarm optimisation to establish the PSOIG algorithm for ship pipeline intelligent layout. This fully optimises the advantages of particle swarm optimisation and genetic algorithms to improve the diversity of solutions and the convergence speed of the algorithm. Finally, the simulation results demonstrate the feasibility and efficiency of the proposed algorithm.

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