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.

A New Hybrid Genetic Algorithm and Particle Swarm Optimization

2012 ◽  
Vol 498 ◽  
pp. 115-125 ◽  
Author(s):  
H. Hachimi ◽  
Rachid Ellaia ◽  
A. El Hami
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
New Generation ◽  
Crossover And Mutation

In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several experiments.

scholarly journals A comparative study of particle swarm optimization and genetic algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Saman M. Almufti ◽  
Amar Yahya Zebari ◽  
Herman Khalid Omer
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.  

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

scholarly journals An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu ◽  
Yangbing Xu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Fuzzy Process ◽  
Short Period ◽  
Mutation Strategies ◽  
Encoding Method ◽  
Machine Replacement ◽  
Crossover And Mutation

The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).

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