Performance Comparison of Particle Swarm Optimization and Genetic Algorithm in Rolling Fin-Tube Heat Exchanger Optimization Design

2008 ◽  
Author(s):  
Wutao Han ◽  
Linghong Tang ◽  
Gongnan Xie ◽  
Qiuwang Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Pressure Drop ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Volume Weight ◽  
Fin Tube

A method for optimization designs of rolling fin-tube heat exchangers was put forward with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively. The length of tube bundles, the row numbers of tubes, the width of heat exchanger core and fin pitch were used as the optimization variables. The allowable pressure drop and heat exchange requirements were considered as restrictive conditions. According to specific design requirements, the volume, weight or pressure drop may be chosen as the optimization objective function. In the same design parameters, ranges of the search variables and restrictive conditions, optimization results compared with GA, the minimum volume, weight and pressure drop PSO could decrease by 3.34%, 4.31% and 14.04%, respectively, and corresponding CPU time could be reduced by 32.39%, 40.23% and 33.45%, respectively. In the fields of optimization designs of heat exchanger, Particle Swarm Optimization is a promising optimization method.

Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 283-286
Author(s):  
Chen Yang Chang ◽  
Jing Mei Zhai ◽  
Qin Xiang Xia ◽  
Bin Cai
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Structure Optimization ◽  
Design Parameters ◽  
Support Vector ◽  
Swarm Optimization ◽  
Multi Objective

Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

Optimization Design for Air Heat Exchanger of Large Fast Reactor Based on Genetic Algorithm

2013 ◽  
Author(s):  
Huajin Yu ◽  
Lina Zhu ◽  
Zhenxing Zhang ◽  
Ziyu Liao
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Fast Reactor ◽  
Optimization Design ◽  
Heat Removal ◽  
Tube Length ◽  
Design Parameters ◽  
Shell Side ◽  
Air Resistance ◽  
Fin Tube

The passive design for decay heat removal system of future fast reactor will put forward higher requirement for air heat exchanger (AHX), which is directly relevant to the structure and anti-seismic design of stack. Under considering the heat exchanger ability and the structure compactness comprehensively, a strategy for the optimization design of AHX based on genetic algorithm was developed in this paper. The air resistance in shell side of vertical fin tube AHX was chosen as the objective function, and the effect of design parameters including fin pitch, number of tube rows, tube pitch and tube length on the air resistance was discussed. The results of the study show that the method for the optimization design of AHX based on genetic algorithm can effectively optimize the structure of AHX and improve the resistance characteristic of the shell side evidently, which leads to design the fast reactor plant, stack structure and seismic resistance simply.

scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Modeling of Heat Exchanger by Using Bio-Inspired Algorithm

2014 ◽  
Vol 660 ◽  
pp. 831-835 ◽  
Author(s):  
N. Atiqah Daud ◽  
Salihatun Md Salleh
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Structural Model ◽  
Particle Swarm ◽  
Study Data ◽  
Primary Objective ◽  
Swarm Optimization ◽  
Validation Test ◽  
Input Parameters

Modelling of heat exchanger helps to define the error that occurs during the operation. Hence by optimizing it using genetic algorithm and particle swarm optimization, the error that occurred could be minimized and compared between both algorithms. The primary objective of this study was to obtain structural model using ARMAX equation. In this study, data from heat exchanger experiment was used to determine the parameter of ARMAX equation. Using genetic algorithm and particle swarm optimization, ARMAX parameters are optimized. Hence, the transfer function represents the plant for modelling. Validation test used were autocorrelation and cross-correlation to validate between normalised data input and error. Based on the result obtained, for GA, the input parameters are-0.000214, -0.000728, -0.0020, and-0.000804 while the output parameters are-1.0000, -0.1783, -0.1473 and 0.3248. For PSO, the input parameters are 0.0104, -0.0122, -0.0067 and 0.0118 while the output parameters are-0.4274, -0.1256, -0.1865 and-0.2614. From validation test, GA produced smoother and effective result compared to PSO with less noise exists.

Intelligence Optimization Design Model of Deep Cement-Stirring Pile

2010 ◽  
Vol 44-47 ◽  
pp. 1505-1508
Author(s):  
Xiang Yang Chen ◽  
Heng Zhen Yan
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Design Theory ◽  
Optimization Design ◽  
Particle Swarm ◽  
Composite Foundation ◽  
Swarm Optimization ◽  
Pile Diameter ◽  
Design Variables ◽  
Pile Length

Aiming at the phenomenon of the more conservative design of deep cement stirring pile currently, used optimization design theory such as genetic algorithm and particle swarm optimization, taken the cement consumption as the object function, taken replacement rate, water-cement ratio, pile diameter and pile length as the design variables, composite foundation bearing capacity and settlement as restrictive conditions, the optimal design models are established respectively based on genetic algorithm and particle swarm optimization. Case studies have shown that these two established models are effective. By comparison, the particle swarm optimization model is the more effective one.

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