A Modified Genetic Algorithm for Multi-Objective Optimization on Running Curve of Automatic Train Operation System Using Penalty Function Method

2018 ◽  
Vol 17 (1) ◽  
pp. 74-87 ◽  
Author(s):  
Yanchu Liang ◽  
Hao Liu ◽  
Cunyuan Qian ◽  
Guanlei Wang
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Function Method ◽  
Penalty Function Method ◽  
Multi Objective Optimization ◽  
Operation System ◽  
Multi Objective ◽  
Modified Genetic Algorithm ◽  
Train Operation ◽  
Automatic Train Operation

Optimization of Structure Parameters for Hydraulic Energy Accumulated Torpedo Launching Device

2011 ◽  
Vol 704-705 ◽  
pp. 612-618
Author(s):  
Bing Tian
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Atmospheric Pressure ◽  
Function Method ◽  
Penalty Function Method ◽  
Structure Parameters ◽  
Multi Objective Optimization ◽  
Single Object ◽  
Mathematical Mode ◽  
The World

For the low launching noise and without increasing atmospheric pressure in chamber, the hydraulic energy accumulated torpedo launching device has been highly regarded by all navies around the world. Mathematical mode of the launch course of the device has been built. Combined with the tactics and technique index, the structure parameters of the device have been optimized with genetic algorithm. As several parameters which were different in degree and range, normalization was applied to convert multi-objective optimization into single-object optimization, and penalty function method was used to deal with constraints. The optimization result shows that the optimized structure parameters not only meet the requirement of launching, but the dimension of the device is more compact to fit the narrow configuration of submarine and may provide references to the design of hydraulic energy accumulated torpedo launching device.

scholarly journals Constrained optimization using penalty function method combined with genetic algorithm

2018 ◽  
Vol 19 ◽  
pp. 01037
Author(s):  
Łukasz Knypiński ◽  
Krzysztof Kowalski ◽  
Lech Nowak
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Penalty Function ◽  
Function Method ◽  
Computer Software ◽  
Selection Procedure ◽  
Primary Objective ◽  
Penalty Function Method ◽  
Penalty Term ◽  
Electromagnetic Linear Actuator

In the paper the way of adaptation of the penalty function method to the genetic algorithm is presented. In case of application of the external penalty function, the penalty term may exceed the value of the primary objective function. This means, that the value of the modified objective function is negative, while in genetic algorithm the adaptation must be of positive value, especially it in the selection procedure utilizes the roulette method. The sigmoidal transformation is applied to solve this problem. The computer software is developed in the Delphi environment. The proposed approach is applied to optimization of the electromagnetic linear actuator.

scholarly journals Genetic Algorithm for the Multi-Objective Optimization of Product Gene

2012 ◽  
Vol 6-7 ◽  
pp. 116-121
Author(s):  
Qing Song Ai ◽  
Zhou Liu ◽  
Yan Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Engineering ◽  
Manufacturing Industry ◽  
Optimization Problems ◽  
Rapid Development ◽  
Optimal Solution ◽  
Adaptive Mutation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

Constrained optimization of the magnetostrictive actuator with the use of penalty function method

2018 ◽  
Vol 37 (5) ◽  
pp. 1575-1584
Author(s):  
Lukasz Knypinski ◽  
Krzysztof Kowalski ◽  
Lech Nowak
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Function Method ◽  
Classical Method ◽  
Optimal Solution ◽  
Penalty Function Method ◽  
Content Type ◽  
Plasma Gun ◽  
Coupled Equations ◽  
Transient Electromagnetic

Purpose The purpose of this paper is to elaborate algorithm and software for the optimization of the actuator–capacitor system taking the dynamics parameters into account. The system is applied for driving the valve of plasma gun. Two optimization strategies are proposed and pondered. The penalty function approach has been expanded in detail. Design/methodology/approach The field-circuit mathematical model of the dynamics operation consists of the strongly coupled equations of the transient electromagnetic fields and the equations of the electric circuit. The numerical implementation is based on finite element method and step-by-step Cranck–Nicholson procedure. The genetic algorithm has been used in the optimization procedure. The sigmoidal transformation has been applied to adjust the classical external penalty function method to the genetic algorithm. Findings The modification consists in adaptation of the penalty function to the genetic algorithm. In the proposed approach, operations involving successive iterations of increasing penalty function and operations containing genetic iterations are intertwined with each other. The differences between these two procedures are getting blurred. The proposed approach is very effective. It is possible to achieve optimal solution even more than ten times faster than using the classical method. Originality/value The proposed approach can be successfully applied to designing and optimization of different electromagnetic devices, including functional constraints.

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