scholarly journals OPTIMIZATION OF ARCHITECTURAL LAYOUT BY THE IMPROVED GENETIC ALGORITHM

2005 ◽  
Vol 11 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Romualdas Baušys ◽  
Ina Pankrašovaite
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Conceptual Design ◽  
Search Method ◽  
Evolutionary Search ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Current Stage

In this paper we consider architectural layout problem that seeks to determine the layout of Units based on lighting, heating, available sizes and other objectives and constraints. For a conceptual design of architectural layout we present an approach based on evolutionary search method known as the genetic algorithms (GAs). However, the rate of convergence of GAs is often not good enough at their current stage. For this reason, the improved genetic algorithm is proposed. We have analysed and compared the performance of standard and improved genetic algorithm for architectural layout problem solutions and presented the results of performance.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

An Improved Genetic Algorithm for Digital Facility Layout Optimization Oriented Multi-Species and Variable-Batch Production Mode

2013 ◽  
Vol 694-697 ◽  
pp. 3632-3635
Author(s):  
Dao Guo Li ◽  
Zhao Xia Chen
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Batch Production ◽  
Production Mode ◽  
Slow Convergence ◽  
Weak Points

When solving facility layout problem for the digital workshop to optimize the production, the traditional genetic algorithm has its flaws with slow convergence speed and that the accuracy of the optimal solution is not ideal. This paper analyzes those weak points and proposed an improved genetic algorithm according to the characteristics of multi-species and variable-batch production mode. The proposed approach improved the convergence speed and the accuracy of the optimal solution. The presented model of GA also has been tested and verified by simulation.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem

1998 ◽  
Vol 6 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Colin R. Reeves ◽  
Takeshi Yamada
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing Algorithm ◽  
Search Method ◽  
Neighborhood Search ◽  
Search Technique ◽  
Permutation Flowshop ◽  
Sequencing Problem ◽  
Annealing Algorithm ◽  
Tabu Search Method

In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

A Review of Genetic Algorithms Applied to Optimizing Maintenance Strategy of Bridges

2011 ◽  
Vol 374-377 ◽  
pp. 2437-2441
Author(s):  
Jing Mei Bian ◽  
Quan Bai ◽  
Qing Hua Shi
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Search Algorithms ◽  
Evolutionary Search ◽  
Maintenance Strategy ◽  
Research Focus ◽  
Current State ◽  
Bridge Maintenance ◽  
State Of Research

Abstract:Along with the fast increasing of maintenance demand, the problems associated with maintenance and reinforcement decision-making for deteriorating bridges such as maintenance strategy optimization have been present research focus. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). This paper begins with a brief overview of bridge maintenance strategy optimization followed by a review of the current state of research in applying evolutionary techniques to solving this problem.

Research of Chip Placement and Routing Algorithms for Board-Level Photoelectric Interconnection

2014 ◽  
Vol 556-562 ◽  
pp. 1577-1579
Author(s):  
Jian Liu ◽  
Zhao Hua Wu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Routing Algorithms ◽  
Optimal Placement ◽  
Improved Method ◽  
Improved Genetic Algorithm ◽  
C Language ◽  
Placement And Routing ◽  
Board Level ◽  
Improved Algorithm

This document improved genetic algorithm and Intelligent discern points algorithmin chip placement and routing for Board-level photoelectric interconnection, by comparisonofthe algorithm results to verify the effectiveness and practicality of the improved algorithm. First introduced the features of chip placement and routing for Board-level Photoelectric Interconnection. Then describes the improved method of genetic algorithms and intelligent discern points algorithms. Finally, implement algorithm by C language on VC6.0++ platform, while the data import MATLAB to displays the optimal placement and routing results. The results show that the effectiveness of improved algorithm, which has a guiding significance for the chip placement and routing.

Calibration of Pipe Friction Factor Based on Improved Genetic Algorithm

2011 ◽  
Vol 217-218 ◽  
pp. 1036-1039 ◽  
Author(s):  
Rui Fen Zhou ◽  
Yu Xue Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Friction Factor ◽  
Water Injection ◽  
Improved Genetic Algorithm ◽  
Oilfield Water

This paper presents a method to calibrate pipe friction factor in oilfield water injection pipeline based on genetic algorithm. For the shortcoming, of basic genetic algorithms, genetic algorithm is made the corresponding improvements, this algorithm is improved global searching capability. Finally, a practical example verified the feasibility of the presented method.

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