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.

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.

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.

scholarly journals Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement ◽  
Selection Scheme ◽  
Dual Structure ◽  
Coding Method

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale structures. Based on the criterion of the OSP for the modal test, an improved genetic algorithm, called “generalized genetic algorithm (GGA)”, is adopted to find the optimal placement of sensors. The dual-structure coding method instead of binary coding method is proposed to code the solution. Accordingly, the dual-structure coding-based selection scheme, crossover strategy and mutation mechanism are given in detail. The tallest building in the north of China is implemented to demonstrate the feasibility and effectiveness of the GGA. The sensor placements obtained by the GGA are compared with those by exiting genetic algorithm, which shows that the GGA can improve the convergence of the algorithm and get the better placement scheme.

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.

The Research on the Resistance Characteristic Coefficient in Heating System Based on the Improved Genetic Algorithm

2012 ◽  
Vol 170-173 ◽  
pp. 2587-2591
Author(s):  
Xian Liang Yang ◽  
Tong Li ◽  
Song Lin Wang ◽  
Zheng Ren Wu
Keyword(s):  
Genetic Algorithm ◽  
Heating System ◽  
Resistance Characteristic ◽  
Improved Genetic Algorithm ◽  
Operation And Management ◽  
Improved Algorithm

With the development of the heating industry, the higher level of operation and management are required. Therefore it needs more accurate values of nodes pressure and flow. In this paper, based on the genetic algorithm, In order to realize the improved algorithm, change the rate of cross and mutation according to the fitness. After revising the resistance characteristic coefficient, we can get the more accurate values of nodes pressure. Finally, an example is given to verify the feasibility and validity of this method.

Research on Improved Genetic Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1760-1763
Author(s):  
Xiao Yu Wang ◽  
Yong Hui Yang ◽  
Shuo Li ◽  
Chuang Gao
Keyword(s):  
Genetic Algorithm ◽  
Embedded System ◽  
Real Time ◽  
Cost Reduction ◽  
Function Optimization ◽  
Improved Genetic Algorithm ◽  
The Real ◽  
Crossover Rate ◽  
Time Requirements ◽  
Improved Algorithm

An improved genetic algorithm for the function optimization of multi-core embedded system is proposed. A number of chromosomes that distribute uniformly in space are generated by the algorithm randomly. Each chromosome is randomly coded and a new one will be generated by mutual calculation. After continuous elimination and circulation, the optimized chromosomes can be selected. The improved algorithm makes the mutation offspring have the opportunity to be the next parent with the increase of mutation. It enhances the parent diversity, increases the crossover rate, activates crossover between the parents and has chance to access to the best solution. The efficiency and cost reduction performance are improved. The different tasks will be distributed in parallel to available processors so as to meet the real-time requirements.

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.

A Kind of Improved Genetic Algorithm for Multi-Depot Vehicle Routing Problem

2013 ◽  
Vol 347-350 ◽  
pp. 3273-3277
Author(s):  
Wan Xiang Lian ◽  
Can Shi Zhu ◽  
Jiang Hua Hu ◽  
Dong Feng Zhang ◽  
Duan Liu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Degree Of Polymerization ◽  
Search Efficiency ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Np Hard Problem ◽  
Algorithm Search ◽  
Improved Algorithm

Multi-Depot Vehicle routing problem is an NP-HARD problem. Because the genetic algorithm is easy premature convergence and search efficiency is not high, this paper established the defects of polymerization degree model, and based on this, this paper proposes an improved algorithm, this algorithm can change the mutation rate according to their own chromosome degree of polymerization size to avoid the prematurity of genetic algorithm, and improved genetic algorithm search efficiency. Through the contrast, the results showed that the algorithm has good search efficiency and stability, which demonstrates that the improved algorithm is effective and feasible for multi-depot vehicle routing problem.

scholarly journals Efficient Iterative Process based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1219 ◽  
Author(s):  
de Paulis ◽  
Cecchetti ◽  
Olivieri ◽  
Piersanti ◽  
Orlandi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Frequency Spectrum ◽  
Input Impedance ◽  
Iterative Process ◽  
Power Delivery ◽  
Improved Genetic Algorithm ◽  
Decoupling Capacitors ◽  
Power Delivery Network ◽  
Capacitor Placement ◽  
Board Level

To reduce the noise created by a power delivery network, the number, the value of decoupling capacitors and their arrangement on the board are critical to reaching this goal. This work deals with specific improvements, implemented on a genetic algorithm, which used for the optimization of the decoupling capacitors in order to obtain the frequency spectrum of the input impedance in different positions on the network, below previously defined values. Measurements are performed on a specifically manufactured board in order to validate the effectiveness of the proposed algorithm and the optimization results obtained for a specific example board.

scholarly journals Dynamic Route Guidance Using Improved Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhanke Yu ◽  
Mingfang Ni ◽  
Zeyan Wang ◽  
Yanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Steady State ◽  
Fitness Function ◽  
Population Diversity ◽  
Route Guidance ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
Guidance Algorithm ◽  
Dynamic Route

This paper presents an improved genetic algorithm (IGA) for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

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