Resource Optimization Based on Adaptive Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 784-788
Author(s):  
Lei Wang ◽  
Yu Yun Kang
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Optimization Problem ◽  
Resource Optimization ◽  
Adaptive Genetic Algorithm ◽  
Manufacturing Environment ◽  
Simulation Results

In order to allocate tasks and optimize resources well in dynamical manufacturing environment, the model for task allocation is established. An adaptive genetic algorithm (AGA) is applied to deal with it. A machine-based encoding approach is also adopted. The simulation results testify the validity of this method, and therefore the task allocation and resources optimization problem could be dealt with efficiently.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

Optimal Design of Control Parameters for Extended Range Munitions Using Improved Genetic Algorithm

2013 ◽  
Vol 709 ◽  
pp. 611-615
Author(s):  
Si Jiang Chang ◽  
Qi Chen
Keyword(s):  
Genetic Algorithm ◽  
Pitch Angle ◽  
Adaptive Genetic Algorithm ◽  
Control Parameters ◽  
Improved Genetic Algorithm ◽  
Optimal Model ◽  
Control Effect ◽  
Optimal Method ◽  
Extended Range ◽  
Simulation Results

To obtain the best control effect for the controller of Extended Range Munitions (ERM), an optimal method for control parameters design was proposed. The adaptive genetic algorithm (GA) with real coding and the elites to keep the tactics were combined, based on which the original GA was improved. An optimal model of pitch angle controller for a type of ERM was established and the improved GA was used as the solver. Taking the stabilization loop as an example, the Powell algorithm, the simple GA and the improved GA were used to optimization, respectively. The simulation results indicate that the improved GA is more efficient and possesses stronger capability for searching.

scholarly journals Research on the Application of Adaptive Genetic Algorithm in Software Unit Security Test Simulation

2021 ◽  
Vol 2066 (1) ◽  
pp. 012010
Author(s):  
Xuefeng Ge
Keyword(s):  
Genetic Algorithm ◽  
Control Structure ◽  
Software Process ◽  
Adaptive Genetic Algorithm ◽  
Model Control ◽  
Simulation Results ◽  
Security Test

Abstract At present, the security test and simulation of software unit mainly focuses on several links, such as software control structure amelioration, software process alternating quantity model control and model inspection tech, and there are still many shortcomings, such as high missed inspection rate, difficult to effectively guarantee the needs of practice, etc. Based on this, this paper first analyses the purpose and principle of software unit security test and simulation, then studies the utilization of ameliorated genetic algorithm in software unit security test simulation, and finally gives the simulation results analysis of software unit security test based on AGA.

scholarly journals Buoyancy Regulation Strategy for Underwater Profiler Based on Adaptive Genetic Algorithm

2021 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Hui Zhi ◽  
Puzhe Zhou ◽  
Yanhu Chen ◽  
Xiaoyan Zhao ◽  
Yuandong Hong ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Underwater Vehicles ◽  
Trial Data ◽  
Regulation System ◽  
Adaptive Genetic Algorithm ◽  
Sea Trial ◽  
Different Depths ◽  
Simulation Results ◽  
Save Energy

Considering the energy limitations of underwater vehicles, a strategy for energy saving is proposed. In the proposed buoyancy regulation strategy, oil of the buoyancy regulation system is pumped out several times at different depths instead of all at once. A balance between energy and time is achieved by assigning suitable weights, and the optimised depth which can be obtained from the pressure sensor is used as the judgement threshold based on the adaptive genetic algorithm. Through the numerical simulation using sea trial data, the influence of weight selection on energy and time is explored, and the frequency of oil draining for the vehicle to ascend is optimised. Simulation results show that the proposed buoyancy regulation strategy can save energy effectively when the frequency of oil draining is 4 times within depths of 0–500 m. Finally, trials were performed in Qiandao Lake and verify the contradictory relationship between energy and time.

Based on Genetic Algorithm Research of Stocker Picking Shortest Path

2012 ◽  
Vol 155-156 ◽  
pp. 186-190
Author(s):  
Fu Cai Wan ◽  
Duo Chen ◽  
Yong Qiang Wu
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Optimization Problem ◽  
Traveling Salesman ◽  
Path Optimization ◽  
Improved Genetic Algorithm ◽  
Operating Process ◽  
Path Distance ◽  
Simulation Results ◽  
The Mathematical Model

This paper analyzes characteristics of automated warehouse stocker picking operating process. Path optimization problem is considered as traveling salesman problem. The coordinates of picking points by calculating determine a stocker running route. The mathematical model of a path distance is built. And using the improved genetic algorithm solves the above problem. Finally, M-file program of stocker running path optimization is written and run in MATLAB. The simulation results that, in solving stocker path optimization problem, it can search for a shortest path by genetic algorithm. Thereby enhance the efficiency of automated warehouse system, increase greater benefits of the enterprise.

Study on Coordinated Control of Multi-Distribution Static Var Compensator

2014 ◽  
Vol 672-674 ◽  
pp. 1127-1131
Author(s):  
Ming Jiang Zhang ◽  
Xi Lin Zhang ◽  
Zhen Hao Wang ◽  
Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
Power Quality ◽  
Distribution System ◽  
Optimization Problem ◽  
Coordinated Control ◽  
Parameters Optimization ◽  
Static Var Compensator ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Simulation Results

DFACTS devices can synthetically manage power quality problems. As one of the most DFACTS devices, the coordinated control of multi-distribution static var compensator should be considered. Controllers are separately designed aiming at different functions, that means the controllers are isolated even contradictory. In allusion to the problem that the separately designed DFACTS controllers exist interactions, the paper turns the coordination of the DFACTS controller into multi-objective optimization problem, takes the single-load infinite-bus distribution system with two DSVC as the research object, using the Non-dominated Sorting Genetic Algorithm with elitism approach (NSGA-II) for DFACTS controller parameters optimization, and the simulation results show the effectiveness of the algorithm.

Hurry Path Planning Based on Adaptive Genetic Algorithm

2013 ◽  
Vol 446-447 ◽  
pp. 1292-1297 ◽  
Author(s):  
Da Qiao Zhang ◽  
Jiu Fen Zhao ◽  
Gang Lei ◽  
Shun Hong Wang ◽  
Xiao Long Zheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Formation Flying ◽  
Planning Method ◽  
High Mode ◽  
Adaptive Genetic Algorithm ◽  
Aerial Vehicles ◽  
Simulation Results ◽  
Risk Cost

During formation flying, Unmanned Aerial Vehicles (UAV) may need to arrive at target ahead of schedule by hurry path. Given fixed flight high mode, hurry planning method was proposed based on Adaptive Genetic Algorithm (AGA), which makes the new path shorter by locally adjusting the default path. By full considering the risk of UAV flight, the hurry path got by AGA meets the requirements of the risk cost and time amount in advance. Simulation results show that the path gotten by AGA can better meet the requirements of the time amount in advance, and evade the threat area effectively too.

A New Design of CMOS Bandgap Reference Based on Genetic Algorithm

2013 ◽  
Vol 712-715 ◽  
pp. 1780-1786 ◽  
Author(s):  
Jian Hai Yu ◽  
Chang Chun Dong
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Bandgap Reference ◽  
Multi Objective ◽  
Simulation Results

A new CMOS bandgap reference which is optimized by adaptive GA(genetic algorithm) is presented in this paper. During the optimization of the parameters, according to the different specifications, the idea which looks on secondary targets as the boundary restrictions is proposed, so that the problem of multi-objective normalization is solved. The secondary optimizing method about coarse adjusting initially, meticulous adjusting successively is proposed in the optimization based on adaptive Genetic Algorithm. The simulation results which have reached the leading standard of industry indicate the advantage and validity of the method comparing with other method used in the design of bandgap references.

scholarly journals Ant colony search algorithm for optimal reactive power optimization

2006 ◽  
Vol 3 (1) ◽  
pp. 77-88 ◽  
Author(s):  
K. Lenin ◽  
M.R. Mohan
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Ant Colony ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Ant Colonies

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents? approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA).

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