Path Planning of Stacker for Automated Building Materials Warehouse Based on the Improved Adaptive Genetic Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1475-1478
Author(s):  
Shen Li
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Building Materials ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Work Efficiency ◽  
Early Maturity ◽  
Slow Convergence ◽  
Simulation Results

In this paper, the improved adaptive genetic algorithm has been presented, which not only can overcome the early maturity and slow convergence speed of traditional genetic algorithm, and greatly improve the efficiency of genetic algorithm. This method can improve the quality of stacker path planning and work efficiency for modern automated building materials warehouse. Using the genetic algorithm toolbox of Matlab, the simulation results can further verified the feasibility and effectiveness of this method.

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.

Optimization of Transit Hub Terminals Based on Improved Adaptive Genetic Algorithm

2014 ◽  
Vol 488-489 ◽  
pp. 942-946
Author(s):  
Chun Mei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Population Diversity ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Allocation Model ◽  
Slow Convergence ◽  
Optimized Allocation ◽  
The Individual ◽  
Crossover And Mutation

In this paper, how to design the layout of transit hub terminals is discussed, and an optimized allocation model about bus lines and bus terminals is established. In order to address the slow convergence of adaptive genetic algorithm, an index that indicates population diversity degree is introduced to adjust the individual crossover and mutation rate. This improved adaptive genetic algorithm is applied for the allocation model and an example is used to validate its efficiency. Results show that it is a promising approach and can improve the convergence speed.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

2021 ◽  
Vol 01 ◽  
Author(s):  
Ying Li ◽  
Chubing Guo ◽  
Jianshe Wu ◽  
Xin Zhang ◽  
Jian Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Systems ◽  
Ant Colony Optimization Algorithm ◽  
A Algorithm ◽  
Collaborative Scheduling ◽  
Simulation Results ◽  
Planning Problems ◽  
Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

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.

Path planning of a mobile robot using genetic heuristics

Robotica ◽  
1998 ◽  
Vol 16 (5) ◽  
pp. 575-588 ◽  
Author(s):  
Andreas C. Nearchou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Hill Climbing ◽  
Planning Problem ◽  
Well Performance ◽  
Simulation Results ◽  
Task Oriented ◽  
Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

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.

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