scholarly journals Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0226161 ◽  
Author(s):  
Yubang Liu ◽  
Shouwen Ji ◽  
Zengrong Su ◽  
Dong Guo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Adaptive Genetic Algorithms ◽  
Automatic Sorting ◽  
Sorting System

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.

Research on Multi-Objective Reservoir Optimal Operation Based on Adaptive Genetic Algorithms

2014 ◽  
Vol 926-930 ◽  
pp. 2042-2045 ◽  
Author(s):  
Jiang Chang ◽  
Guang Wen Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Operation ◽  
Adaptive Genetic Algorithm ◽  
Control Parameters ◽  
Multi Objective ◽  
Adaptive Genetic Algorithms ◽  
Individual Fitness ◽  
Dispersion Degree ◽  
Poor Convergence ◽  
Standard Genetic Algorithm

The comprehensive reservoir scheduling is based on multi-objective reservoir operation, should try to coordinate between each target scheduling of progression. In this paper, based on the characteristics of scheduling period, this paper proposes a reservoir optimal operation based on adaptive genetic algorithm solution of the problem. Because of the adaptive genetic algorithm can during evolution according to individual fitness and dispersion degree of genetic control parameters are adjusted automatically, can better solve encountered in the application of the standard genetic algorithm in the problem of poor convergence and prematurity.

A New Path Planning Method of Robot Based on Improved Adaptive Genetic Algorithms

2013 ◽  
Vol 278-280 ◽  
pp. 590-593
Author(s):  
Huai Qiang Li ◽  
Ming Xia Shi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Path Planning ◽  
Simulation Experiment ◽  
Planning Method ◽  
Adaptive Genetic Algorithm ◽  
Robot Path Planning ◽  
Adaptive Genetic Algorithms ◽  
Planning Space ◽  
Robot Path

This paper presents a new method of robot path planning which is based on improved adaptive genetic algorithm. On the foundation of building the model in planning space by link-graph, we first gets the feasible paths by using Ford algorithms ,and then adjusts the points of every path by using improved adaptive genetic algorithms to get the best or better path. The simulation experiment shows the advancement of the method.

Multi-Objective Fleet Assignment Problem Based on Self-Adaptive Genetic Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2895-2900 ◽  
Author(s):  
Xiao Yang ◽  
Bo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Assignment Problem ◽  
Network Capacity ◽  
Total Carbon ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Route Network ◽  
Assignment Model ◽  
Key Factor

Since the beginning of the twenty-first century, energy conservation has become the theme of the development of the world. China government set the emissions-reduction targets in various industries on the 12th Five-Year Plan. And the airlines were committed to reduce their carbon emissions. From an operational perspective, the airline model assignment problem is a key factor of the total carbon emissions on the entire route network. But the traditional aircraft assignment models approach did not account for this purpose to reduce carbon emissions. By constructing the multi-objective optimization models consider carbon emissions assignment model using a genetic algorithm, numerical example shows that the model is able to meet all aspects demand which include meeting route network capacity demand, minimizing operating costs and reducing total aircraft fleet carbon emissions.

Research on Multi-Objective Transmission Planning in Electricity Markets

2012 ◽  
Vol 516-517 ◽  
pp. 1429-1432
Author(s):  
Yang Liu ◽  
Xu Liu ◽  
Feng Xian Cui ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Electricity Markets ◽  
Optimization Problem ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Multi Objective ◽  
Complex Optimization ◽  
The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

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