Intelligent Tourism Route Optimization Method Based on the Improved Genetic Algorithm

2016 ◽  
Author(s):  
Xiujuan Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Route Optimization ◽  
Improved Genetic Algorithm

Research on Coordination of Evacuation of Human and Car Simulation Optimization Method

2011 ◽  
Vol 130-134 ◽  
pp. 2185-2189
Author(s):  
Yun Long Ma ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulation Optimization ◽  
Optimization Method ◽  
Crowd Simulation ◽  
Optimal Scheduling ◽  
Regular Pattern ◽  
Improved Genetic Algorithm ◽  
Whole Process ◽  
Evacuation Strategy ◽  
Coordination System

Among researches on people evacuation under emergency, there are no deep touch of coordinating evacuation strategy of human and traffic, and also can not set models and simulate the whole process of evacuation. This research focused on the human evacuation under emergencies, divided the situations into Not Intervene and May Intervene, analyzed the coordination system and probed the model of coordinating evacuation. Based on the result of crowd simulation, the author could forecast the result of human evacuation and find the optimal traffic schedule proposal. The author took use of the improved Genetic Algorithm as well as the distributed coordinating simulation, finally found a systematic optimal scheduling program and revealed the internal regular pattern of coordinating evacuation.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

The Improved Route Optimization Based on Hybrid Optimization Algorithm Based on GA and TS

2012 ◽  
Vol 170-173 ◽  
pp. 3695-3698
Author(s):  
Jun Ma ◽  
Li Ying Zhang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Route Optimization ◽  
Hybrid Optimization ◽  
Improved Genetic Algorithm ◽  
Hybrid Optimization Algorithm ◽  
Search Speed

The vehicle routing planning in the process of logistics is a hot issue. There is a lack of traditional genetic algorithm used to solve this issue, so the taboos is introduced to improve it. The improved genetic algorithm based on taboos get high-search speed compared to the traditional genetic algorithm, and this improvement is verified by the example.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

Optimal Sensor Placement Based on MAC and SPGA Algorithms

2012 ◽  
Vol 594-597 ◽  
pp. 1118-1122 ◽  
Author(s):  
Yong Ming Fu ◽  
Ling Yu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Optimization Method ◽  
Frame Structure ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Modal Assurance Criterion ◽  
Sensor Optimization ◽  
Computation Efficiency ◽  
The Individual

In order to solve the problem on sensor optimization placement in the structural health monitoring (SHM) field, a new sensor optimization method is proposed based on the modal assurance criterion (MAC) and the single parenthood genetic algorithm (SPGA). First, the required sensor numbers are obtained by using the step accumulating method. The SPGA is used to place sensors, in which the binary coding is adopted to realize the genetic manipulation through gene exchange, gene shift and gene inversion. Then, the method is further simplified and improved for higher computation efficiency. Where, neither the individual diversity of initial population nor the immature convergence problem is required. Finally, a numerical example of 61 truss frame structure is used to assess the robustness of the proposed method. The illustrated results show that the new method is better than the improved genetic algorithm and the step accumulating method in the search capacity, computational efficiency and reliability.

Machine Learning-Based Optimization for Subsea Pipeline Route Design

2021 ◽  
Author(s):  
Subrata Bhowmik
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Optimization Method ◽  
Route Optimization ◽  
Route Selection ◽  
Optimal Route ◽  
Operational Costs ◽  
Route Design ◽  
Pipeline Route

Abstract Optimal route selection for the subsea pipeline is a critical task for the pipeline design process, and the route selected can significantly affect the overall project cost. Therefore, it is necessary to design the routes to be economical and safe. On-bottom stability (OBS) and fixed obstacles like existing crossings and free spans are the main factors that affect the route selection. This article proposes a novel hybrid optimization method based on a typical Machine Learning algorithm for designing an optimal pipeline route. The proposed optimal route design is compared with one of the popular multi-objective optimization method named Genetic Algorithm (GA). The proposed pipeline route selection method uses a Reinforcement Learning (RL) algorithm, a particular type of machine learning method to train a pipeline system that would optimize the route selection of subsea pipelines. The route optimization tool evaluates each possible route by incorporating Onbottom stability criteria based on DNVGL-ST-109 standard and other constraints such as the minimum pipeline route length, static obstacles, pipeline crossings, and free-span section length. The cost function in the optimization method simultaneously handles the minimization of length and cost of mitigating procedures. Genetic Algorithm, a well established optimization method, has been used as a reference to compare the optimal route with the result from the proposed Reinforcement Learning based optimization method. Three different case studies are performed for finding the optimal route selection using the Reinforcement Learning (RL) approach considering the OBS criteria into its cost function and compared with the Genetic Algorithm (GA). The RL method saves upto 20% pipeline length for a complex problem with 15 crossings and 31 free spans. The RL optimization method provides the optimal routes, considering different aspects of the design and the costs associated with the various factors to stabilize a pipeline (mattress, trenching, burying, concrete coating, or even employing a more massive pipe with additional steel wall thickness). OBS criteria significantly influence the best route, indicating that the tool can reduce the pipeline's design time and minimize installation and operational costs of the pipeline. Conventionally the pipeline route optimization is performed by a manual process where the minimum roule length and static obstacles are considered to find an optimum route. The engineering is then performed to fulfill the criteria of this route, and this approach may not lead to an optimized engineering cost. The proposed Reinforced Learning method for route optimization is a mixed type, faster, and cost-efficient approach. It significantly minimizes the pipeline's installation and operational costs up to 20% of the conventional route selection process.

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