The Optimization Method to Transfer Bus Routes for Rail Transit Terminal Station

2012 ◽  
Vol 253-255 ◽  
pp. 1869-1875
Author(s):  
Sheng Zhang ◽  
Wu Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Hill Climbing ◽  
Rail Transit ◽  
Hill Climbing Algorithm ◽  
Variation Rate ◽  
Encoding Method ◽  
The Hill ◽  
Terminal Station

The optimization model is framed with a goal to minimize overall consumption of travel time for passengers. A variety of constrains are considered, including time, capacity, stop number, profit and so on. According to the features of the model, the hill-climbing algorithm is adopted to obtain the initial solution, which reduces the time of optimization. Meanwhile, direct order encoding method, namely node method, is introduced for encoding, construct a Hybrid Genetic Algorithm for the solution. The results show that adapter value is more steady and the model result is preferable when the variation rate is increased while the number of iteration is decreased.

scholarly journals Optimization of Airport Shuttle Bus Routes Based on Travel Time Reliability

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Danwen Bao ◽  
Jiayu Gu ◽  
Zhiwei Di ◽  
Tianxuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Mutation Rate ◽  
Hybrid Genetic Algorithm ◽  
Estimation Method ◽  
Hill Climbing ◽  
Optimization Process ◽  
Travel Time Reliability ◽  
Hill Climbing Algorithm ◽  
Crossover Rate

An optimization model of airport shuttle bus routes is constructed by taking operational reliability maximization as a main goal in this paper. Also, a hybrid genetic algorithm is designed to solve this problem. Then the theoretical method is applied to the case of Nanjing Lukou International Airport. During the research, a travel time reliability estimation method is proposed based on back propagation (BP) neural network. Absolute error and regression fitting methods are used to test the measurement results. It is proved that this method has higher accuracy and is applicable to calculate airport bus routes reliability. In algorithm design, the hill-climbing algorithm with strong local search ability is integrated into genetic algorithm. Initial solution is determined by hill-climbing algorithm so as to avoid the search process falling into a local optimal solution, which makes the accuracy of calculation result improved. However, the calculation results show that the optimization process of hybrid genetic algorithm is greatly affected by both the crossover rate and mutation rate. A higher mutation rate or lower crossover rate will decrease the stability of the optimization process. Multiple trials are required to determine the optimal crossover rate and mutation rate. The proposed method provides a scientific basis for optimizing the airport bus routes and improving the efficiency of airport’s external transportation services.

Smooth gait optimization of a fish robot using the genetic-hill climbing algorithm

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 257-278 ◽  
Author(s):  
Tuong Quan Vo ◽  
Hyoung Seok Kim ◽  
Byung Ryong Lee
Keyword(s):  
Genetic Algorithm ◽  
Hill Climbing ◽  
Optimal Parameters ◽  
Fish Robot ◽  
Hill Climbing Algorithm ◽  
Smooth Motion ◽  
Simulation Results ◽  
The Hill ◽  
Gait Optimization ◽  
Input Torque

SUMMARYThis paper presents a model of a three-joint (four links) carangiform fish robot. The smooth gait or smooth motion of a fish robot is optimized by using a combination of the Genetic Algorithm (GA) and the Hill Climbing Algorithm (HCA) with respect to its dynamic system. Genetic algorithm is used to create an initial set of optimal parameters for the two input torque functions of the system. This set is then optimized by using HCA to ensure that the final set of optimal parameters is a “near” global optimization result. Finally, the simulation results are presented in order to demonstrate that the proposed method is effective.

scholarly journals Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2177
Author(s):  
Jun-Ho Huh ◽  
Jimin Hwa ◽  
Yeong-Seok Seo
Keyword(s):  
Genetic Algorithm ◽  
Software Architecture ◽  
Large Scale ◽  
Structural Characteristics ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Software Systems ◽  
Small Scale ◽  
Hill Climbing Algorithm ◽  
Architecture Management

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

2019 ◽  
Vol 12 (4) ◽  
pp. 153-170 ◽  
Author(s):  
Guefrouchi Ryma ◽  
Kholladi Mohamed-Khireddine
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ontology Mapping ◽  
Hill Climbing ◽  
Computational Time ◽  
Mutation Operator ◽  
Mapping Process ◽  
The Hill

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

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.

Application of a Hybrid Algorithm Based on Genetic Algorithm and Hill-Climbing Algorithm to Tool Path Optimization in CNC Machining

2010 ◽  
Vol 102-104 ◽  
pp. 681-685 ◽  
Author(s):  
Hai Qing Du ◽  
Ji Bao Qi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Tool Path ◽  
Cnc Machining ◽  
Path Optimization ◽  
Hill Climbing ◽  
Tool Path Optimization ◽  
Tool Paths ◽  
Hill Climbing Algorithm ◽  
Accelerate Convergence

The efficiency of CNC machining is greatly influenced by the tool path. A new hybrid algorithm for tool path optimization in CNC varied-shape grinding is presented in this paper. The algorithm was constructed by adding hill-climbing algorithm to nature genetic algorithm. In the new algorithm, the crossover operator and mutation operator were redesigned to enhance the local search capability and to accelerate convergence. Verification experiment demonstrated that the algorithm can reduce non-cutting movement of tool paths and improve machining efficiency significantly.

A HYBRID GENETIC ALGORITHM AND EXPECTATION MAXIMIZATION METHOD FOR GLOBAL GENE TRAJECTORY CLUSTERING

2005 ◽  
Vol 03 (05) ◽  
pp. 1227-1242 ◽  
Author(s):  
ZEKE S. H. CHAN ◽  
N. KASABOV ◽  
LESLEY COLLINS
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Gene Expression Data ◽  
Expectation Maximization ◽  
Time Course ◽  
Human Fibroblasts ◽  
Hybrid Genetic Algorithm ◽  
Hill Climbing ◽  
Global Optimality ◽  
Expression Data

Clustering time-course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network modeling and discovery as it significantly reduces the dimensionality of the gene space required for analysis. Traditional clustering methods that perform hill-climbing from randomly initialized cluster centers are prone to produce inconsistent and sub-optimal cluster solutions over different runs. This paper introduces a novel method that hybridizes genetic algorithm (GA) and expectation maximization algorithms (EM) for clustering gene trajectories with the mixtures of multiple linear regression models (MLRs), with the objective of improving the global optimality and consistency of the clustering performance. The proposed method is applied to cluster the human fibroblasts and the yeast time-course gene expression data based on their trajectory similarities. It outperforms the standard EM method significantly in terms of both clustering accuracy and consistency. The biological implications of the improved clustering performance are demonstrated.

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