scholarly journals Evaluation of spatially variable control parameters in a complex catchment modelling system: a genetic algorithm application

2007 ◽  
Vol 9 (3) ◽  
pp. 163-173 ◽  
Author(s):  
Tianjun Fang ◽  
James E. Ball
Keyword(s):  
Genetic Algorithm ◽  
Careful Consideration ◽  
Successful Implementation ◽  
Control Parameters ◽  
Catchment Modelling ◽  
Uniform Crossover ◽  
Variable Control ◽  
Real Value ◽  
Value Coding ◽  
Modelling System

Successful implementation of a catchment modelling system requires careful consideration of the system calibration which involves evaluation of many spatially and temporally variable control parameters. Evaluation of spatially variable control parameters has been an issue of increasing concern arising from an increased awareness of the inappropriateness of assuming catchment averaged values. Presented herein is the application of a real-value coding genetic algorithm (GA) for evaluation of spatially variable control parameters for implementation with the Storm Water Management Model (SWMM). It was found that a real-value coding GA using multiple storms calibration was a robust search technique that was capable of identifying the most promising range of values for spatially variable control parameters. As the selection of appropriate GA operators is an important aspect of the GA efficiency, a comprehensive investigation of the GA operators in a high-dimensional search space was conducted. It was found that a uniform crossover operation was superior to both one-point and two-point crossover operations over the whole range of crossover probabilities, and the optimal uniform crossover and mutation probabilities for the complex system considered were in the range of 0.75–0.90 and 0.01–0.1, respectively.

scholarly journals An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1194
Author(s):  
Thejus Pathmakumar ◽  
Madan Mohan Rayguru ◽  
Sriharsha Ghanta ◽  
Manivannan Kalimuthu ◽  
Mohan Rajesh Elara
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robots ◽  
Numerical Simulations ◽  
Labour Force ◽  
Successful Implementation ◽  
Multi Objective Optimization ◽  
Metallic Surfaces ◽  
Industrial Sites ◽  
Experimental Trials ◽  
Time And Energy

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.

Hybrid genetic algorithm-based optimization of powertrain and control parameters of plug-in hybrid electric bus

2015 ◽  
Vol 352 (3) ◽  
pp. 776-801 ◽  
Author(s):  
Liang Li ◽  
Yahui Zhang ◽  
Chao Yang ◽  
Xiaohong Jiao ◽  
Lipeng Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Control Parameters ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric ◽  
And Control

scholarly journals Research on the Interaction of Genetic Algorithm in Assisted Composition

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Han Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Value ◽  
Coding Method ◽  
Algorithmic Music ◽  
Computer Aided ◽  
Character Representation ◽  
Value Coding ◽  
Algorithmic Music Composition ◽  
Special Process

Computer-aided composition is an attempt to use a formalized process to minimize human (or composer) involvement in the creation of music using a computer. Exploring the problem of computer-aided composition can enable us to understand and simulate the thinking mode of composers in the special process of music creation, which is an important application of artificial intelligence in the field of art. Feature extraction on the MIDI files has been introduced in this paper. Based on the genetic algorithm in this paper, a platform of the sampling coding method to optimize the character representation has solved the traditional algorithmic music composition study. Music directly from the pitch and duration can be derived from the characteristics, respectively, in the form of a one-hot encoding independently said. Failure to the rhythm of the characterization of the pitch and duration are problems that lead to the inability of compositional networks to learn musical styles better. Rhythm is the combination of pitch and time values according to certain rules. The rhythm of music affects the overall style of music. By associating the pitch and time value coding, the rhythm style of music can be preserved better so that the composition network can learn the style characteristics of music more easily.

Simultaneous Modular Product Scheduling and Manufacturing Cell Reconfiguration Using a Genetic Algorithm

2006 ◽  
Vol 128 (4) ◽  
pp. 984-995 ◽  
Author(s):  
Hegui Ye ◽  
Ming Liang
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing System ◽  
Product Variety ◽  
Manufacturing Cost ◽  
Successful Implementation ◽  
Strong Impact ◽  
Reconfigurable Manufacturing ◽  
Product Variant ◽  
Modular Product ◽  
Work In Process

Modular product design can facilitate the diversification of product variety at a low cost. Reconfigurable manufacturing, if planned properly, is able to deliver high productivity and quick responsiveness to market changes. Together, the two could provide an unprecedented competitive edge to a manufacturing company. The production of a family of modular products in a reconfigurable manufacturing system often requires reorganizing the manufacturing system in such a way that each configuration corresponds to one product variant in the same family. The successful implementation of this strategy lies in proper scheduling of the modular product operations and optimal selection of a configuration for producing each product variant. These two issues are closely related and have a strong impact on each other. Nevertheless, they have often been treated separately, rendering inefficient, infeasible, and conflicting decisions. As such, an integrated model is developed to address the two problems simultaneously. The objective is to minimize the sum of the manufacturing cost components that are affected by the two planning decisions. These include reconfiguration cost, machine idle cost, material handling cost, and work-in-process cost incurred in producing a batch of product variants. Due to the combinatorial nature of the problem, a genetic algorithm (GA) is proposed to provide quick and near-optimal solutions. A case study is conducted using a steering column to illustrate the application of the integrated approach. Our computational experience shows that the proposed GA substantially outperforms a popular optimization software package, LINGO, in terms of both solution quality and computing efficiency.

scholarly journals Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3576 ◽  
Author(s):  
Aws Najm ◽  
Ibraheem Ibraheem ◽  
Ahmad Azar ◽  
Amjad Humaidi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Technique ◽  
Switching Topology ◽  
Control Law ◽  
Genetic Optimization ◽  
Multi Agent System ◽  
Control Parameters ◽  
Consensus Control ◽  
Communication Topology ◽  
Multi Agent

A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations.

Based on Ant Colony Algorithm Simulation of Single Crystal Alloy Finite Element

2012 ◽  
Vol 201-202 ◽  
pp. 549-552
Author(s):  
Shu Zhang ◽  
Lei Meng
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Ant Colony Algorithm ◽  
Fem Analysis ◽  
Control Parameters ◽  
Algorithm Optimization ◽  
Ant System ◽  
Ant Colonies ◽  
Ant Algorithms ◽  
System A

we have used the metaphor of ant colonies to define "the Ant system", a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some FEM algorithms. And in accordance with treatment principles, the microstructure of the alloy is simulated. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

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