scholarly journals A genetic algorithm rooted in integer encoding and fuzzy controller

2019 ◽  
Vol 8 (2) ◽  
pp. 113
Author(s):  
M. Jalali Varnamkhasti
Keyword(s):  
Genetic Diversity ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Search Algorithms ◽  
Benchmark Problems ◽  
Selection Mechanism ◽  
Genetic Operators ◽  
Local Search Algorithms ◽  
Loss Of Genetic Diversity ◽  
Essential Problem

The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.

scholarly journals A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
M. Jalali Varnamkhasti ◽  
L. S. Lee
Keyword(s):  
Fuzzy Logic Controller ◽  
Fundamental Problem ◽  
Population Diversity ◽  
Premature Convergence ◽  
Knapsack Problems ◽  
Genetic Operators ◽  
Local Search Algorithms ◽  
Multidimensional Knapsack ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic

The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focuses on controlling the genetic parameters by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with other genetic operators, heuristics, and local search algorithms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.

scholarly journals Aesthetic local search of wind farm layouts

2017 ◽  
Author(s):  
Michael Mayo ◽  
Maisa Daoud
Keyword(s):  
Energy Efficiency ◽  
Local Search ◽  
Wind Farm ◽  
Search Algorithms ◽  
Benchmark Problems ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Wind Farm Layout ◽  
Cost Of Energy ◽  
Visual Impact

The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or cost of energy alone. In this paper, we consider the geometry of wind farm layouts and whether it is possible to bi-optimise a layout for both energy efficiency and the degree of visual impact that the layout exhibits. We develop a novel optimisation approach for solving the problem, with our approach towards measuring mathematically the degree of visual impact drawing inspiration from the field of architecture. To evaluate our ideas, we demonstrate them on three benchmark problems for the wind farm layout optimisation problem in conjunction with two recently published stochastic local search algorithms. Optimal patterned layouts are shown to be very close in terms of energy efficiency to optimal non-patterned layouts.

scholarly journals Aesthetic local search of wind farm layouts

2017 ◽  
Author(s):  
Michael Mayo ◽  
Maisa Daoud
Keyword(s):  
Energy Efficiency ◽  
Local Search ◽  
Wind Farm ◽  
Search Algorithms ◽  
Benchmark Problems ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Wind Farm Layout ◽  
Cost Of Energy ◽  
Visual Impact

The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or cost of energy alone. In this paper, we consider the geometry of wind farm layouts and whether it is possible to bi-optimise a layout for both energy efficiency and the degree of visual impact that the layout exhibits. We develop a novel optimisation approach for solving the problem, with our approach towards measuring mathematically the degree of visual impact drawing inspiration from the field of architecture. To evaluate our ideas, we demonstrate them on three benchmark problems for the wind farm layout optimisation problem in conjunction with two recently published stochastic local search algorithms. Optimal patterned layouts are shown to be very close in terms of energy efficiency to optimal non-patterned layouts.

The Local Search Algorithms for Maximum 3-Satisfiablility Problem

2010 ◽  
Vol 33 (7) ◽  
pp. 1127-1139
Author(s):  
Da-Ming ZHU ◽  
Shao-Han MA ◽  
Ping-Ping ZHANG
Keyword(s):  
Local Search ◽  
Search Algorithms ◽  
Local Search Algorithms

Evolutionary genetics of small populations

2017 ◽  
Author(s):  
Richard Frankham ◽  
Jonathan D. Ballou ◽  
Katherine Ralls ◽  
Mark D. B. Eldridge ◽  
Michele R. Dudash ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Size ◽  
Evolutionary Genetics ◽  
Small Population ◽  
Small Populations ◽  
Effective Population ◽  
Genetic Management ◽  
Evolutionary Genetic ◽  
Loss Of Genetic Diversity ◽  
Number Of Individuals

Genetic management of fragmented populations involves the application of evolutionary genetic theory and knowledge to alleviate problems due to inbreeding and loss of genetic diversity in small population fragments. Populations evolve through the effects of mutation, natural selection, chance (genetic drift) and gene flow (migration). Large outbreeding, sexually reproducing populations typically contain substantial genetic diversity, while small populations typically contain reduced levels. Genetic impacts of small population size on inbreeding, loss of genetic diversity and population differentiation are determined by the genetically effective population size, which is usually much smaller than the number of individuals.

Multi-objective adaptive cruise controller design using nonlinear predictive controller with the objective function with variable weights determined by fuzzy logic controller

2021 ◽  
pp. 095440702110147
Author(s):  
Behzad Samani ◽  
Amir H. Shamekhi
Keyword(s):  
Objective Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Hierarchical Control ◽  
Control Input ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Control Step ◽  
Predictive Controller

In this paper, an adaptive cruise control system with a hierarchical control structure is designed. The upper-level controller is a model predictive controller (MPC) that by minimizing an objective function in the presence of the constraints, calculates the desired acceleration as control input and sends it to the lower-level controller. So the lower-level controller, which is a fuzzy controller, determines the amount of throttle valve opening or brake pressure to get the car to this desired acceleration. The model predictive controller performs optimization at each control step to minimize the objective function and achieve the reference values. Usually, the objective function has predetermined and constant weights to meet objectives such as maintain the driver’s desired speed and increase safety and in some cases increase comfort and reduce fuel consumption. In this paper, it is suggested that instead of using constant weights in the objective function, these weights should be determined by a fuzzy controller, depending on the different conditions in which the car is placed. The simulation results show that the variability of the weights of the objective function achieves control objectives much better than the optimization of the objective function with constant weights.

Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

2011 ◽  
Vol 403-408 ◽  
pp. 5068-5075
Author(s):  
Fatma Zada ◽  
Shawket K. Guirguis ◽  
Walied M. Sead
Keyword(s):  
Fuzzy Logic ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
System Response ◽  
Robot Arm ◽  
Neural Controller ◽  
Hybrid Controller ◽  
Neural Fuzzy ◽  
Transient And Steady State

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

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