Real-time Engine Control Parameters Optimization Method for Small Diesel Engine by Multi Objective Genetic Algorithm

2009 ◽  
Vol 2 (1) ◽  
pp. 134-144 ◽  
Author(s):  
Atsushi Sakawaki ◽  
Hirotaka Kaji ◽  
Minoru Yamamoto ◽  
Shigeho Sakoda
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Real Time ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Engine Control ◽  
Control Parameters ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Optimization of a Star Pattern Recognition Algorithm for Attitude Determination Using a Multi-Objective Genetic Algorithm

2005 ◽  
Author(s):  
Ebenezer Seisie-Amoasi ◽  
Brian G. Williams ◽  
Marco P. Schoen
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Attitude Determination ◽  
Recognition Algorithm ◽  
Control Parameters ◽  
Pattern Recognition Algorithm ◽  
Multi Objective ◽  
Star Pattern ◽  
Multi Objective Genetic Algorithm ◽  
Star Pattern Recognition

Attitude determination for unmanned spacecrafts usually employs star trackers. The specifications for these devices dictate fast, reliable, robust, and autonomous algorithms to satisfy various mission constraints. This results into simple algorithms for reduced power consumption and reduced overall weight. Optimizing a Star Pattern Recognition Algorithm (SPRA), using an imbedded star map, requires the optimization of the genetic operators that constitute the SPRA and the control parameters within the SPRA. Simultaneous optimization of the control parameters of the SPRA results into a multi-objective and multi-parameter constrained optimization problem. The optimizing of genetic algorithms is often time consuming and rather tedious by nature. In this work, a Multi-Objective Genetic Algorithm (MOGA) acting as a meta-level GA is applied together with a double objective transition selection scheme to achieve the optimization. This approach results in significantly expediting the cost assignment process. By evolving a pareto set, an optimization population element rule is determined to exist between the control parameters of the SPRA. The existence of this rule ensures effective balance between population exploitation and exploration in the algorithm estimation process. This leads to effective solutions for finding the optimum with multiple concurrent objectives while taking the constraints into consideration. Simulation results using the optimized parameters for the SPRA indicate an improvement of the recognition accuracy from less than 60% to 100% as well as a reduction of the processing time of over 2000 generations to under 250 generations at 99% precision.

A New Interval Multi-Objective Optimization Method for Uncertain Problems with Dependent Interval Variables

2020 ◽  
Vol 17 (10) ◽  
pp. 2050007
Author(s):  
Guiping Liu ◽  
Rui Luo ◽  
Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Iterative Algorithm ◽  
Correlation Coefficients ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Interval Model ◽  
Numerical Examples ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Interval Variables

In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

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