A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization

2012 ◽  
Vol 622-623 ◽  
pp. 64-68 ◽  
Author(s):  
S. Padmanabhan ◽  
M. Chandrasekaran ◽  
P. Asokan ◽  
V. Srinivasa Raman
Keyword(s):  
Genetic Algorithm ◽  
Power Efficiency ◽  
Mechanical Design ◽  
Design Solution ◽  
Functional Requirements ◽  
Performance Study ◽  
Gear Design ◽  
Adverse Result ◽  
Real Coded Genetic Algorithm ◽  
Comparative Results

he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the design of helical gear pair and a combined objective function with maximizes the Power, Efficiency and minimizes the overall Weight, Centre distance. The performance of the proposed algorithms is validated through LINGO Software and the comparative results are analyzed.

Design Optimization of Spur and Helical Gear Pairs

2015 ◽  
Vol 766-767 ◽  
pp. 1034-1043
Author(s):  
S. Sudhagar ◽  
V. Srinivasa Raman
Keyword(s):  
Design Optimization ◽  
Power Efficiency ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Helical Gear ◽  
Gear Train ◽  
Test Problems ◽  
Gear Pair ◽  
Real Coded Genetic Algorithm ◽  
Comparative Results

Gears are the most common of machine elements and due to that many studies have been conducted on optimum gear design. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs design optimization is difficult to solve because it involves multiple objectives and large number of variables. Hence a trustworthy and resilient optimization technique will be more useful in obtaining an optimal solution for the problems. In the proposed work an effort has been made to optimize spur and helical gear pair design using LINGO and Meta heuristics algorithms like Real Coded Genetic Algorithm (RCGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).On applying the combined objective function factors like Power, Efficiency is maximized and the overall Weight, Centre distance has been minimized in the model. The performance of the proposed algorithms is validated through test problems and the comparative results are reported.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansur Mohammed Ali Gamel ◽  
Pin Jern Ker ◽  
Hui Jing Lee ◽  
Wan Emilin Suliza Wan Abdul Rashid ◽  
M. A. Hannan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Waste Heat ◽  
Antireflection Coating ◽  
Base Layer ◽  
Average Percentage ◽  
Cell Efficiency ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Dimensional Optimization

AbstractThe optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

Genetic algorithm- and finite element-based design and analysis of nonprismatic piezolaminated beam for optimal vibration energy harvesting

2015 ◽  
Vol 230 (14) ◽  
pp. 2532-2548 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Harvesting ◽  
Output Power ◽  
Governing Equation ◽  
Optimization Technique ◽  
Vibration Energy ◽  
Fe Model ◽  
Vibration Energy Harvesting ◽  
Real Coded Genetic Algorithm

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.

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