scholarly journals Selection of heavy machinery for earthwork activities: A multi-objective optimization approach using a genetic algorithm

2022 ◽  
Author(s):  
Ali Shehadeh ◽  
Odey Alshboul ◽  
Omer Tatari ◽  
Mohammad A. Alzubaidi ◽  
Ahmed Hamed El-Sayed Salama
Keyword(s):  
Genetic Algorithm ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Heavy Machinery ◽  
Multi Objective ◽  
Selection Of

scholarly journals Optimization of the linear quadratic regulator (LQR) control quarter car suspension system using genetic algorithm

2016 ◽  
Vol 36 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Mahesh Nagarkar ◽  
G. J. Vikhe Patil
Keyword(s):  
Genetic Algorithm ◽  
Linear Quadratic Regulator ◽  
Suspension System ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Linear Quadratic ◽  
Multi Objective ◽  
Weighting Matrix ◽  
Car Suspension ◽  
Quarter Car

<p>In this paper, a genetic algorithm (GA) based in an optimization approach is presented in order to search the optimum weighting matrix parameters of a linear quadratic regulator (LQR). A Macpherson strut quarter car suspension system is implemented for ride control application. Initially, the GA is implemented with the objective of minimizing root mean square (RMS) controller force. For single objective optimization, RMS controller force is reduced by 20.42% with slight increase in RMS sprung mass acceleration. Trade-off is observed between controller force and sprung mass acceleration. Further, an analysis is extended to multi-objective optimization with objectives such as minimization of RMS controller force and RMS sprung mass acceleration and minimization of RMS controller force, RMS sprung mass acceleration and suspension space deflection. For multi-objective optimization, Pareto-front gives flexibility in order to choose the optimum solution as per designer’s need.</p>

A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
H. Maral ◽  
C. B. Şenel ◽  
K. Deveci ◽  
E. Alpman ◽  
L. Kavurmacıoğlu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tip Clearance ◽  
Design Parameters ◽  
Leakage Flow ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Tip Leakage Flow ◽  
Tip Leakage ◽  
Multi Objective ◽  
Blade Tip

Abstract Tip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The “squealer” design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.

Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm

2004 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
K. J. Jungowski ◽  
G. Poissant ◽  
H. Golshan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Pipeline ◽  
Multi Objective Optimization ◽  
Maximum Throughput ◽  
Multi Objective ◽  
Decision Variables ◽  
Pipeline Networks ◽  
Multiple Unit ◽  
Pareto Fronts ◽  
Selection Of

This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3 × 1029 cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85 × 1078 cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.

High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

2017 ◽  
Vol 232 (6) ◽  
pp. 734-750 ◽  
Author(s):  
Sayed E Mirmohammadsadeghi ◽  
H Amirabadi
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Pressure ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
304 Austenitic Stainless Steel

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

Multi-Objective Optimization for a Novel Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

2007 ◽  
Author(s):  
Yongquan Wang ◽  
Hualing Chen ◽  
Zhiying Ou ◽  
Xueming He
Keyword(s):  
Genetic Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Sensor ◽  
Energy Relations

In this paper, we present the multi-objective optimization for an entire microsystem, a novel capacitive electrostatic feedback accelerometer. From the energy relations of the coupled electrostatic-field, the dynamic model of the system is constructed. Aiming at the global performance, a multi-objective optimization model, where sensitivity, resolution and damping resonant frequency are selected as objectives, is established based on the concept of multidisciplinary design optimization (MDO). Genetic algorithm (GA) is used to solve this problem, and compared with a traditional optimization approach, sequence quadratic programming (SQP). Both the two algorithms can achieve our aim commendably, and the optimal solution given by GA is more satisfied. The research provides us a good foundation to develop the stochastic and implicit parallel properties of GA to obtain Pareto optimal solutions.

Micro-Genetic algorithm with fuzzy selection of operators for multi-Objective optimization: μFAME

2021 ◽  
Vol 61 ◽  
pp. 100818
Author(s):  
Alejandro Santiago ◽  
Bernabé Dorronsoro ◽  
Héctor J. Fraire ◽  
Patricia Ruiz
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Genetic Algorithm ◽  
Selection Of
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