Multi-Objective Crashworthiness Optimization of Composite Hat-Shape Energy Absorber Using GMDH-Type Neural Networks and Genetic Algorithms

2010 ◽  
Author(s):  
Naser Tavassoli ◽  
Bahram Notghi ◽  
Abolfazl Darvizeh ◽  
Mansour Darvizeh
Keyword(s):  
Neural Networks ◽  
Energy Absorption ◽  
Failure Criterion ◽  
Design Procedure ◽  
Vehicle Body ◽  
Group Method ◽  
Nsga Ii ◽  
Car Body ◽  
Multi Objective ◽  
Conflicting Objectives

Reducing the weight of car body and increasing the crashworthiness capability of car body are two important objectives of car design. In this paper, a multi-objective optimization for optimal composite hat-shape energy absorption system is presented At the first, the behaviors of the hat shape under impact, as simplified model of side member of a vehicle body, are studied by the finite element method using commercial software ABAQUS. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are then achieved for modeling of both the absorbed energy (E) and the Tsai-Hill Failure Criterion (TS) with respect to geometrical design variables using those training and testing data obtained models. The obtained polynomial neural meta-models are finally used in a multi-objective optimum design procedure using NSGA-II with a new diversity preserving mechanism for Pareto based optimization of hat-shape. Two conflicting objectives such as maximizing the energy absorption capability (E), minimizing the Tsai-Hill Failure Criterion are considered in this work.

MODELING AND MULTI-OBJECTIVE OPTIMIZATION OF FORWARD-CURVED BLADE CENTRIFUGAL FANS USING CFD AND NEURAL NETWORKS

2011 ◽  
Vol 35 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Abolfazl Khalkhali ◽  
Mehdi Farajpoor ◽  
Hamed Safikhani
Keyword(s):  
Neural Networks ◽  
Second Step ◽  
Group Method ◽  
Centrifugal Fan ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Design Variables ◽  
Conflicting Objectives ◽  
Centrifugal Fans ◽  
Curved Blade

In the present study, multi-objective optimization of Forward-Curved (FC) blade centrifugal fans is performed in three steps. In the first step, Head rise (HR) and the Head loss (HL) in a set of FC centrifugal fan is numerically investigated using commercial software NUMECA. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, in the second step, for modeling of HR and HL with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of FC centrifugal fans considering two conflicting objectives, HR and HL.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

scholarly journals Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

2021 ◽  
Vol 9 (8) ◽  
pp. 888
Author(s):  
Qasem Al-Tashi ◽  
Emelia Akashah Patah Akhir ◽  
Said Jadid Abdulkadir ◽  
Seyedali Mirjalili ◽  
Tareq M. Shami ◽  
...  
Keyword(s):  
Feature Selection ◽  
Recovery Factor ◽  
Classification Error ◽  
Grey Wolf Optimizer ◽  
Small Subset ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives

The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.

scholarly journals Long-Term Hydropower Generation Scheduling of Large-Scale Cascade Reservoirs Using Chaotic Adaptive Multi-Objective Bat Algorithm

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2373 ◽  
Author(s):  
Lyuwen Su ◽  
Kan Yang ◽  
Hu Hu ◽  
Zhe Yang
Keyword(s):  
Large Scale ◽  
Southern China ◽  
Bat Algorithm ◽  
Economic Benefits ◽  
Population Diversity ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Logical Mapping ◽  
Hydropower Reservoirs

With growing concerns over renewable energy, the cascade hydropower reservoirs operation (CHRO), which balances the development of economic benefits and power supply security, plays an increasingly important role in hydropower systems. Due to conflicting objectives and complicated operation constraints, the CHRO problem considering the requirements of maximizing power generation benefit and firm power output is determined as a multi-objective optimization problem (MOP). In this paper, a chaotic adaptive multi-objective bat algorithm (CAMOBA) is proposed to solve the CHRO problem, and the external archive set is added to preserve non-dominant solutions. Meanwhile, population initialization based on the improved logical mapping function is adopted to improve population diversity. Furthermore, the self-adaptive local search strategy and mutation operation are designed to escape local minima. The CAMOBA is applied to the CHRO problem of the Qingjiang cascade hydropower stations in southern China. The results show that CAMOBA outperforms the multi-objective bat algorithm (MOBA) and non-dominated sorting genetic algorithms-II (NSGA-II) in different hydrological years. The spacing (SP) and hypervolume (HV) metrics verify the excellent performance of CAMOBA in diversity and convergence. In summary, the CAMOBA is demonstrated to get better scheduling solutions, providing an effective approach for solving the cascade hydropower reservoirs operation (CHRO).

Optimal Photovoltaic System Design with Multi-Objective Optimization

2013 ◽  
Vol 4 (4) ◽  
pp. 63-89 ◽  
Author(s):  
Amin Ibrahim ◽  
Farid Bourennani ◽  
Shahryar Rahnamayan ◽  
Greg F. Naterer
Keyword(s):  
Incident Energy ◽  
State Of The Art ◽  
Photovoltaic System ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Generalized Differential ◽  
Maximum Field ◽  
Real World Problems

Recently, several parts of the world suffer from electrical black-outs due to high electrical demands during peak hours. Stationary photovoltaic (PV) collector arrays produce clean and sustainable energy especially during peak hours which are generally day time. In addition, PVs do not emit any waste or emissions, and are silent in operation. The incident energy collected by PVs is mainly dependent on the number of collector rows, distance between collector rows, dimension of collectors, collectors inclination angle and collectors azimuth, which all are involved in the proposed modeling in this article. The objective is to achieve optimal design of a PV farm yielding two conflicting objectives namely maximum field incident energy and minimum of the deployment cost. Two state-of-the-art multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Generalized Differential Evolution Generation 3 (GDE3) are compared to design PV farms in Toronto, Canada area. The results are presented and discussed to illustrate the advantage of utilizing MOEA in PV farms design and other energy related real-world problems.

Reference Point Based Multi-Objective Optimization to Workflow Grid Scheduling

2012 ◽  
Vol 3 (1) ◽  
pp. 80-99 ◽  
Author(s):  
Ritu Garg ◽  
Awadhesh Kumar Singh
Keyword(s):  
Reference Point ◽  
Simulation Analysis ◽  
Computation Time ◽  
Grid Scheduling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Computing ◽  
Conflicting Objectives ◽  
Multiple Regions ◽  
Np Hard Problem

Grid provides global computing infrastructure for users to avail the services supported by the network. The task scheduling decision is a major concern in heterogeneous grid computing environment. The scheduling being an NP-hard problem, meta-heuristic approaches are preferred option. In order to optimize the performance of workflow execution two conflicting objectives, namely makespan (execution time) and total cost, have been considered here. In this paper, reference point based multi-objective evolutionary algorithms, R-NSGA-II and R-e-MOEA, are used to solve the workflow grid scheduling problem. The algorithms provide the preferred set of solutions simultaneously, near the multiple regions of interest that are specified by the user. To improve the diversity of solutions we used the modified form of R-NSGA-II (represented as M-R-NSGA-II). From the simulation analysis it is observed that, compared to other algorithms, R-e-MOEA delivers better convergence, uniform spacing among solutions keeping the computation time limited.

Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

2018 ◽  
Vol 11 (1) ◽  
pp. 20-46 ◽  
Author(s):  
K. Shankar ◽  
Akshay S. Baviskar
Keyword(s):  
Engineering Design ◽  
Nsga Ii ◽  
Design Problems ◽  
Content Type ◽  
Multi Objective ◽  
Decision Variables ◽  
Engineering Design Problems ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Utopia Point

Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

PARETO BASED MULTI-OBJECTIVE OPTIMIZATION OF SOLAR THERMAL ENERGY STORAGE USING GENETIC ALGORITHMS

2010 ◽  
Vol 34 (3-4) ◽  
pp. 463-474 ◽  
Author(s):  
Abolfazl Khalkhali ◽  
Mohamadhosein Sadafi ◽  
Javad Rezapour ◽  
Hamed Safikhani
Keyword(s):  
Genetic Algorithms ◽  
Solar System ◽  
Geometrical Parameters ◽  
Net Energy ◽  
Solar Thermal Energy ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Conflicting Objectives ◽  
Change Material

Net energy stored (Q net) and the discharge time of Phase Change Material (t PCM) in a solar system, are important conflicting objectives to be optimized simultaneously. In the present paper, multi-objective genetic algorithms (GAs) are used for Pareto approach optimization of a solar system using modified NSGA II algorithms. The competing objectives are Q net and t PCM and design variables are some geometrical parameters of solar system. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of solar system can be discovered. These important results can be used for better design of a solar system.

Multi-Objective Optimization of Parameters for Milling Using Evolutionary Algorithms and Artificial Neural Networks

2019 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu-Mahfouz ◽  
AHM Esfakur Rahman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Cost Function ◽  
Machine Learning Algorithms ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Artificial Neural

Abstract Model-based design of manufacturing processes have been gaining popularity since the advent of machine learning algorithms such as evolutionary algorithms and artificial neural networks (ANN). The problem of selecting the best machining parameters can be cast an optimization problem given a cost function and by utilizing an input-output connectionist framework using as ANNs. In this paper, we present a comparison of various evolutionary algorithms for parameter optimization of an end-milling operation based on a well-known cost function from literature. We propose a modification to the cost function for milling and include an additional objective of minimizing surface roughness and by using NSGA-II, a multi-objective optimization algorithm. We also present comparison of several population-based evolutionary search algorithms such as variants of particle swarm optimization, differential evolution and NSGA-II.

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