Multi-Objective Optimal Design of a Passenger Car’s Body

2010 ◽  
Author(s):  
Amir Hosein Adl ◽  
Masoud Shariat Panahi
Keyword(s):  
Fatigue Life ◽  
Numerical Model ◽  
Natural Frequency ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Early Stage ◽  
Computational Cost ◽  
The Body ◽  
Fe Model ◽  
Multi Objective

The body of a passenger car roughly constitutes 25–30% of its overall weight. Any reduction in the weight of the car’s body would not only mean less materials and fuel to be consumed, but also less exhaust emissions to be released and less non-biodegradable materials to be dumped or recycled. However, the automotive industry’s desire for an increasing weight reduction of passenger cars is inevitably limited by other design considerations such as mechanical strength, overall stiffness of the body, durability, safety and corrosion resistance. The problem of weight minimization can be expressed in the form of a constrained, multi-objective optimization problem in which the weight of the body and its fatigue life constitute the conflicting cost functions and values of such critical performance parameters as body’s natural frequency forms the constraint set. The above optimization problem poses a challenge to the designer, as the weight, fatigue life and natural frequency of the geometrically complex body cannot be readily evaluated and a comprehensive numerical model, such as a Finite-Elements (FE) one, has to be employed. This numerical model would nonetheless be highly time-consuming, especially considering the need for re-assessing the model dozens, and sometimes hundreds, of times per iteration of the optimization algorithm. To avoid this, we use a neural approximation of the FE model to reduce the time and computational cost. Results of a finite number of FE simulations are used to train the Multi-Layer Perceptron (MLP) neural network which will then be used as the evaluation engine of the optimization algorithm. An efficient computer code based on the improved Non-dominated Sorting Genetic Algorithms (NSGA II) is used to find the Pareto set of distinct solutions. The designer would then be able to choose from a set of non-dominated, feasible solutions based on economical and/or logistics requirements at an early stage of the design process.

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

2019 ◽  
Vol 33 (08) ◽  
pp. 1959025
Author(s):  
Lan Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Superior Performance ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Evolution Algebra ◽  
Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms

2009 ◽  
Vol 131 (9) ◽  
Author(s):  
Rajesh Kudikala ◽  
Deb Kalyanmoy ◽  
Bishakh Bhattacharya
Keyword(s):  
Optimization Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Piezoelectric Actuators ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Control ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

scholarly journals Multi-objective Optimization of Compliant Parallel Platform for Desired Stiffness and Workspace

2021 ◽  
Author(s):  
Hongwei Xu ◽  
Haibo Zhou ◽  
Zhiqiang Li ◽  
Xia Ju
Keyword(s):  
Numerical Model ◽  
Optimization Problem ◽  
Structural Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Element Analysis ◽  
Multi Objective ◽  
Performance Indexes ◽  
Parallel Platform ◽  
The Relationship

Abstract Stiffness and workspace are crucial performance indexes of a precision mechanism. In this paper, an optimization method is presented, for a compliant parallel platform to achieve desired stiffness and workspace. First, a numerical model is proposed to reveal the relationship between structural parameters, desired stiffness and workspace of the compliant parallel platform. Then, the influence of the various parameters on stiffness and workspace of the platform is analyzed. Based on Gaussian distribution, the multi-objective optimization problem is transformed into a single-objective one, in order to guarantee convergence precision. Furthermore, particle swarm optimization is used to optimize the structural parameters of the platform, which significantly improve its stiffness and workspace. Last, the effectiveness of the proposed numerical model is verified by finite element analysis and experiment.

Generating Power From Ocean Waves Using a Float With Excessive Buoyancy: Theory and Dynamic Model Results

2003 ◽  
Author(s):  
T. MacCready ◽  
T. Zambrano ◽  
B. D. Hibbs
Keyword(s):  
Numerical Model ◽  
Natural Frequency ◽  
Power Output ◽  
Ocean Surface ◽  
Ocean Waves ◽  
The Body ◽  
Energy Extraction ◽  
Ocean Energy ◽  
Wave Conditions ◽  
Rich Data

We are exploring a new approach to ocean energy extraction through a device that we refer to as the NAF (an acronym for Non-Archimedean Float). The NAF is a fully submerged body with excess buoyancy; i.e., the mass of the body is far less than the mass of the water it displaces. When such a float is tethered beneath the ocean surface the buoyancy yields a large force vector in the direction perpendicular to the isobaric surfaces that parallel the water/air interface. The constant shifting of the wave troughs provides the opportunity for energy extraction using turbines affixed to the float. We are exploring the NAF concept because its simplicity results in many inherent benefits. The device has few moving parts, gathers energy from waves coming in any direction, and exists as a non-obtrusive, completely submerged installation. A numerical model of the NAF has been created to determine the dynamic behavior and power output for various configurations and under various wave conditions. The numerical model is set up to calculate the various forces experienced by the NAF float, and from these it calculates the velocity and position of the float through time series steps. The model effectively demonstrates which variables are important and how power output relates to NAF dimensions. One early finding from the model result relates to tuning the natural frequency of the NAF to match the natural frequency of the waves. The NAF moves like an inverted pendulum, and its natural frequency is primarily dependent on the length of the pendulum. Regardless of the actual float buoyancy, the 6 to 12 second periods that typify average wave conditions dictate that the NAF tether should be between 30-m and 60-m long. Also, a scale version of this novel energy device consisting of a float tethered beneath the ocean surface was deployed off the coast of southern California. The deployment yielded rich data sequences that are sufficient for comparison with a dynamic numerical model.

Optimum two-plane balancing of rigid rotor using discrete optimization algorithm

2019 ◽  
Vol 16 (1) ◽  
pp. 138-146 ◽  
Author(s):  
Prem Singh ◽  
Himanshu Chaudhary
Keyword(s):  
Discrete Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Jaya Algorithm ◽  
Rigid Rotor ◽  
Objective Functions ◽  
Content Type ◽  
Multi Objective ◽  
Ga Algorithm ◽  
Reaction Forces

Purpose This paper aims to present the optimum two-plane discrete balancing procedure for rigid rotor. The discrete two-plane balancing in which rotor is balanced to minimize the residual effects or the reactions on the bearing supports using discrete parameters such as masses and their angular positions on two balancing planes. Design/methodology/approach Therefore as a multi-objective optimization problem is formulated by considering reaction forces on the bearing supports as a multi objective functions and discrete parameters on each balancing plane as design variables. These multi-objective functions are converted into a single-objective function using appropriate weighting factors. Further, this optimization problem is solved using discrete optimization algorithm, based on Jaya algorithm. Findings The performance of the discrete Jaya algorithm is compared to genetic algorithm (GA) algorithm. It is found that Jaya algorithm is computationally more efficient than GA algorithm. A number of masses per plane are used to balance the rotor. A comparison of reaction forces using number of masses per plane is investigated. Originality/value The effectiveness of the proposed methodology is tested by the balancing problem of rotor available in the literature. The influence of a number of balance masses on bearing forces and objective function are discussed. ADAMS software is used for validation of a developed balancing approach.

INFLUENCE OF OBJECTIVE FUNCTIONS IN STRUCTURAL DAMAGE IDENTIFICATION USING REFINED AND SIMPLE MODELS

2009 ◽  
Vol 09 (04) ◽  
pp. 607-625 ◽  
Author(s):  
RICARDO PERERA ◽  
SHENG-EN FANG
Keyword(s):  
Dynamic Response ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Computational Cost ◽  
Computational Time ◽  
Fe Model ◽  
Beam Model ◽  
Objective Functions ◽  
Multi Objective

The most usual approach for solving damage identification problems is the use of the finite element (FE) model updating method. To apply the method, a minimization of an objective function measuring the fit between measured and model predicted data is performed. Then, the success of the procedure depends strongly on the accuracy of the FE model and the choice of a suitable objective function. Although detailed FE models provide an accurate means for calculating the dynamic response of the structure, their size and complexity involve a large number of parameters to be updated and a high computational cost. In order to shorten the computational time, more simplified and practical models able to model the global dynamic response of the structure accurately would be desirable. Furthermore, working with several objective functions instead of only one would increase the robustness and performance of the procedure. In this paper, a multi-objective simple beam model is proposed and compared with a more refined model based on plane elements. Furthermore, in the multi-objective framework, different combinations of objective functions are studied. The reliability and effectiveness of the proposed model has been evaluated in a damage detection problem of a reinforced concrete frame experimentally tested under different levels of damage.

scholarly journals A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

2012 ◽  
Vol 6-7 ◽  
pp. 445-451
Author(s):  
Chang Sheng Zhang ◽  
Ming Kang Ren ◽  
Bin Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Solution Quality ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

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