scholarly journals Multi-Objective Optimization of Friction Stir Spot-Welded Parameters on Aluminum Alloy Sheets Based on Automotive Joint Loads

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 520 ◽  
Author(s):  
Biao Zhang ◽  
Xin Chen ◽  
Kaixuan Pan ◽  
Jianing Wang
Keyword(s):  
Axial Stress ◽  
Peel Strength ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Friction Stir ◽  
Multi Objective ◽  
Load Mode ◽  
Weld Parameters ◽  
Spot Welded

By controlling various friction stir spot-welded (FSSW) factors, two base sheets AA 5052-H32 and 6061-T6 were selected to bond similar and dissimilar metal joints while considering dissimilar configuration orders. The effects of weld parameters on the sheer strength and peel strength were separately developed into empirical models utilizing the integrated central composite matrix design and response surface methodology (RSM). Meanwhile, the finite element (FE) analysis of the multi-axis load-bearing characteristics for automotive solder joints during service was carried out. As a result, the weights of the shear and axial stress, accounting for 90.5% and 9.5% respectively, were employed to restrict the relationship between multiple target properties, and the resulting security strength was applied to determine the feasible domain in subsequent parametric optimization. In order to enable the optimal multi-axis capacities in accordance with the load mode, the genetic algorithm NSGA-II was chosen to compute the Pareto front and further determine the best compromise solutions. The obtained optimums corresponding to each joining condition were validated by confirmation runs, indicating that this coupled multi-objective optimization approach based on working conditions was beneficial to the targeted improvement of post-weld mechanical properties.

Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-II

2014 ◽  
Vol 7 (3) ◽  
pp. 906-916 ◽  
Author(s):  
Mehran Shaygan ◽  
Abbas Alimohammadi ◽  
Ali Mansourian ◽  
Zohreh Shams Govara ◽  
S. Mostapha Kalami
Keyword(s):  
Land Use ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Land Use Allocation

scholarly journals LAND-USE PLANNING FOR SUSTAINABLE URBAN DEVELOPMENT IN AFRICA: A SPATIAL AND MULTI-OBJECTIVE OPTIMIZATION APPROACH

2019 ◽  
Vol 45 (5) ◽  
pp. 1-15 ◽  
Author(s):  
Alex Lubida ◽  
Mozafar Veysipanah ◽  
Petter Pilesjo ◽  
Ali Mansourian
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Geographical Information ◽  
Sustainable Urban Development ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
The City

Land-use planning, which requires finding a balance among different conflicting social, economic and environment factors, is a complex task needed everywhere, including Africa. One example is the city of Zanzibar in Tanzania, which is under special consideration for land-use revision. From one side, the city has high potentials for tourist industry and at the other side there are major challenges with the city structure and poor accessibilities. In order to prepare a proper land-use plan for the city, a variety of influencing conflicting factors needs to be considered and satisfied. This can be regarded as a common problem in many African cities, which are under development. This paper aims to address the problem by proposing and demonstrating the use of Geographical Information System (GIS) and multi-objective optimization for land-use planning, in Zanzibar as a case study. The measures which have been taken by Zanzibar government to address the development challenges through the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) were identified by studying related documents and interviewing experts. Based on these, two objective functions were developed for land-use planning. Optimum base land-use plans were developed and mapped by optimizing the objective functions using the NSGA-II algorithm. The results show that the proposed approach and outputs can considerably facilitate land-use planning in Zanzibar. Similar approaches are highly recommended for other cities in Africa which are under development.

Multi-Objective Optimization Approach with Job-Based Encoding Method for Semiconductor Final Testing Scheduling Problem

2012 ◽  
Vol 622-623 ◽  
pp. 152-157
Author(s):  
Yi Sun ◽  
Xin Wei ◽  
Shigeru Fujimura ◽  
Gen Ke Yang
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Penalty Cost ◽  
Multi Objective ◽  
Testing Stage ◽  
Encoding Method

The semiconductor final testing scheduling problem (SFTSP) is a variation of the complex scheduling problem, which deals with the arrangement of the job sequence for the final testing process. In this paper, we present an actual SFTSP case includes almost all the flow-shop factors as reentry characteristic, serial and batch processing stages, lot-clusters and parallel machines. Since the critical equipment needs to be utilized efficiently at a specific testing stage, the scheduling arrangement is then playing an important role in order to reduce both the makespan and penalty cost of all late products in total final testing progress. On account of the difficulty and long time it takes to solve this problem, we propose a multi-objective optimization approach, which uses a lot-merging procedure, a new job-based encoding method, and an adjustment to the non-dominated sorting genetic algorithm II (NSGA-II). Simulation results of the adjusted NSGA-II on this SFTSP problem are compared with its traditional algorithm and much better performance of the adjusted one is observed.

scholarly journals Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes

2021 ◽  
pp. 92-108
Author(s):  
Orlenys López-Pintado ◽  
Marlon Dumas ◽  
Maksym Yerokhin ◽  
Fabrizio Maria Maggi
Keyword(s):  
Resource Utilization ◽  
Cycle Time ◽  
Search Space ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Resource Allocations ◽  
Nsga Ii ◽  
Multi Objective ◽  
The Cost

AbstractThe allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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