Applying Multi-Objective Optimization Algorithms to Mechanical Engineering

2018 ◽  
pp. 287-301 ◽  
Author(s):  
Preeti Shivach ◽  
Lata Nautiyal ◽  
Mangey Ram
Keyword(s):  
Design Process ◽  
Environmental Variables ◽  
Mechanical Engineering ◽  
Optimization Process ◽  
State Variables ◽  
Multi Objective Optimization ◽  
Simulation And Optimization ◽  
System Parameters ◽  
Multi Objective ◽  
Day By Day

In today's scenarios, the utilization of simulation and optimization in the field of designing is achieving wider recognition in the various zones of commerce as the computational competences of computers upsurge day by day. The result is that the uses for numerical optimization have increased tremendously. Design process in engineering is a distinct practice of solving the problems where a group of recurrently indistinct objectives has to be well-adjusted deprived of violating any given circumstances. Consequently, it seems quite ordinary to consider a design process as an optimization process. The design process could be articulated as to allocate values to the system parameters to confirm that the state variables and the characteristics are as suitable as possible through an inclusive range of operating and environmental variables. This is a complex multi-objective optimization problem (MOOP). This chapter discusses the use of MOO algorithms in mechanical engineering.

Multi-objective optimization of the suspension system parameters of a full vehicle model

2018 ◽  
Vol 20 (1) ◽  
pp. 151-177 ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas
Keyword(s):  
Suspension System ◽  
Multi Objective Optimization ◽  
Vehicle Model ◽  
System Parameters ◽  
Multi Objective ◽  
Full Vehicle

Finite Element Modelling and Multi-Objective Optimization of Composite Submarine Pressure Hull Subjected to Hydrostatic Pressure

2019 ◽  
Vol 953 ◽  
pp. 53-58 ◽  
Author(s):  
Elsayed Fathallah
Keyword(s):  
Hydrostatic Pressure ◽  
Parametric Design ◽  
Epoxy Composite ◽  
Load Capacity ◽  
Optimization Process ◽  
Valuable Insight ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimized Model ◽  
Better Than

Excellent mechanical behavior and low density of composite materials make them candidates to replace metals for many underwater applications. This paper presents a comprehensive study about the multi-objective optimization of composite pressure hull subjected to hydrostatic pressure to minimize the weight of the pressure hull and maximize the buckling load capacity according to the design requirements. Two models were constructed, one model constructed from Carbon/Epoxy composite (USN-150), the other model is metallic pressure hull constructed from HY100. The analysis and the optimization process were completely performed using ANSYS Parametric Design Language (APDL). Tsai-Wu failure criterion was incorporated in the optimization process. The results obtained emphasize that, the submarine constructed from Carbon/Epoxy composite (USN-150) is better than the submarine constructed from HY100. Finally, an optimized model with an optimum pattern of fiber orientations was presented. Hopefully, the results may provide a valuable insight for the future of designing composite underwater vehicles.

scholarly journals A Decomposition-Based Interactive Method for Multi-Objective Evolutionary Algorithm

2012 ◽  
Author(s):  
Nguye Long ◽  
Bui Thu Lam
Keyword(s):  
Optimization Problems ◽  
Decision Makers ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Interactive Method ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Single Solution ◽  
Optimal Set

Multi-objectivity has existed in many real-world optimization problems. In most multi-objective cases, objectives are often conflicting, there is no single solution being optimal with regards to all objectives. These problems are called Multi-objective Optimization Problems (MOPs). To date, there have been al large number of methods for solving MOPs including evolutionary methods (namly Multi-objective Evolutionary Algorithms MOEAs). With the use of a population of solutions for searching. MOEAs are naturally suitable for approximating optimal solutions (called the Pareto Optimal Set (POS) or the efficient set). There has been a popular trend in MOEAs considering the role of Decision Makers (DMs) during the optimization process (known as the human-in-loop) for checking, analyzing the results and giving the preference to guide the optimization process. This is call the interactive method.

A Lion’s Pride Inspired Algorithm for VLSI Floorplanning

2019 ◽  
Vol 29 (01) ◽  
pp. 2050003
Author(s):  
Lalin L. Laudis ◽  
N. Ramadass
Keyword(s):  
Design Process ◽  
Integrated Circuit ◽  
Large Scale ◽  
Optimization Problem ◽  
Standard Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Scale Integration ◽  
Vlsi Floorplanning

The complexity of any integrated circuit pushes the researchers to optimize the various parameters in the design process. Usually, the Nondeterministic Polynomial problems in the design process of Very Large Scale Integration (VLSI) are considered as a Single Objective Optimization Problem (SOOP). However, due to the increasing demand for the multi-criterion optimization, researchers delve up on Multi-Objective Optimization methodologies to solve a problem with multiple objectives. Moreover, it is evident from the literature that biologically inspired algorithm works very well in optimizing a Multi-Objective Optimization Problem (MOOP). This paper proposes a new Lion’s pride inspired algorithm to solve any MOOP. The methodologies mimic the traits of a Lion which always strives to become the Pride Lion. The Algorithm was tested with VLSI floorplanning problem wherein the area and dead space are the objectives. The algorithm was also tested with several standard test problems. The tabulated results justify the ruggedness of the proposed algorithm in solving any MOOP.

Mixture Surrogate Models for Multi- Objective Optimization

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Shailesh S. Kadre ◽  
Vipin K. Tripathi
Keyword(s):  
Finite Element ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Multiple Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Costly Function ◽  
Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

A Review of Multi-objective Optimization: Methods and Algorithms in Mechanical Engineering Problems

2021 ◽  
Author(s):  
João Luiz Junho Pereira ◽  
Guilherme Antônio Oliver ◽  
Matheus Brendon Francisco ◽  
Sebastião Simões Cunha ◽  
Guilherme Ferreira Gomes
Keyword(s):  
Mechanical Engineering ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Engineering Problems
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