scholarly journals New multiobjective optimization algorithm using NBI-SASP approaches for mechanical structural problems

2022 ◽  
Vol 13 ◽  
pp. 4
Author(s):  
Samira El Moumen ◽  
Siham Ouhimmou
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Structural Problems ◽  
Engineering Design Problems ◽  
Set Of Points

Various engineering design problems are formulated as constrained multi-objective optimization problems. One of the relevant and popular methods that deals with these problems is the weighted method. However, the major inconvenience with its application is that it does not yield a well distributed set. In this study, the use of the Normal Boundary Intersection approach (NBI) is proposed, which is effective in obtaining an evenly distributed set of points in the Pareto set. Given an evenly distributed set of weights, it can be strictly shown that this approach is absolutely independent of the relative scales of the functions. Moreover, in order to ensure the convergence to the Global Pareto frontier, NBI approach has to be aligned with a global optimization method. Thus, the following paper suggests NBI-Simulated Annealing Simultaneous Perturbation method (NBI-SASP) as a new method for multiobjective optimization problems. The study shall test also the applicability of the NBI-SASP approach using different engineering multi-objective optimization problems and the findings shall be compared to a method of reference (NSGA). Results clearly demonstrate that the suggested method is more efficient when it comes to search ability and it provides a well distributed global Pareto Front.

Pareto-Optimal Solutions for a Truss Problem

2011 ◽  
Vol 423 ◽  
pp. 53-64
Author(s):  
W. El Alem ◽  
A. El Hami ◽  
Rachid Ellaia
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Pareto Frontier ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Multi Objective Optimization ◽  
Recent Contribution ◽  
Multi Objective ◽  
Structural Problems ◽  
Nbi Method

Most optimization problems, particularly those in engineering design, require the simultaneous optimization of more than one objective function. In this context, the solutions of these problems are based on the Pareto frontier construction. Substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The Normal Boundary Intersection (NBI) is a recent contribution that generates a well-distributed Pareto frontier efficiently. Nevertheless, this method should be combined with a global optimization method to ensure the convergence to the global Pareto frontier. This paper proposes the NBI method using Adaptive Simulated Annealing (ASA) algorithm, namely NBI-ASA as a global nonlinear multi-objective optimization method. A well known benchmark multi-objective problem has been chosen from the literature to demonstrate the validity of the proposed method, applicability of the method for structural problems has been tested through a truss problem and promising results were obtained. The results indicate that the proposed method is a powerful search and multi-objective optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.

Hybrid Weights-Utility and Taguchi Method for Multi-Objective Optimization Problems

2015 ◽  
Vol 764-765 ◽  
pp. 305-308
Author(s):  
Kuang Hung Hsien ◽  
Shyh Chour Huang
Keyword(s):  
Taguchi Method ◽  
Engineering Design ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Utility Concept ◽  
Engineering Design Problems

In this paper, hybrid weights-utility and Taguchi method is proposed to solve multi-objective optimization problems. The new method combines the Taguchi method and the weights-utility concept. The weights of the objective function and overall utility values are very important for the weights-utility, and must be set correctly in order to obtain an optimal solution. Application of this method to engineering design problems is illustrated with the aid of one case study, and the result shows that the weights-utlity method is able to handle multi-objective optimization problems, with an optimal solution which better meets the demand of multi-objective optimization problems than the utility concept does.

An Axiomatic Design Approach to Multi-Objective Optimization

2010 ◽  
Author(s):  
Esin Tarcan ◽  
A. Kerim Kar
Keyword(s):  
Design Theory ◽  
Optimization Problems ◽  
Axiomatic Design ◽  
Thermal System ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Axiomatic Design Theory ◽  
Degree Of Coupling

In this study, a methodology is suggested by applying axiomatic design theory to multi-objective optimization of engineering design problems. In axiomatic design, Semangularity (S) and Reangularity (R) are utilized to decide which design is better. However it is not used for optimization purpose. This proposed methodology is applied on two case studies, as theoretical and thermal system multi-criteria optimization problems. It is foreseen that this methodology will reduce the degree of coupling in design optimization.

scholarly journals An Efficient Pareto Set Identification Approach for Multi-Objective Optimization on Black-Box Functions

2004 ◽  
Author(s):  
G. Gary Wang ◽  
Songqing Shan
Keyword(s):  
Optimization Problems ◽  
Black Box ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Identification Approach ◽  
Practical Tool ◽  
Set Identification

Both multiple objectives and computation-intensive black-box functions often exist simultaneously in engineering design problems. Few of existing multi-objective optimization approaches addresses problems with expensive black-box functions. In this paper, a new method called the Pareto set pursing (PSP) method is developed. By developing sampling guidance functions, this approach progressively provides a designer with a rich and evenly distributed Pareto optimal points. This work describes PSP in detail with analysis of its properties. From testing and design application, PSP demonstrates considerable efficiency, accuracy, and robustness. Theoretical proof of convergence of PSP is also given. It is believed that PSP has a great potential to be a practical tool for multi-objective optimization problems.

scholarly journals ∊-constraint heat transfer search (∊-HTS) algorithm for solving multi-objective engineering design problems

2017 ◽  
Vol 5 (1) ◽  
pp. 104-119 ◽  
Author(s):  
Mohamed A. Tawhid ◽  
Vimal Savsani
Keyword(s):  
Heat Transfer ◽  
Engineering Design ◽  
Pareto Front ◽  
Optimization Problems ◽  
Gear Train ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Numerical Experimentation

Abstract In this paper, an effective ∊-constraint heat transfer search (∊-HTS) algorithm for the multi-objective engineering design problems is presented. This algorithm is developed to solve multi-objective optimization problems by evaluating a set of single objective sub-problems. The effectiveness of the proposed algorithm is checked by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as discrete, convex, and non-convex. This algorithm is also tested for several distinctive multi-objective engineering design problems, such as four bar truss problem, gear train problem, multi-plate disc brake design, speed reducer problem, welded beam design, and spring design problem. Moreover, the numerical experimentation shows that the proposed algorithm generates the solution to represent true Pareto front. Highlights A novel multi-objective optimization (MOO) algorithm is proposed. Proposed algorithm is presented to obtain the Pareto-optimal solutions. The multi-objective optimization algorithm compared with other work in the literature. Test performance of proposed algorithm on MOO benchmark/design engineering problems.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

A Modified Non-Dominated Sorting Genetic Algorithm with Fractional Factorial Design for Multi-Objective Optimization Problems

2010 ◽  
Vol 26 (2) ◽  
pp. 143-156
Author(s):  
J.-L. Liu ◽  
T.-F. Lee
Keyword(s):  
Genetic Algorithm ◽  
Factorial Design ◽  
Fractional Factorial Design ◽  
Optimization Problems ◽  
Fractional Factorial ◽  
Good Genes ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mutation Operators ◽  
Engineering Design Problems

AbstractThis study develops an intelligent non-dominated sorting genetic algorithm (GA), called INSGA herein, which includes a non-dominated sorting, crowded distance sorting, binary tournament selection, intelligent crossover and non-uniform mutation operators, for solving multi-objective optimization problems (MOOPs). This work adopts Goldberg's notion of non-dominated sorting and Deb's crowded distance sorting in the proposed MOGA to achieve solutions with good diversity-preservation and uniform spread on the approximated Pareto front. In addition, the chromosomes of offspring are generated based on an intelligent crossover operator using a fractional factorial design to select good genes from parents intelligently and achieve the goals of fast convergence and high numerical accuracy. To further improve the fine turning capabilities of the presented MOGA, a non-uniform mutation operator is also applied. A typical mutation approach is to create a random number and then add it to corresponding original value. Performance evaluation of the INSGA is examined by applying it to a variety of unconstrained and constrained multi-objective optimization functions. Moreover, two engineering design problems, which include a two-bar truss design and a welded beam design, are studied by the proposed INSGA. Results include the estimated Pareto-optimal front of non-dominated solutions.

Multi-Objective Optimization of Compliant Mechanisms Including Failure Theories

2007 ◽  
Author(s):  
Stephen L. Canfield ◽  
Daniel L. Chlarson ◽  
Alexander Shibakov ◽  
Joseph D. Richardson ◽  
Anupam Saxena
Keyword(s):  
Multiobjective Optimization ◽  
Compliant Mechanisms ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Design Objective ◽  
Multi Objective ◽  
Failure Theory ◽  
Measuring Efficiency ◽  
Failure Theories

This paper will present a version of failure theory suitable for designing optimal compliant mechanisms. The resulting theory will be incorporated as design objective functions within a multi-objective optimizing engine with the purpose of producing optimal and robust compliant mechanisms suitable for manufacture. Combining these failure-based objective functions with the classical ones measuring efficiency in performing a task, in the context of diversity promoting multiobjective optimization (see [1]) will demonstrate the tool’s ability to produce optimal compliant mechanisms that are failure-proof as well as provide insights into the complexity of particular design problems.

Multi-Objective Optimization Methods of Plate Solid Oxide Fuel Cell's Temperature System

2013 ◽  
Vol 694-697 ◽  
pp. 728-733
Author(s):  
Xin Liu ◽  
Xiao Hong Hao ◽  
Xin Hua Yang ◽  
Ai Min An ◽  
Hao Chen Zhang
Keyword(s):  
Optimization Problems ◽  
External Disturbance ◽  
Operating Mode ◽  
Optimization Methods ◽  
Optimization Method ◽  
Solid Oxide ◽  
Working Environment ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Multi Objective

The working environment of Solid Oxide Fuel Cells (SOFC) includes high temperature and speedy chemical reaction. The improved control structure and optimization method for the simplified temperature system of SOFC are proposed in this paper. It designs a real-time cascade PID controller for dynamic reactive temperatures of SOFC which vary significantly as the external disturbance or operating mode changes. Considering the efficiency of fuel utility and output power are incommensurable multiple goals, some fuzzy-based rules are introduced to solve these complex multi-objective optimization problems. The experiments’ result shows that the controllers have good robustness and quickness when the system is under the mode with external disturbances.

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