scholarly journals Multi-Objective Optimization of Plastics Thermoforming

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1760
Author(s):  
António Gaspar-Cunha ◽  
Paulo Costa ◽  
Wagner de Campos Galuppo ◽  
João Miguel Nóbrega ◽  
Fernando Duarte ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Thickness Distribution ◽  
Final Part ◽  
Constant Thickness ◽  
Thickness Variation ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Successive Generations ◽  
Different Thickness

The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from the other works proposed in the literature, the shaping step was considered individually with the aim of optimizing the thickness distribution of the final part originated from sheets characterized by different thickness profiles, such as constant thickness, spline thickness variation in one direction and concentric thickness variation in two directions, while maintaining the temperature constant. As far we know, this is the first work where such a type of approach is proposed. A multi-objective optimization strategy based on Evolutionary Algorithms was applied to the determination of the final part thickness distribution with the aim of demonstrating the validity of the methodology proposed. The results obtained considering three different theoretical initial sheet shapes indicate clearly that the methodology proposed is valid, as it provides solutions with physical meaning and with great potential to be applied in real practice. The different thickness profiles obtained for the optimal Pareto solutions show, in all cases, that that the different profiles along the front are related to the objectives considered. Also, there is a clear improvement in the successive generations of the evolutionary algorithm.

Evolutionary Multi-Objective Optimization in Finance

2007 ◽  
pp. 74-89 ◽  
Author(s):  
C. A.C. Coello
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Short Description ◽  
Research Trends ◽  
Final Part ◽  
Future Research ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Basic Concepts

This chapter provides a brief introduction of the use of evolutionary algorithms in the solution of multi-objective optimization problems (an area now called “evolutionary multi-objective optimization”). Besides providing some basic concepts and a brief description of the approaches that are more commonly used nowadays, the chapter also provides some of the current and future research trends in the area. In the final part of the chapter, we provide a short description of the sort of applications that multi-objective evolutionary algorithms have found in finance, identifying some possible paths for future research.

A Multi-objective Optimization Strategy for Controlling the Structural Properties of Lightweight Mutiphasic PE/EVA Foams

2020 ◽  
pp. 597-601
Author(s):  
Sirwan Ghavami ◽  
Mohammad-Hasan Khademi ◽  
Farkhondeh Hemmati ◽  
Ali Fazeli ◽  
Jamshid Mohammadi-Roshandeh
Keyword(s):  
Structural Properties ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts

2021 ◽  
Author(s):  
Zhenkun Wang ◽  
Qingyan Li ◽  
Qite Yang ◽  
Hisao Ishibuchi
Keyword(s):  
Evolutionary Algorithms ◽  
Coping Strategies ◽  
Test Problem ◽  
Pareto Front ◽  
Optimization Problems ◽  
Feasible Region ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Fronts ◽  
Boundary Solutions

AbstractIt has been acknowledged that dominance-resistant solutions (DRSs) extensively exist in the feasible region of multi-objective optimization problems. Recent studies show that DRSs can cause serious performance degradation of many multi-objective evolutionary algorithms (MOEAs). Thereafter, various strategies (e.g., the $$\epsilon $$ ϵ -dominance and the modified objective calculation) to eliminate DRSs have been proposed. However, these strategies may in turn cause algorithm inefficiency in other aspects. We argue that these coping strategies prevent the algorithm from obtaining some boundary solutions of an extremely convex Pareto front (ECPF). That is, there is a dilemma between eliminating DRSs and preserving boundary solutions of the ECPF. To illustrate such a dilemma, we propose a new multi-objective optimization test problem with the ECPF as well as DRSs. Using this test problem, we investigate the performance of six representative MOEAs in terms of boundary solutions preservation and DRS elimination. The results reveal that it is quite challenging to distinguish between DRSs and boundary solutions of the ECPF.

A Multi-Objective Optimization Strategy With Priority Ranking of the Design Objectives

1990 ◽  
Author(s):  
Masahide Matsumoto ◽  
Jumpei Abe ◽  
Masataka Yoshimura
Keyword(s):  
Structural Design ◽  
Torsional Rigidity ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Response Level ◽  
Multi Objective ◽  
Lower Priority ◽  
Priority Ranking ◽  
Priority Order ◽  
Motorcycle Frame

Abstract Generally, two types of priorities are considered among multiple objectives in the design of machine structures. One of these objectives is named the “hard objective”, which is the absolutely indispensable design requirement. The other is called the “soft objective”, which has lower priority order. This paper proposes a multi-objective structural optimization strategy with priority ranking of those design objectives. Further, this strategy is demonstrated on the actual example of a motorcycle frame structural design which has three design objectives, (1) an increase in static torsional rigidity, (2) a reduction of dynamic response level, and (3) a decrease in the weight of the motorcycle frame.

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