scholarly journals ĐA TÁC VỤ TIẾN HÓA: KỸ THUẬT TỐI ƯU HÓA MỚI

2018 ◽  
Vol 8 (3) ◽  
pp. 88
Author(s):  
Lại Thị Nhung ◽  
Nguyễn Thị Hòa ◽  
Phạm Văn Hạnh ◽  
Lê Đăng Nguyên ◽  
Lê Trọng Vĩnh
Keyword(s):  
Evolutionary Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective ◽  
Evolutionary Multitasking

Trong vài thập kỷ vừa qua, các thuật toán tiến hóa (Evolutionary Algorithms - EA) đã được áp dụng thành công để giải các bài toán tối ưu khác nhau trong khoa học và kỹ thuật. Các vấn đề này thường được phân loại vào hai nhóm: i) Tối ưu hóa đơn mục tiêu (single-objective optimization - SOO), trong đó mỗi điểm trong không gian tìm kiếm của bài toán được ánh xạ thành một giá trị mục tiêu vô hướng; và ii) Tối ưu hóa đa mục tiêu (multi-objective optimization-MOO), trong đó mỗi một điểm trong không gian tìm kiếm của bài toán được ánh xạ thành một vec-tơ (các giá trị) mục tiêu. Trong bài báo này, chúng tôi sẽ giới thiệu một loại thứ ba hoàn toàn mới đó là đa tác vụ tiến hóa (evolutionary multitasking), cho phép giải đồng thời nhiều bài toán tối ưu khác nhau trên một quần thể duy nhất và được gọi là tối ưu hóa đa nhân tố (multifactorial optimization - MFO).

Evolutionary Multi-Objective Optimization for Assignment Problems

2011 ◽  
pp. 364-387
Author(s):  
Mark P. Kleeman ◽  
Gary B. Lamont
Keyword(s):  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Assignment Problems ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Single Objective

Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single objective. This chapter focuses on assignment problems that have multiple objectives that need to be satisfied. In particular, this chapter looks at how multi-objective evolutionary algorithms have been used to solve some of these problems. Additionally, this chapter examines many of the operators that have been utilized to solve assignment problems and discusses some of the advantages and disadvantages of using specific operators.

ROTATED PROBLEMS AND ROTATIONALLY INVARIANT CROSSOVER IN EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION

2008 ◽  
Vol 07 (02) ◽  
pp. 149-186 ◽  
Author(s):  
ANTONY IORIO ◽  
XIAODONG LI
Keyword(s):  
Evolutionary Algorithms ◽  
Coordinate System ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Problem Domain ◽  
Nsga Ii ◽  
Multi Objective ◽  
Parameter Interactions ◽  
Rotationally Invariant ◽  
Single Objective

Problems that are not aligned with the coordinate system can present difficulties to many optimization algorithms, including evolutionary algorithms, by trapping the search on a ridge. The ridge problem in single-objective optimization is understood, but until now little work has been done on understanding this issue in the multi-objective domain. Multi-objective problems with parameter interactions present difficulties to an optimization algorithm, which are not present in the single-objective domain. In this work, we have explained the nature of these difficulties, and investigated the behavior of the NSGA-II, which has difficulties with problems not aligned with the principle coordinate system. This study has investigated Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-Centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX) operator within the NSGA-II, on problems exhibiting parameter interactions through a rotation of the coordinate system. An analysis of these operators on three rotated bi-objective test problems, and a four-and eight-objective problem is provided. New observations on the behavior of rotationally invariant crossover operators in the multi-objective problem domain have been reported.

Enhancement of Labyrinth Seals Efficiency by Means of a Multi-Objective Optimization Technique

2018 ◽  
Author(s):  
Mikhail Gritckevich ◽  
Kunyuan Zhou ◽  
Vincent Peltier ◽  
Markus Raben ◽  
Olga Galchenko
Keyword(s):  
Optimization Technique ◽  
Leakage Flow ◽  
Multi Objective Optimization ◽  
Labyrinth Seals ◽  
Engine Operation ◽  
Turbine Engine ◽  
Multi Objective ◽  
Single Objective ◽  
Ansys Workbench ◽  
Comprehensive Study

A comprehensive study of several labyrinth seals has been performed in the framework of both single-objective and multi-objective optimizations with the main focus on the effect of stator grooves formed due to the rubbing during gas turbine engine operation. For that purpose, the developed optimization workflow based on the DLR-AutoOpti optimizer and ANSYS-Workbench CAE environment has been employed to reduce the leakage flow and windage heating for several seals. The obtained results indicate that the seal designs obtained from optimizations without stator grooves have worse performance during the lifecycle than those with the stator grooves, justifying the importance of considering this effect for real engineering applications.

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.

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