How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing

2007 ◽  
pp. 465-482
Author(s):  
N. Chakraborti
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimality ◽  
Multi Objective Optimization ◽  
Recent Past ◽  
Multi Objective ◽  
Manufacturing Sectors ◽  
Design And Manufacturing ◽  
Basic Concepts

An informal analysis is provided for the basic concepts associated with multi-objective optimization and the notion of Pareto-optimality, particularly in the context of genetic algorithms. A number of evolutionary algorithms developed for this purpose are also briefly introduced, and finally, a number of paradigm examples are presented from the materials and manufacturing sectors, where multi-objective genetic algorithms have been successfully utilized in the recent past.

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.

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.

Approach for Negotiation Problems in Multi-Agent Systems for DC Micro Grid Using Multi-Objective Evolutionary Algorithms

2015 ◽  
Vol 713-715 ◽  
pp. 2106-2109
Author(s):  
Mauricio Mauledoux ◽  
Edilberto Mejía-Ruda ◽  
Oscar I. Caldas
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimal ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Objective ◽  
Multi Agent ◽  
Micro Grid ◽  
Optimal Space ◽  
Multi Agents

The work is devoted to solve allocation task problem in multi agents systems using multi-objective genetic algorithms and comparing the technique with methods used in game theories. The paper shows the main advantages of genetic algorithms and the way to apply a parallel approach dividing the population in sub-populations saving time in the search and expanding the coverage of the solution in the Pareto optimal space.

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