F06-2 An Evolutionary Computation Approach for Multi Objective Optimization Problems

2001 ◽  
Vol 2001.14 (0) ◽  
pp. 697-698
Author(s):  
Tomoyuki HIROYASU ◽  
Mitsunori MIKI ◽  
Shinya WATANABE
Keyword(s):  
Evolutionary Computation ◽  
Optimization Problems ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

2021 ◽  
Author(s):  
Wen-Jing Hong ◽  
Peng Yang ◽  
Ke Tang
Keyword(s):  
Evolutionary Computation ◽  
Real World ◽  
Large Scale ◽  
Optimization Problems ◽  
Divide And Conquer ◽  
Research Progress ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Decision Variables

AbstractLarge-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables. More recently, it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables. From the perspective of methodology, the large-scale MOEAs are categorized into three classes and introduced respectively: divide and conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed.

scholarly journals REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS

2016 ◽  
Vol 6 (2) ◽  
pp. 54
Author(s):  
Joaquín Javier Meza Álvarez ◽  
Juan Manuel Cueva Lovelle ◽  
Helbert Eduardo Espitia
Keyword(s):  
Social Behavior ◽  
Evolutionary Computation ◽  
Optimization Algorithm ◽  
Critical Review ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Good Alternative ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Good View

El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization.

Pareto-Optimal Equilibrium Points in non-Cooperative Multi-Objective Optimization Problems

2021 ◽  
pp. 114995
Author(s):  
Mohammadali Saniee Monfared ◽  
Sayyed Ehsan Monabbati ◽  
Atefeh Rajabi Kafshgar
Keyword(s):  
Optimization Problems ◽  
Equilibrium Points ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective

Injecting domain knowledge in multi-objective optimization problems: A semantic approach

2021 ◽  
pp. 103546
Author(s):  
Cristóbal Barba-González ◽  
Antonio J. Nebro ◽  
José García-Nieto ◽  
María del Mar Roldán-García ◽  
Ismael Navas-Delgado ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Optimization Problems ◽  
Multi Objective Optimization ◽  
Semantic Approach ◽  
Multi Objective

Evolving Dynamic Multi-Objective Optimization Problems with Objective Replacement

2005 ◽  
Vol 23 (3) ◽  
pp. 267-293 ◽  
Author(s):  
SHENG-UEI GUAN ◽  
QIAN CHEN ◽  
WENTING MO
Keyword(s):  
Optimization Problems ◽  
Multi Objective Optimization ◽  
Multi Objective
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